Training in progress, step 305, checkpoint

checkpoint-305/1_AdvancedWeightedPooling/config.json

@@ -0,0 +1,12 @@
+{
+  "embed_dim": 768,
+  "num_heads": 4,
+  "dropout": 0.025,
+  "bias": true,
+  "gate_min": 0.05,
+  "gate_max": 0.95,
+  "gate_dropout": 0.05,
+  "dropout_gate_open": 0.0,
+  "dropout_gate_close": 0.0,
+  "CLS_self_attn": 0
+}

checkpoint-305/1_AdvancedWeightedPooling/pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7e2a4fc93c8370e5138de3d32718c6a165a0c5dd0d94618938b75dda07f023b9
+size 14201307

checkpoint-305/README.md

@@ -0,0 +1,1132 @@
+---
+base_model: microsoft/deberta-v3-small
+library_name: sentence-transformers
+metrics:
+- pearson_cosine
+- spearman_cosine
+- pearson_manhattan
+- spearman_manhattan
+- pearson_euclidean
+- spearman_euclidean
+- pearson_dot
+- spearman_dot
+- pearson_max
+- spearman_max
+- cosine_accuracy
+- cosine_accuracy_threshold
+- cosine_f1
+- cosine_f1_threshold
+- cosine_precision
+- cosine_recall
+- cosine_ap
+- dot_accuracy
+- dot_accuracy_threshold
+- dot_f1
+- dot_f1_threshold
+- dot_precision
+- dot_recall
+- dot_ap
+- manhattan_accuracy
+- manhattan_accuracy_threshold
+- manhattan_f1
+- manhattan_f1_threshold
+- manhattan_precision
+- manhattan_recall
+- manhattan_ap
+- euclidean_accuracy
+- euclidean_accuracy_threshold
+- euclidean_f1
+- euclidean_f1_threshold
+- euclidean_precision
+- euclidean_recall
+- euclidean_ap
+- max_accuracy
+- max_accuracy_threshold
+- max_f1
+- max_f1_threshold
+- max_precision
+- max_recall
+- max_ap
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:32500
+- loss:GISTEmbedLoss
+widget:
+- source_sentence: phase changes do not change
+  sentences:
+  - The major Atlantic slave trading nations, ordered by trade volume, were the Portuguese,
+    the British, the Spanish, the French, the Dutch, and the Danish. Several had established
+    outposts on the African coast where they purchased slaves from local African leaders.
+  - "phase changes do not change mass. Particles have mass, but mass is energy. \n\
+    \ phase changes do not change  energy"
+  - According to the U.S. Census Bureau , the county is a total area of , which has
+    land and ( 0.2 % ) is water .
+- source_sentence: what jobs can you get with a bachelor degree in anthropology?
+  sentences:
+  - To determine the atomic weight of an element, you should add up protons and neutrons.
+  - '[''Paleontologist*'', ''Archaeologist*'', ''University Professor*'', ''Market
+    Research Analyst*'', ''Primatologist.'', ''Forensic Scientist*'', ''Medical Anthropologist.'',
+    ''Museum Technician.'']'
+  - The wingspan flies , the moth comes depending on the location from July to August
+    .
+- source_sentence: Identify different forms of energy (e.g., light, sound, heat).
+  sentences:
+  - '`` Irreplaceable '''' '''' remained on the chart for thirty weeks , and was certified
+    double-platinum by the Recording Industry Association of America ( RIAA ) , denoting
+    sales of two million downloads , and had sold over 3,139,000 paid digital downloads
+    in the US as of October 2012 , according to Nielsen SoundScan . '''''
+  - On Rotten Tomatoes , the film has a rating of 63 % , based on 87 reviews , with
+    an average rating of 5.9/10 .
+  - Heat, light, and sound are all different forms of energy.
+- source_sentence: what is so small it can only be seen with an electron microscope?
+  sentences:
+  - "Viruses are so small that they can be seen only with an electron microscope..\
+    \ Where most viruses are DNA, HIV is an RNA virus. \n HIV is so small it can only\
+    \ be seen with an electron microscope"
+  - The development of modern lasers has opened many doors to both research and applications.
+    A laser beam was used to measure the distance from the Earth to the moon. Lasers
+    are important components of CD players. As the image above illustrates, lasers
+    can provide precise focusing of beams to selectively destroy cancer cells in patients.
+    The ability of a laser to focus precisely is due to high-quality crystals that
+    help give rise to the laser beam. A variety of techniques are used to manufacture
+    pure crystals for use in lasers.
+  - Discussion for (a) This value is the net work done on the package. The person
+    actually does more work than this, because friction opposes the motion. Friction
+    does negative work and removes some of the energy the person expends and converts
+    it to thermal energy. The net work equals the sum of the work done by each individual
+    force. Strategy and Concept for (b) The forces acting on the package are gravity,
+    the normal force, the force of friction, and the applied force. The normal force
+    and force of gravity are each perpendicular to the displacement, and therefore
+    do no work. Solution for (b) The applied force does work.
+- source_sentence: what aspects of your environment may relate to the epidemic of
+    obesity
+  sentences:
+  - Jan Kromkamp ( born August 17 , 1980 in Makkinga , Netherlands ) is a Dutch footballer
+    .
+  - When chemicals in solution react, the proper way of writing the chemical formulas
+    of the dissolved ionic compounds is in terms of the dissociated ions, not the
+    complete ionic formula. A complete ionic equation is a chemical equation in which
+    the dissolved ionic compounds are written as separated ions. Solubility rules
+    are very useful in determining which ionic compounds are dissolved and which are
+    not. For example, when NaCl(aq) reacts with AgNO3(aq) in a double-replacement
+    reaction to precipitate AgCl(s) and form NaNO3(aq), the complete ionic equation
+    includes NaCl, AgNO3, and NaNO3 written as separated ions:.
+  - Genetic changes in human populations occur too slowly to be responsible for the
+    obesity epidemic. Nevertheless, the variation in how people respond to the environment
+    that promotes physical inactivity and intake of high-calorie foods suggests that
+    genes do play a role in the development of obesity.
+model-index:
+- name: SentenceTransformer based on microsoft/deberta-v3-small
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: sts test
+      type: sts-test
+    metrics:
+    - type: pearson_cosine
+      value: 0.3774946012125992
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.4056589966976888
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.3861982631744407
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.4059364545183154
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.38652243004790016
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.4056589966976888
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.3774648453085433
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.40563469676275316
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.38652243004790016
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.4059364545183154
+      name: Spearman Max
+  - task:
+      type: binary-classification
+      name: Binary Classification
+    dataset:
+      name: allNLI dev
+      type: allNLI-dev
+    metrics:
+    - type: cosine_accuracy
+      value: 0.67578125
+      name: Cosine Accuracy
+    - type: cosine_accuracy_threshold
+      value: 0.9427558183670044
+      name: Cosine Accuracy Threshold
+    - type: cosine_f1
+      value: 0.5225225225225225
+      name: Cosine F1
+    - type: cosine_f1_threshold
+      value: 0.8046966791152954
+      name: Cosine F1 Threshold
+    - type: cosine_precision
+      value: 0.3795811518324607
+      name: Cosine Precision
+    - type: cosine_recall
+      value: 0.838150289017341
+      name: Cosine Recall
+    - type: cosine_ap
+      value: 0.4368751759846574
+      name: Cosine Ap
+    - type: dot_accuracy
+      value: 0.67578125
+      name: Dot Accuracy
+    - type: dot_accuracy_threshold
+      value: 724.1080322265625
+      name: Dot Accuracy Threshold
+    - type: dot_f1
+      value: 0.5225225225225225
+      name: Dot F1
+    - type: dot_f1_threshold
+      value: 618.074951171875
+      name: Dot F1 Threshold
+    - type: dot_precision
+      value: 0.3795811518324607
+      name: Dot Precision
+    - type: dot_recall
+      value: 0.838150289017341
+      name: Dot Recall
+    - type: dot_ap
+      value: 0.436842886797982
+      name: Dot Ap
+    - type: manhattan_accuracy
+      value: 0.677734375
+      name: Manhattan Accuracy
+    - type: manhattan_accuracy_threshold
+      value: 223.6764373779297
+      name: Manhattan Accuracy Threshold
+    - type: manhattan_f1
+      value: 0.5239852398523985
+      name: Manhattan F1
+    - type: manhattan_f1_threshold
+      value: 372.31396484375
+      name: Manhattan F1 Threshold
+    - type: manhattan_precision
+      value: 0.38482384823848237
+      name: Manhattan Precision
+    - type: manhattan_recall
+      value: 0.8208092485549133
+      name: Manhattan Recall
+    - type: manhattan_ap
+      value: 0.43892484929307635
+      name: Manhattan Ap
+    - type: euclidean_accuracy
+      value: 0.67578125
+      name: Euclidean Accuracy
+    - type: euclidean_accuracy_threshold
+      value: 9.377331733703613
+      name: Euclidean Accuracy Threshold
+    - type: euclidean_f1
+      value: 0.5225225225225225
+      name: Euclidean F1
+    - type: euclidean_f1_threshold
+      value: 17.321048736572266
+      name: Euclidean F1 Threshold
+    - type: euclidean_precision
+      value: 0.3795811518324607
+      name: Euclidean Precision
+    - type: euclidean_recall
+      value: 0.838150289017341
+      name: Euclidean Recall
+    - type: euclidean_ap
+      value: 0.4368602200677977
+      name: Euclidean Ap
+    - type: max_accuracy
+      value: 0.677734375
+      name: Max Accuracy
+    - type: max_accuracy_threshold
+      value: 724.1080322265625
+      name: Max Accuracy Threshold
+    - type: max_f1
+      value: 0.5239852398523985
+      name: Max F1
+    - type: max_f1_threshold
+      value: 618.074951171875
+      name: Max F1 Threshold
+    - type: max_precision
+      value: 0.38482384823848237
+      name: Max Precision
+    - type: max_recall
+      value: 0.838150289017341
+      name: Max Recall
+    - type: max_ap
+      value: 0.43892484929307635
+      name: Max Ap
+  - task:
+      type: binary-classification
+      name: Binary Classification
+    dataset:
+      name: Qnli dev
+      type: Qnli-dev
+    metrics:
+    - type: cosine_accuracy
+      value: 0.646484375
+      name: Cosine Accuracy
+    - type: cosine_accuracy_threshold
+      value: 0.8057259321212769
+      name: Cosine Accuracy Threshold
+    - type: cosine_f1
+      value: 0.6688102893890675
+      name: Cosine F1
+    - type: cosine_f1_threshold
+      value: 0.7187118530273438
+      name: Cosine F1 Threshold
+    - type: cosine_precision
+      value: 0.538860103626943
+      name: Cosine Precision
+    - type: cosine_recall
+      value: 0.8813559322033898
+      name: Cosine Recall
+    - type: cosine_ap
+      value: 0.6720663622193426
+      name: Cosine Ap
+    - type: dot_accuracy
+      value: 0.646484375
+      name: Dot Accuracy
+    - type: dot_accuracy_threshold
+      value: 618.8643798828125
+      name: Dot Accuracy Threshold
+    - type: dot_f1
+      value: 0.6688102893890675
+      name: Dot F1
+    - type: dot_f1_threshold
+      value: 552.0260009765625
+      name: Dot F1 Threshold
+    - type: dot_precision
+      value: 0.538860103626943
+      name: Dot Precision
+    - type: dot_recall
+      value: 0.8813559322033898
+      name: Dot Recall
+    - type: dot_ap
+      value: 0.672083506527328
+      name: Dot Ap
+    - type: manhattan_accuracy
+      value: 0.6484375
+      name: Manhattan Accuracy
+    - type: manhattan_accuracy_threshold
+      value: 386.58905029296875
+      name: Manhattan Accuracy Threshold
+    - type: manhattan_f1
+      value: 0.6645569620253164
+      name: Manhattan F1
+    - type: manhattan_f1_threshold
+      value: 462.609130859375
+      name: Manhattan F1 Threshold
+    - type: manhattan_precision
+      value: 0.5303030303030303
+      name: Manhattan Precision
+    - type: manhattan_recall
+      value: 0.8898305084745762
+      name: Manhattan Recall
+    - type: manhattan_ap
+      value: 0.6724653688821339
+      name: Manhattan Ap
+    - type: euclidean_accuracy
+      value: 0.646484375
+      name: Euclidean Accuracy
+    - type: euclidean_accuracy_threshold
+      value: 17.27533721923828
+      name: Euclidean Accuracy Threshold
+    - type: euclidean_f1
+      value: 0.6688102893890675
+      name: Euclidean F1
+    - type: euclidean_f1_threshold
+      value: 20.787063598632812
+      name: Euclidean F1 Threshold
+    - type: euclidean_precision
+      value: 0.538860103626943
+      name: Euclidean Precision
+    - type: euclidean_recall
+      value: 0.8813559322033898
+      name: Euclidean Recall
+    - type: euclidean_ap
+      value: 0.6720591998758361
+      name: Euclidean Ap
+    - type: max_accuracy
+      value: 0.6484375
+      name: Max Accuracy
+    - type: max_accuracy_threshold
+      value: 618.8643798828125
+      name: Max Accuracy Threshold
+    - type: max_f1
+      value: 0.6688102893890675
+      name: Max F1
+    - type: max_f1_threshold
+      value: 552.0260009765625
+      name: Max F1 Threshold
+    - type: max_precision
+      value: 0.538860103626943
+      name: Max Precision
+    - type: max_recall
+      value: 0.8898305084745762
+      name: Max Recall
+    - type: max_ap
+      value: 0.6724653688821339
+      name: Max Ap
+---
+
+# SentenceTransformer based on microsoft/deberta-v3-small
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) <!-- at revision a36c739020e01763fe789b4b85e2df55d6180012 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 tokens
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DebertaV2Model 
+  (1): AdvancedWeightedPooling(
+    (alpha_dropout_layer): Dropout(p=0.05, inplace=False)
+    (gate_dropout_layer): Dropout(p=0.0, inplace=False)
+    (linear_cls_Qpj): Linear(in_features=768, out_features=768, bias=True)
+    (linear_attnOut): Linear(in_features=768, out_features=768, bias=True)
+    (mha): MultiheadAttention(
+      (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
+    )
+    (layernorm_output): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+    (layernorm_weightedPooing): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+    (layernorm_attnOut): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+  )
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("bobox/DeBERTa3-s-CustomPoolin-toytest4-step1-checkpoints-tmp")
+# Run inference
+sentences = [
+    'what aspects of your environment may relate to the epidemic of obesity',
+    'Genetic changes in human populations occur too slowly to be responsible for the obesity epidemic. Nevertheless, the variation in how people respond to the environment that promotes physical inactivity and intake of high-calorie foods suggests that genes do play a role in the development of obesity.',
+    'When chemicals in solution react, the proper way of writing the chemical formulas of the dissolved ionic compounds is in terms of the dissociated ions, not the complete ionic formula. A complete ionic equation is a chemical equation in which the dissolved ionic compounds are written as separated ions. Solubility rules are very useful in determining which ionic compounds are dissolved and which are not. For example, when NaCl(aq) reacts with AgNO3(aq) in a double-replacement reaction to precipitate AgCl(s) and form NaNO3(aq), the complete ionic equation includes NaCl, AgNO3, and NaNO3 written as separated ions:.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Semantic Similarity
+* Dataset: `sts-test`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.3775     |
+| **spearman_cosine** | **0.4057** |
+| pearson_manhattan   | 0.3862     |
+| spearman_manhattan  | 0.4059     |
+| pearson_euclidean   | 0.3865     |
+| spearman_euclidean  | 0.4057     |
+| pearson_dot         | 0.3775     |
+| spearman_dot        | 0.4056     |
+| pearson_max         | 0.3865     |
+| spearman_max        | 0.4059     |
+
+#### Binary Classification
+* Dataset: `allNLI-dev`
+* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
+
+| Metric                       | Value      |
+|:-----------------------------|:-----------|
+| cosine_accuracy              | 0.6758     |
+| cosine_accuracy_threshold    | 0.9428     |
+| cosine_f1                    | 0.5225     |
+| cosine_f1_threshold          | 0.8047     |
+| cosine_precision             | 0.3796     |
+| cosine_recall                | 0.8382     |
+| cosine_ap                    | 0.4369     |
+| dot_accuracy                 | 0.6758     |
+| dot_accuracy_threshold       | 724.108    |
+| dot_f1                       | 0.5225     |
+| dot_f1_threshold             | 618.075    |
+| dot_precision                | 0.3796     |
+| dot_recall                   | 0.8382     |
+| dot_ap                       | 0.4368     |
+| manhattan_accuracy           | 0.6777     |
+| manhattan_accuracy_threshold | 223.6764   |
+| manhattan_f1                 | 0.524      |
+| manhattan_f1_threshold       | 372.314    |
+| manhattan_precision          | 0.3848     |
+| manhattan_recall             | 0.8208     |
+| manhattan_ap                 | 0.4389     |
+| euclidean_accuracy           | 0.6758     |
+| euclidean_accuracy_threshold | 9.3773     |
+| euclidean_f1                 | 0.5225     |
+| euclidean_f1_threshold       | 17.321     |
+| euclidean_precision          | 0.3796     |
+| euclidean_recall             | 0.8382     |
+| euclidean_ap                 | 0.4369     |
+| max_accuracy                 | 0.6777     |
+| max_accuracy_threshold       | 724.108    |
+| max_f1                       | 0.524      |
+| max_f1_threshold             | 618.075    |
+| max_precision                | 0.3848     |
+| max_recall                   | 0.8382     |
+| **max_ap**                   | **0.4389** |
+
+#### Binary Classification
+* Dataset: `Qnli-dev`
+* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
+
+| Metric                       | Value      |
+|:-----------------------------|:-----------|
+| cosine_accuracy              | 0.6465     |
+| cosine_accuracy_threshold    | 0.8057     |
+| cosine_f1                    | 0.6688     |
+| cosine_f1_threshold          | 0.7187     |
+| cosine_precision             | 0.5389     |
+| cosine_recall                | 0.8814     |
+| cosine_ap                    | 0.6721     |
+| dot_accuracy                 | 0.6465     |
+| dot_accuracy_threshold       | 618.8644   |
+| dot_f1                       | 0.6688     |
+| dot_f1_threshold             | 552.026    |
+| dot_precision                | 0.5389     |
+| dot_recall                   | 0.8814     |
+| dot_ap                       | 0.6721     |
+| manhattan_accuracy           | 0.6484     |
+| manhattan_accuracy_threshold | 386.5891   |
+| manhattan_f1                 | 0.6646     |
+| manhattan_f1_threshold       | 462.6091   |
+| manhattan_precision          | 0.5303     |
+| manhattan_recall             | 0.8898     |
+| manhattan_ap                 | 0.6725     |
+| euclidean_accuracy           | 0.6465     |
+| euclidean_accuracy_threshold | 17.2753    |
+| euclidean_f1                 | 0.6688     |
+| euclidean_f1_threshold       | 20.7871    |
+| euclidean_precision          | 0.5389     |
+| euclidean_recall             | 0.8814     |
+| euclidean_ap                 | 0.6721     |
+| max_accuracy                 | 0.6484     |
+| max_accuracy_threshold       | 618.8644   |
+| max_f1                       | 0.6688     |
+| max_f1_threshold             | 552.026    |
+| max_precision                | 0.5389     |
+| max_recall                   | 0.8898     |
+| **max_ap**                   | **0.6725** |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 32,500 training samples
+* Columns: <code>sentence1</code> and <code>sentence2</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                          | sentence2                                                                          |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                             |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 29.39 tokens</li><li>max: 323 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 54.42 tokens</li><li>max: 423 tokens</li></ul> |
+* Samples:
+  | sentence1                                                                                                                                                                                                                                                                                             | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
+  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>In which London road is Harrod’s department store?</code>                                                                                                                                                                                                                                       | <code>Harrods, Brompton Road, London | Shopping/Department Stores in London | LondonTown.com Opening Times Britain's most famous store and possibly the most famous store in the world, Harrods features on many tourist 'must-see' lists - and with good reason. Its humble beginnings date back to 1849, when Charles Henry Harrod opened a small East End grocer and tea merchant business that emphasised impeccable service over value. Today, it occupies a vast seven floor site in London's fashionable Knightsbridge and boasts a phenomenal range of products from pianos and cooking pans to fashion and perfumery. The luxurious Urban Retreat can be found on the sixth floor while newer departments include Superbrands, with 17 boutiques from top international designers, and Salon du Parfums, housing some of the most exceptional and exclusive perfumes in the world. The Food Hall is ostentatious to the core and mouth-wateringly exotic, and the store as a whole is well served with 27 restaurants. At Christmas time the Brompton Road windows are transformed into a magical winter wonderland and Father Christmas takes up residence at the enchanting Christmas Grotto. The summer and winter sales are calendar events in the shopping year, and although both sales are extremely crowded there are some great bargains on offer. �</code> |
+  | <code>e.&#9;in solids the atoms are closely locked in position and can only vibrate, in liquids the atoms and molecules are more loosely connected and can collide with and move past one another, while in gases the atoms or molecules are free to move independently, colliding frequently.</code> | <code>Within a substance, atoms that collide frequently and move independently of one another are most likely in a gas</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
+  | <code>Joe Cole was unable to join West Bromwich Albion .</code>                                                                                                                                                                                                                                       | <code>On 16th October Joe Cole took a long hard look at himself realising that he would never get the opportunity to join West Bromwich Albion and joined Coventry City instead.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |
+* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+  ```json
+  {'guide': SentenceTransformer(
+    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
+    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+    (2): Normalize()
+  ), 'temperature': 0.025}
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 256
+- `lr_scheduler_type`: cosine_with_min_lr
+- `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 3.3333333333333337e-06}
+- `warmup_ratio`: 0.33
+- `save_safetensors`: False
+- `fp16`: True
+- `push_to_hub`: True
+- `hub_model_id`: bobox/DeBERTa3-s-CustomPoolin-toytest4-step1-checkpoints-tmp
+- `hub_strategy`: all_checkpoints
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 256
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 3
+- `max_steps`: -1
+- `lr_scheduler_type`: cosine_with_min_lr
+- `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 3.3333333333333337e-06}
+- `warmup_ratio`: 0.33
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: False
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: True
+- `resume_from_checkpoint`: None
+- `hub_model_id`: bobox/DeBERTa3-s-CustomPoolin-toytest4-step1-checkpoints-tmp
+- `hub_strategy`: all_checkpoints
+- `hub_private_repo`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `eval_use_gather_object`: False
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step | Training Loss | sts-test_spearman_cosine | allNLI-dev_max_ap | Qnli-dev_max_ap |
+|:------:|:----:|:-------------:|:------------------------:|:-----------------:|:---------------:|
+| 0.0010 | 1    | 6.0688        | -                        | -                 | -               |
+| 0.0020 | 2    | 7.5576        | -                        | -                 | -               |
+| 0.0030 | 3    | 4.6849        | -                        | -                 | -               |
+| 0.0039 | 4    | 5.4503        | -                        | -                 | -               |
+| 0.0049 | 5    | 5.6057        | -                        | -                 | -               |
+| 0.0059 | 6    | 6.3049        | -                        | -                 | -               |
+| 0.0069 | 7    | 6.8336        | -                        | -                 | -               |
+| 0.0079 | 8    | 5.0777        | -                        | -                 | -               |
+| 0.0089 | 9    | 4.8358        | -                        | -                 | -               |
+| 0.0098 | 10   | 4.641         | -                        | -                 | -               |
+| 0.0108 | 11   | 4.828         | -                        | -                 | -               |
+| 0.0118 | 12   | 5.2269        | -                        | -                 | -               |
+| 0.0128 | 13   | 5.6772        | -                        | -                 | -               |
+| 0.0138 | 14   | 5.1422        | -                        | -                 | -               |
+| 0.0148 | 15   | 6.2469        | -                        | -                 | -               |
+| 0.0157 | 16   | 4.6802        | -                        | -                 | -               |
+| 0.0167 | 17   | 4.5492        | -                        | -                 | -               |
+| 0.0177 | 18   | 4.8062        | -                        | -                 | -               |
+| 0.0187 | 19   | 7.5141        | -                        | -                 | -               |
+| 0.0197 | 20   | 5.5202        | -                        | -                 | -               |
+| 0.0207 | 21   | 6.5025        | -                        | -                 | -               |
+| 0.0217 | 22   | 7.318         | -                        | -                 | -               |
+| 0.0226 | 23   | 4.6458        | -                        | -                 | -               |
+| 0.0236 | 24   | 4.6191        | -                        | -                 | -               |
+| 0.0246 | 25   | 4.3159        | -                        | -                 | -               |
+| 0.0256 | 26   | 6.3677        | -                        | -                 | -               |
+| 0.0266 | 27   | 5.6052        | -                        | -                 | -               |
+| 0.0276 | 28   | 4.196         | -                        | -                 | -               |
+| 0.0285 | 29   | 4.4802        | -                        | -                 | -               |
+| 0.0295 | 30   | 4.9193        | -                        | -                 | -               |
+| 0.0305 | 31   | 4.0996        | -                        | -                 | -               |
+| 0.0315 | 32   | 5.6307        | -                        | -                 | -               |
+| 0.0325 | 33   | 4.5745        | -                        | -                 | -               |
+| 0.0335 | 34   | 4.4514        | -                        | -                 | -               |
+| 0.0344 | 35   | 4.0617        | -                        | -                 | -               |
+| 0.0354 | 36   | 5.0298        | -                        | -                 | -               |
+| 0.0364 | 37   | 3.9815        | -                        | -                 | -               |
+| 0.0374 | 38   | 4.0871        | -                        | -                 | -               |
+| 0.0384 | 39   | 4.2378        | -                        | -                 | -               |
+| 0.0394 | 40   | 3.8226        | -                        | -                 | -               |
+| 0.0404 | 41   | 4.3519        | -                        | -                 | -               |
+| 0.0413 | 42   | 3.6345        | -                        | -                 | -               |
+| 0.0423 | 43   | 5.0829        | -                        | -                 | -               |
+| 0.0433 | 44   | 4.6701        | -                        | -                 | -               |
+| 0.0443 | 45   | 4.1371        | -                        | -                 | -               |
+| 0.0453 | 46   | 4.2418        | -                        | -                 | -               |
+| 0.0463 | 47   | 4.4766        | -                        | -                 | -               |
+| 0.0472 | 48   | 4.4797        | -                        | -                 | -               |
+| 0.0482 | 49   | 3.8471        | -                        | -                 | -               |
+| 0.0492 | 50   | 4.3194        | -                        | -                 | -               |
+| 0.0502 | 51   | 3.9426        | -                        | -                 | -               |
+| 0.0512 | 52   | 3.5333        | -                        | -                 | -               |
+| 0.0522 | 53   | 4.2426        | -                        | -                 | -               |
+| 0.0531 | 54   | 3.9816        | -                        | -                 | -               |
+| 0.0541 | 55   | 3.663         | -                        | -                 | -               |
+| 0.0551 | 56   | 3.9057        | -                        | -                 | -               |
+| 0.0561 | 57   | 4.0345        | -                        | -                 | -               |
+| 0.0571 | 58   | 3.5233        | -                        | -                 | -               |
+| 0.0581 | 59   | 3.7999        | -                        | -                 | -               |
+| 0.0591 | 60   | 3.1885        | -                        | -                 | -               |
+| 0.0600 | 61   | 3.6013        | -                        | -                 | -               |
+| 0.0610 | 62   | 3.392         | -                        | -                 | -               |
+| 0.0620 | 63   | 3.3814        | -                        | -                 | -               |
+| 0.0630 | 64   | 4.0428        | -                        | -                 | -               |
+| 0.0640 | 65   | 3.7825        | -                        | -                 | -               |
+| 0.0650 | 66   | 3.4181        | -                        | -                 | -               |
+| 0.0659 | 67   | 3.7793        | -                        | -                 | -               |
+| 0.0669 | 68   | 3.8344        | -                        | -                 | -               |
+| 0.0679 | 69   | 3.2165        | -                        | -                 | -               |
+| 0.0689 | 70   | 3.3811        | -                        | -                 | -               |
+| 0.0699 | 71   | 3.5984        | -                        | -                 | -               |
+| 0.0709 | 72   | 3.8583        | -                        | -                 | -               |
+| 0.0719 | 73   | 3.296         | -                        | -                 | -               |
+| 0.0728 | 74   | 2.7661        | -                        | -                 | -               |
+| 0.0738 | 75   | 2.9805        | -                        | -                 | -               |
+| 0.0748 | 76   | 2.566         | -                        | -                 | -               |
+| 0.0758 | 77   | 3.258         | -                        | -                 | -               |
+| 0.0768 | 78   | 3.3804        | -                        | -                 | -               |
+| 0.0778 | 79   | 2.8828        | -                        | -                 | -               |
+| 0.0787 | 80   | 3.1077        | -                        | -                 | -               |
+| 0.0797 | 81   | 2.9441        | -                        | -                 | -               |
+| 0.0807 | 82   | 2.9465        | -                        | -                 | -               |
+| 0.0817 | 83   | 2.7088        | -                        | -                 | -               |
+| 0.0827 | 84   | 2.9215        | -                        | -                 | -               |
+| 0.0837 | 85   | 3.4698        | -                        | -                 | -               |
+| 0.0846 | 86   | 2.2414        | -                        | -                 | -               |
+| 0.0856 | 87   | 3.1601        | -                        | -                 | -               |
+| 0.0866 | 88   | 2.7714        | -                        | -                 | -               |
+| 0.0876 | 89   | 3.0311        | -                        | -                 | -               |
+| 0.0886 | 90   | 3.0336        | -                        | -                 | -               |
+| 0.0896 | 91   | 1.9358        | -                        | -                 | -               |
+| 0.0906 | 92   | 2.6031        | -                        | -                 | -               |
+| 0.0915 | 93   | 2.7515        | -                        | -                 | -               |
+| 0.0925 | 94   | 2.8496        | -                        | -                 | -               |
+| 0.0935 | 95   | 1.8015        | -                        | -                 | -               |
+| 0.0945 | 96   | 2.8138        | -                        | -                 | -               |
+| 0.0955 | 97   | 2.0597        | -                        | -                 | -               |
+| 0.0965 | 98   | 2.1053        | -                        | -                 | -               |
+| 0.0974 | 99   | 2.6785        | -                        | -                 | -               |
+| 0.0984 | 100  | 2.588         | -                        | -                 | -               |
+| 0.0994 | 101  | 2.0099        | -                        | -                 | -               |
+| 0.1004 | 102  | 2.7947        | -                        | -                 | -               |
+| 0.1014 | 103  | 2.3274        | -                        | -                 | -               |
+| 0.1024 | 104  | 2.2545        | -                        | -                 | -               |
+| 0.1033 | 105  | 2.4575        | -                        | -                 | -               |
+| 0.1043 | 106  | 2.4413        | -                        | -                 | -               |
+| 0.1053 | 107  | 2.3185        | -                        | -                 | -               |
+| 0.1063 | 108  | 2.1577        | -                        | -                 | -               |
+| 0.1073 | 109  | 2.1278        | -                        | -                 | -               |
+| 0.1083 | 110  | 2.0967        | -                        | -                 | -               |
+| 0.1093 | 111  | 2.6142        | -                        | -                 | -               |
+| 0.1102 | 112  | 1.8553        | -                        | -                 | -               |
+| 0.1112 | 113  | 2.1523        | -                        | -                 | -               |
+| 0.1122 | 114  | 2.1726        | -                        | -                 | -               |
+| 0.1132 | 115  | 1.8564        | -                        | -                 | -               |
+| 0.1142 | 116  | 1.8413        | -                        | -                 | -               |
+| 0.1152 | 117  | 2.0441        | -                        | -                 | -               |
+| 0.1161 | 118  | 2.2159        | -                        | -                 | -               |
+| 0.1171 | 119  | 2.6779        | -                        | -                 | -               |
+| 0.1181 | 120  | 2.2976        | -                        | -                 | -               |
+| 0.1191 | 121  | 1.9407        | -                        | -                 | -               |
+| 0.1201 | 122  | 1.9019        | -                        | -                 | -               |
+| 0.1211 | 123  | 2.2149        | -                        | -                 | -               |
+| 0.1220 | 124  | 1.6823        | -                        | -                 | -               |
+| 0.1230 | 125  | 1.8402        | -                        | -                 | -               |
+| 0.1240 | 126  | 1.6914        | -                        | -                 | -               |
+| 0.125  | 127  | 2.1626        | -                        | -                 | -               |
+| 0.1260 | 128  | 1.6414        | -                        | -                 | -               |
+| 0.1270 | 129  | 2.2043        | -                        | -                 | -               |
+| 0.1280 | 130  | 1.9987        | -                        | -                 | -               |
+| 0.1289 | 131  | 1.8868        | -                        | -                 | -               |
+| 0.1299 | 132  | 1.8262        | -                        | -                 | -               |
+| 0.1309 | 133  | 2.0404        | -                        | -                 | -               |
+| 0.1319 | 134  | 1.9134        | -                        | -                 | -               |
+| 0.1329 | 135  | 2.3725        | -                        | -                 | -               |
+| 0.1339 | 136  | 1.4127        | -                        | -                 | -               |
+| 0.1348 | 137  | 1.6876        | -                        | -                 | -               |
+| 0.1358 | 138  | 1.8376        | -                        | -                 | -               |
+| 0.1368 | 139  | 1.6992        | -                        | -                 | -               |
+| 0.1378 | 140  | 1.5032        | -                        | -                 | -               |
+| 0.1388 | 141  | 2.0334        | -                        | -                 | -               |
+| 0.1398 | 142  | 2.3581        | -                        | -                 | -               |
+| 0.1407 | 143  | 1.4236        | -                        | -                 | -               |
+| 0.1417 | 144  | 2.202         | -                        | -                 | -               |
+| 0.1427 | 145  | 1.7654        | -                        | -                 | -               |
+| 0.1437 | 146  | 1.5748        | -                        | -                 | -               |
+| 0.1447 | 147  | 1.7996        | -                        | -                 | -               |
+| 0.1457 | 148  | 1.7517        | -                        | -                 | -               |
+| 0.1467 | 149  | 1.8933        | -                        | -                 | -               |
+| 0.1476 | 150  | 1.2836        | -                        | -                 | -               |
+| 0.1486 | 151  | 1.7145        | -                        | -                 | -               |
+| 0.1496 | 152  | 1.6499        | -                        | -                 | -               |
+| 0.1506 | 153  | 1.8273        | 0.4057                   | 0.4389            | 0.6725          |
+| 0.1516 | 154  | 2.2859        | -                        | -                 | -               |
+| 0.1526 | 155  | 1.0833        | -                        | -                 | -               |
+| 0.1535 | 156  | 1.6829        | -                        | -                 | -               |
+| 0.1545 | 157  | 2.1464        | -                        | -                 | -               |
+| 0.1555 | 158  | 1.745         | -                        | -                 | -               |
+| 0.1565 | 159  | 1.7319        | -                        | -                 | -               |
+| 0.1575 | 160  | 1.6968        | -                        | -                 | -               |
+| 0.1585 | 161  | 1.7401        | -                        | -                 | -               |
+| 0.1594 | 162  | 1.729         | -                        | -                 | -               |
+| 0.1604 | 163  | 2.0782        | -                        | -                 | -               |
+| 0.1614 | 164  | 2.6545        | -                        | -                 | -               |
+| 0.1624 | 165  | 1.4045        | -                        | -                 | -               |
+| 0.1634 | 166  | 1.2937        | -                        | -                 | -               |
+| 0.1644 | 167  | 1.1171        | -                        | -                 | -               |
+| 0.1654 | 168  | 1.3537        | -                        | -                 | -               |
+| 0.1663 | 169  | 1.7028        | -                        | -                 | -               |
+| 0.1673 | 170  | 1.4143        | -                        | -                 | -               |
+| 0.1683 | 171  | 1.8648        | -                        | -                 | -               |
+| 0.1693 | 172  | 1.6768        | -                        | -                 | -               |
+| 0.1703 | 173  | 1.9528        | -                        | -                 | -               |
+| 0.1713 | 174  | 1.1718        | -                        | -                 | -               |
+| 0.1722 | 175  | 1.8176        | -                        | -                 | -               |
+| 0.1732 | 176  | 0.8439        | -                        | -                 | -               |
+| 0.1742 | 177  | 1.5092        | -                        | -                 | -               |
+| 0.1752 | 178  | 1.1947        | -                        | -                 | -               |
+| 0.1762 | 179  | 1.6395        | -                        | -                 | -               |
+| 0.1772 | 180  | 1.4394        | -                        | -                 | -               |
+| 0.1781 | 181  | 1.7548        | -                        | -                 | -               |
+| 0.1791 | 182  | 1.1181        | -                        | -                 | -               |
+| 0.1801 | 183  | 1.0271        | -                        | -                 | -               |
+| 0.1811 | 184  | 2.3108        | -                        | -                 | -               |
+| 0.1821 | 185  | 2.1242        | -                        | -                 | -               |
+| 0.1831 | 186  | 1.9822        | -                        | -                 | -               |
+| 0.1841 | 187  | 2.3605        | -                        | -                 | -               |
+| 0.1850 | 188  | 1.5251        | -                        | -                 | -               |
+| 0.1860 | 189  | 1.2351        | -                        | -                 | -               |
+| 0.1870 | 190  | 1.5859        | -                        | -                 | -               |
+| 0.1880 | 191  | 1.8056        | -                        | -                 | -               |
+| 0.1890 | 192  | 1.349         | -                        | -                 | -               |
+| 0.1900 | 193  | 0.893         | -                        | -                 | -               |
+| 0.1909 | 194  | 1.5122        | -                        | -                 | -               |
+| 0.1919 | 195  | 1.3875        | -                        | -                 | -               |
+| 0.1929 | 196  | 1.29          | -                        | -                 | -               |
+| 0.1939 | 197  | 2.2931        | -                        | -                 | -               |
+| 0.1949 | 198  | 1.2663        | -                        | -                 | -               |
+| 0.1959 | 199  | 1.9712        | -                        | -                 | -               |
+| 0.1969 | 200  | 2.3307        | -                        | -                 | -               |
+| 0.1978 | 201  | 1.6544        | -                        | -                 | -               |
+| 0.1988 | 202  | 1.638         | -                        | -                 | -               |
+| 0.1998 | 203  | 1.3412        | -                        | -                 | -               |
+| 0.2008 | 204  | 1.4454        | -                        | -                 | -               |
+| 0.2018 | 205  | 1.5437        | -                        | -                 | -               |
+| 0.2028 | 206  | 1.4921        | -                        | -                 | -               |
+| 0.2037 | 207  | 1.4298        | -                        | -                 | -               |
+| 0.2047 | 208  | 1.6174        | -                        | -                 | -               |
+| 0.2057 | 209  | 1.4137        | -                        | -                 | -               |
+| 0.2067 | 210  | 1.5652        | -                        | -                 | -               |
+| 0.2077 | 211  | 1.1631        | -                        | -                 | -               |
+| 0.2087 | 212  | 1.2351        | -                        | -                 | -               |
+| 0.2096 | 213  | 1.7537        | -                        | -                 | -               |
+| 0.2106 | 214  | 1.3186        | -                        | -                 | -               |
+| 0.2116 | 215  | 1.2258        | -                        | -                 | -               |
+| 0.2126 | 216  | 0.7695        | -                        | -                 | -               |
+| 0.2136 | 217  | 1.2775        | -                        | -                 | -               |
+| 0.2146 | 218  | 1.6795        | -                        | -                 | -               |
+| 0.2156 | 219  | 1.2862        | -                        | -                 | -               |
+| 0.2165 | 220  | 1.1723        | -                        | -                 | -               |
+| 0.2175 | 221  | 1.3322        | -                        | -                 | -               |
+| 0.2185 | 222  | 1.7564        | -                        | -                 | -               |
+| 0.2195 | 223  | 1.1071        | -                        | -                 | -               |
+| 0.2205 | 224  | 1.2011        | -                        | -                 | -               |
+| 0.2215 | 225  | 1.2303        | -                        | -                 | -               |
+| 0.2224 | 226  | 1.212         | -                        | -                 | -               |
+| 0.2234 | 227  | 1.0117        | -                        | -                 | -               |
+| 0.2244 | 228  | 1.1907        | -                        | -                 | -               |
+| 0.2254 | 229  | 2.1293        | -                        | -                 | -               |
+| 0.2264 | 230  | 1.3063        | -                        | -                 | -               |
+| 0.2274 | 231  | 1.2841        | -                        | -                 | -               |
+| 0.2283 | 232  | 1.3778        | -                        | -                 | -               |
+| 0.2293 | 233  | 1.2242        | -                        | -                 | -               |
+| 0.2303 | 234  | 0.9227        | -                        | -                 | -               |
+| 0.2313 | 235  | 1.2221        | -                        | -                 | -               |
+| 0.2323 | 236  | 2.1041        | -                        | -                 | -               |
+| 0.2333 | 237  | 1.3341        | -                        | -                 | -               |
+| 0.2343 | 238  | 1.0876        | -                        | -                 | -               |
+| 0.2352 | 239  | 1.3328        | -                        | -                 | -               |
+| 0.2362 | 240  | 1.2958        | -                        | -                 | -               |
+| 0.2372 | 241  | 1.1522        | -                        | -                 | -               |
+| 0.2382 | 242  | 1.7942        | -                        | -                 | -               |
+| 0.2392 | 243  | 1.1325        | -                        | -                 | -               |
+| 0.2402 | 244  | 1.6466        | -                        | -                 | -               |
+| 0.2411 | 245  | 1.4608        | -                        | -                 | -               |
+| 0.2421 | 246  | 0.6375        | -                        | -                 | -               |
+| 0.2431 | 247  | 2.0177        | -                        | -                 | -               |
+| 0.2441 | 248  | 1.2069        | -                        | -                 | -               |
+| 0.2451 | 249  | 0.7639        | -                        | -                 | -               |
+| 0.2461 | 250  | 1.3465        | -                        | -                 | -               |
+| 0.2470 | 251  | 1.064         | -                        | -                 | -               |
+| 0.2480 | 252  | 1.3757        | -                        | -                 | -               |
+| 0.2490 | 253  | 1.612         | -                        | -                 | -               |
+| 0.25   | 254  | 0.7917        | -                        | -                 | -               |
+| 0.2510 | 255  | 1.5515        | -                        | -                 | -               |
+| 0.2520 | 256  | 0.799         | -                        | -                 | -               |
+| 0.2530 | 257  | 0.9882        | -                        | -                 | -               |
+| 0.2539 | 258  | 1.1814        | -                        | -                 | -               |
+| 0.2549 | 259  | 0.6394        | -                        | -                 | -               |
+| 0.2559 | 260  | 1.4756        | -                        | -                 | -               |
+| 0.2569 | 261  | 0.5338        | -                        | -                 | -               |
+| 0.2579 | 262  | 0.9779        | -                        | -                 | -               |
+| 0.2589 | 263  | 1.5307        | -                        | -                 | -               |
+| 0.2598 | 264  | 1.1213        | -                        | -                 | -               |
+| 0.2608 | 265  | 0.9482        | -                        | -                 | -               |
+| 0.2618 | 266  | 0.9599        | -                        | -                 | -               |
+| 0.2628 | 267  | 1.4455        | -                        | -                 | -               |
+| 0.2638 | 268  | 1.6496        | -                        | -                 | -               |
+| 0.2648 | 269  | 0.7402        | -                        | -                 | -               |
+| 0.2657 | 270  | 0.7835        | -                        | -                 | -               |
+| 0.2667 | 271  | 0.7821        | -                        | -                 | -               |
+| 0.2677 | 272  | 1.5422        | -                        | -                 | -               |
+| 0.2687 | 273  | 1.0995        | -                        | -                 | -               |
+| 0.2697 | 274  | 1.378         | -                        | -                 | -               |
+| 0.2707 | 275  | 1.3562        | -                        | -                 | -               |
+| 0.2717 | 276  | 0.7376        | -                        | -                 | -               |
+| 0.2726 | 277  | 1.1678        | -                        | -                 | -               |
+| 0.2736 | 278  | 1.2989        | -                        | -                 | -               |
+| 0.2746 | 279  | 1.9559        | -                        | -                 | -               |
+| 0.2756 | 280  | 1.1237        | -                        | -                 | -               |
+| 0.2766 | 281  | 0.952         | -                        | -                 | -               |
+| 0.2776 | 282  | 1.6629        | -                        | -                 | -               |
+| 0.2785 | 283  | 1.871         | -                        | -                 | -               |
+| 0.2795 | 284  | 1.5946        | -                        | -                 | -               |
+| 0.2805 | 285  | 1.4456        | -                        | -                 | -               |
+| 0.2815 | 286  | 1.4085        | -                        | -                 | -               |
+| 0.2825 | 287  | 1.1394        | -                        | -                 | -               |
+| 0.2835 | 288  | 1.0315        | -                        | -                 | -               |
+| 0.2844 | 289  | 1.488         | -                        | -                 | -               |
+| 0.2854 | 290  | 1.4006        | -                        | -                 | -               |
+| 0.2864 | 291  | 0.9237        | -                        | -                 | -               |
+| 0.2874 | 292  | 1.163         | -                        | -                 | -               |
+| 0.2884 | 293  | 1.7037        | -                        | -                 | -               |
+| 0.2894 | 294  | 0.8715        | -                        | -                 | -               |
+| 0.2904 | 295  | 1.2101        | -                        | -                 | -               |
+| 0.2913 | 296  | 1.1179        | -                        | -                 | -               |
+| 0.2923 | 297  | 1.3986        | -                        | -                 | -               |
+| 0.2933 | 298  | 1.7068        | -                        | -                 | -               |
+| 0.2943 | 299  | 0.8695        | -                        | -                 | -               |
+| 0.2953 | 300  | 1.3778        | -                        | -                 | -               |
+| 0.2963 | 301  | 1.2834        | -                        | -                 | -               |
+| 0.2972 | 302  | 0.8123        | -                        | -                 | -               |
+| 0.2982 | 303  | 1.6521        | -                        | -                 | -               |
+| 0.2992 | 304  | 1.1064        | -                        | -                 | -               |
+| 0.3002 | 305  | 0.9578        | -                        | -                 | -               |
+
+</details>
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.2.1
+- Transformers: 4.44.2
+- PyTorch: 2.5.0+cu121
+- Accelerate: 0.34.2
+- Datasets: 3.0.2
+- Tokenizers: 0.19.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### GISTEmbedLoss
+```bibtex
+@misc{solatorio2024gistembed,
+    title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning},
+    author={Aivin V. Solatorio},
+    year={2024},
+    eprint={2402.16829},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

checkpoint-305/added_tokens.json

@@ -0,0 +1,3 @@
+{
+  "[MASK]": 128000
+}

checkpoint-305/config.json

@@ -0,0 +1,35 @@
+{
+  "_name_or_path": "microsoft/deberta-v3-small",
+  "architectures": [
+    "DebertaV2Model"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-07,
+  "max_position_embeddings": 512,
+  "max_relative_positions": -1,
+  "model_type": "deberta-v2",
+  "norm_rel_ebd": "layer_norm",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 768,
+  "pos_att_type": [
+    "p2c",
+    "c2p"
+  ],
+  "position_biased_input": false,
+  "position_buckets": 256,
+  "relative_attention": true,
+  "share_att_key": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2",
+  "type_vocab_size": 0,
+  "vocab_size": 128100
+}

checkpoint-305/config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.2.1",
+    "transformers": "4.44.2",
+    "pytorch": "2.5.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

checkpoint-305/modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_AdvancedWeightedPooling",
+    "type": "__main__.AdvancedWeightedPooling"
+  }
+]

checkpoint-305/optimizer.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:40d96590c8a23f81ef1d1d7cf8887c6c2fe8f1e82ec3125086369be9140a5064
+size 141824506

checkpoint-305/pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b5cdb4c1fd9f1c98ad21daa454a7a507fe4e5cffc616dd6827e0a70dade6a898
+size 565251810

checkpoint-305/rng_state.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:08264892b9cb4b7a4e4c175ee7bfe5b88ecc57e10f027b10e81e11e42c424200
+size 14244

checkpoint-305/scheduler.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:30f526cd22b9ea7b7b16277caa0b815306677474381771eb078b44e3891ede9f
+size 1256

checkpoint-305/sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

checkpoint-305/special_tokens_map.json

@@ -0,0 +1,15 @@
+{
+  "bos_token": "[CLS]",
+  "cls_token": "[CLS]",
+  "eos_token": "[SEP]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

checkpoint-305/spm.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c679fbf93643d19aab7ee10c0b99e460bdbc02fedf34b92b05af343b4af586fd
+size 2464616

checkpoint-305/tokenizer_config.json

@@ -0,0 +1,58 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128000": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "[CLS]",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "eos_token": "[SEP]",
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "sp_model_kwargs": {},
+  "split_by_punct": false,
+  "tokenizer_class": "DebertaV2Tokenizer",
+  "unk_token": "[UNK]",
+  "vocab_type": "spm"
+}

checkpoint-305/trainer_state.json

@@ -0,0 +1,2256 @@
+{
+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 0.3001968503937008,
+  "eval_steps": 153,
+  "global_step": 305,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "epoch": 0.000984251968503937,
+      "grad_norm": NaN,
+      "learning_rate": 0.0,
+      "loss": 6.0688,
+      "step": 1
+    },
+    {
+      "epoch": 0.001968503937007874,
+      "grad_norm": NaN,
+      "learning_rate": 0.0,
+      "loss": 7.5576,
+      "step": 2
+    },
+    {
+      "epoch": 0.002952755905511811,
+      "grad_norm": Infinity,
+      "learning_rate": 0.0,
+      "loss": 4.6849,
+      "step": 3
+    },
+    {
+      "epoch": 0.003937007874015748,
+      "grad_norm": Infinity,
+      "learning_rate": 0.0,
+      "loss": 5.4503,
+      "step": 4
+    },
+    {
+      "epoch": 0.004921259842519685,
+      "grad_norm": 32.947513580322266,
+      "learning_rate": 9.940357852882705e-10,
+      "loss": 5.6057,
+      "step": 5
+    },
+    {
+      "epoch": 0.005905511811023622,
+      "grad_norm": 40.39633560180664,
+      "learning_rate": 1.988071570576541e-09,
+      "loss": 6.3049,
+      "step": 6
+    },
+    {
+      "epoch": 0.006889763779527559,
+      "grad_norm": 44.38965606689453,
+      "learning_rate": 2.9821073558648116e-09,
+      "loss": 6.8336,
+      "step": 7
+    },
+    {
+      "epoch": 0.007874015748031496,
+      "grad_norm": 28.46092987060547,
+      "learning_rate": 3.976143141153082e-09,
+      "loss": 5.0777,
+      "step": 8
+    },
+    {
+      "epoch": 0.008858267716535433,
+      "grad_norm": 25.817373275756836,
+      "learning_rate": 4.970178926441353e-09,
+      "loss": 4.8358,
+      "step": 9
+    },
+    {
+      "epoch": 0.00984251968503937,
+      "grad_norm": 27.06571388244629,
+      "learning_rate": 5.964214711729623e-09,
+      "loss": 4.641,
+      "step": 10
+    },
+    {
+      "epoch": 0.010826771653543307,
+      "grad_norm": 27.857297897338867,
+      "learning_rate": 6.9582504970178946e-09,
+      "loss": 4.828,
+      "step": 11
+    },
+    {
+      "epoch": 0.011811023622047244,
+      "grad_norm": 29.35377311706543,
+      "learning_rate": 7.952286282306164e-09,
+      "loss": 5.2269,
+      "step": 12
+    },
+    {
+      "epoch": 0.012795275590551181,
+      "grad_norm": 32.06682586669922,
+      "learning_rate": 8.946322067594435e-09,
+      "loss": 5.6772,
+      "step": 13
+    },
+    {
+      "epoch": 0.013779527559055118,
+      "grad_norm": 30.774206161499023,
+      "learning_rate": 9.940357852882705e-09,
+      "loss": 5.1422,
+      "step": 14
+    },
+    {
+      "epoch": 0.014763779527559055,
+      "grad_norm": 45.917659759521484,
+      "learning_rate": 1.0934393638170978e-08,
+      "loss": 6.2469,
+      "step": 15
+    },
+    {
+      "epoch": 0.015748031496062992,
+      "grad_norm": 30.189363479614258,
+      "learning_rate": 1.1928429423459246e-08,
+      "loss": 4.6802,
+      "step": 16
+    },
+    {
+      "epoch": 0.01673228346456693,
+      "grad_norm": 25.023223876953125,
+      "learning_rate": 1.2922465208747517e-08,
+      "loss": 4.5492,
+      "step": 17
+    },
+    {
+      "epoch": 0.017716535433070866,
+      "grad_norm": 26.336748123168945,
+      "learning_rate": 1.3916500994035789e-08,
+      "loss": 4.8062,
+      "step": 18
+    },
+    {
+      "epoch": 0.018700787401574805,
+      "grad_norm": 51.350032806396484,
+      "learning_rate": 1.4910536779324056e-08,
+      "loss": 7.5141,
+      "step": 19
+    },
+    {
+      "epoch": 0.01968503937007874,
+      "grad_norm": 33.953224182128906,
+      "learning_rate": 1.590457256461233e-08,
+      "loss": 5.5202,
+      "step": 20
+    },
+    {
+      "epoch": 0.02066929133858268,
+      "grad_norm": 36.828433990478516,
+      "learning_rate": 1.68986083499006e-08,
+      "loss": 6.5025,
+      "step": 21
+    },
+    {
+      "epoch": 0.021653543307086614,
+      "grad_norm": 43.68119812011719,
+      "learning_rate": 1.789264413518887e-08,
+      "loss": 7.318,
+      "step": 22
+    },
+    {
+      "epoch": 0.022637795275590553,
+      "grad_norm": 23.9952392578125,
+      "learning_rate": 1.888667992047714e-08,
+      "loss": 4.6458,
+      "step": 23
+    },
+    {
+      "epoch": 0.023622047244094488,
+      "grad_norm": 19.315523147583008,
+      "learning_rate": 1.988071570576541e-08,
+      "loss": 4.6191,
+      "step": 24
+    },
+    {
+      "epoch": 0.024606299212598427,
+      "grad_norm": 22.747207641601562,
+      "learning_rate": 2.087475149105368e-08,
+      "loss": 4.3159,
+      "step": 25
+    },
+    {
+      "epoch": 0.025590551181102362,
+      "grad_norm": 32.90699768066406,
+      "learning_rate": 2.1868787276341955e-08,
+      "loss": 6.3677,
+      "step": 26
+    },
+    {
+      "epoch": 0.0265748031496063,
+      "grad_norm": 25.224443435668945,
+      "learning_rate": 2.2862823061630224e-08,
+      "loss": 5.6052,
+      "step": 27
+    },
+    {
+      "epoch": 0.027559055118110236,
+      "grad_norm": 16.32752799987793,
+      "learning_rate": 2.3856858846918493e-08,
+      "loss": 4.196,
+      "step": 28
+    },
+    {
+      "epoch": 0.028543307086614175,
+      "grad_norm": 19.50617218017578,
+      "learning_rate": 2.4850894632206765e-08,
+      "loss": 4.4802,
+      "step": 29
+    },
+    {
+      "epoch": 0.02952755905511811,
+      "grad_norm": 21.613880157470703,
+      "learning_rate": 2.5844930417495034e-08,
+      "loss": 4.9193,
+      "step": 30
+    },
+    {
+      "epoch": 0.03051181102362205,
+      "grad_norm": 16.059247970581055,
+      "learning_rate": 2.6838966202783303e-08,
+      "loss": 4.0996,
+      "step": 31
+    },
+    {
+      "epoch": 0.031496062992125984,
+      "grad_norm": 27.45536994934082,
+      "learning_rate": 2.7833001988071578e-08,
+      "loss": 5.6307,
+      "step": 32
+    },
+    {
+      "epoch": 0.03248031496062992,
+      "grad_norm": 19.249103546142578,
+      "learning_rate": 2.8827037773359847e-08,
+      "loss": 4.5745,
+      "step": 33
+    },
+    {
+      "epoch": 0.03346456692913386,
+      "grad_norm": 17.516521453857422,
+      "learning_rate": 2.982107355864811e-08,
+      "loss": 4.4514,
+      "step": 34
+    },
+    {
+      "epoch": 0.0344488188976378,
+      "grad_norm": 11.684857368469238,
+      "learning_rate": 3.081510934393639e-08,
+      "loss": 4.0617,
+      "step": 35
+    },
+    {
+      "epoch": 0.03543307086614173,
+      "grad_norm": 16.813453674316406,
+      "learning_rate": 3.180914512922466e-08,
+      "loss": 5.0298,
+      "step": 36
+    },
+    {
+      "epoch": 0.03641732283464567,
+      "grad_norm": 10.92259407043457,
+      "learning_rate": 3.280318091451293e-08,
+      "loss": 3.9815,
+      "step": 37
+    },
+    {
+      "epoch": 0.03740157480314961,
+      "grad_norm": 11.449631690979004,
+      "learning_rate": 3.37972166998012e-08,
+      "loss": 4.0871,
+      "step": 38
+    },
+    {
+      "epoch": 0.038385826771653545,
+      "grad_norm": 11.223074913024902,
+      "learning_rate": 3.479125248508947e-08,
+      "loss": 4.2378,
+      "step": 39
+    },
+    {
+      "epoch": 0.03937007874015748,
+      "grad_norm": 9.864290237426758,
+      "learning_rate": 3.578528827037774e-08,
+      "loss": 3.8226,
+      "step": 40
+    },
+    {
+      "epoch": 0.040354330708661415,
+      "grad_norm": 11.084328651428223,
+      "learning_rate": 3.6779324055666005e-08,
+      "loss": 4.3519,
+      "step": 41
+    },
+    {
+      "epoch": 0.04133858267716536,
+      "grad_norm": 9.965168952941895,
+      "learning_rate": 3.777335984095428e-08,
+      "loss": 3.6345,
+      "step": 42
+    },
+    {
+      "epoch": 0.04232283464566929,
+      "grad_norm": 18.375577926635742,
+      "learning_rate": 3.8767395626242556e-08,
+      "loss": 5.0829,
+      "step": 43
+    },
+    {
+      "epoch": 0.04330708661417323,
+      "grad_norm": 11.232723236083984,
+      "learning_rate": 3.976143141153082e-08,
+      "loss": 4.6701,
+      "step": 44
+    },
+    {
+      "epoch": 0.04429133858267716,
+      "grad_norm": 10.896126747131348,
+      "learning_rate": 4.0755467196819094e-08,
+      "loss": 4.1371,
+      "step": 45
+    },
+    {
+      "epoch": 0.045275590551181105,
+      "grad_norm": 10.237566947937012,
+      "learning_rate": 4.174950298210736e-08,
+      "loss": 4.2418,
+      "step": 46
+    },
+    {
+      "epoch": 0.04625984251968504,
+      "grad_norm": 13.484391212463379,
+      "learning_rate": 4.274353876739563e-08,
+      "loss": 4.4766,
+      "step": 47
+    },
+    {
+      "epoch": 0.047244094488188976,
+      "grad_norm": 9.769365310668945,
+      "learning_rate": 4.373757455268391e-08,
+      "loss": 4.4797,
+      "step": 48
+    },
+    {
+      "epoch": 0.04822834645669291,
+      "grad_norm": 9.953036308288574,
+      "learning_rate": 4.4731610337972176e-08,
+      "loss": 3.8471,
+      "step": 49
+    },
+    {
+      "epoch": 0.04921259842519685,
+      "grad_norm": 15.378759384155273,
+      "learning_rate": 4.572564612326045e-08,
+      "loss": 4.3194,
+      "step": 50
+    },
+    {
+      "epoch": 0.05019685039370079,
+      "grad_norm": 9.474905967712402,
+      "learning_rate": 4.6719681908548713e-08,
+      "loss": 3.9426,
+      "step": 51
+    },
+    {
+      "epoch": 0.051181102362204724,
+      "grad_norm": 8.882713317871094,
+      "learning_rate": 4.7713717693836986e-08,
+      "loss": 3.5333,
+      "step": 52
+    },
+    {
+      "epoch": 0.05216535433070866,
+      "grad_norm": 14.956167221069336,
+      "learning_rate": 4.870775347912525e-08,
+      "loss": 4.2426,
+      "step": 53
+    },
+    {
+      "epoch": 0.0531496062992126,
+      "grad_norm": 9.333856582641602,
+      "learning_rate": 4.970178926441353e-08,
+      "loss": 3.9816,
+      "step": 54
+    },
+    {
+      "epoch": 0.054133858267716536,
+      "grad_norm": 9.034503936767578,
+      "learning_rate": 5.06958250497018e-08,
+      "loss": 3.663,
+      "step": 55
+    },
+    {
+      "epoch": 0.05511811023622047,
+      "grad_norm": 10.114209175109863,
+      "learning_rate": 5.168986083499007e-08,
+      "loss": 3.9057,
+      "step": 56
+    },
+    {
+      "epoch": 0.05610236220472441,
+      "grad_norm": 9.802759170532227,
+      "learning_rate": 5.268389662027834e-08,
+      "loss": 4.0345,
+      "step": 57
+    },
+    {
+      "epoch": 0.05708661417322835,
+      "grad_norm": 10.378804206848145,
+      "learning_rate": 5.3677932405566605e-08,
+      "loss": 3.5233,
+      "step": 58
+    },
+    {
+      "epoch": 0.058070866141732284,
+      "grad_norm": 10.237386703491211,
+      "learning_rate": 5.467196819085488e-08,
+      "loss": 3.7999,
+      "step": 59
+    },
+    {
+      "epoch": 0.05905511811023622,
+      "grad_norm": 11.103493690490723,
+      "learning_rate": 5.5666003976143156e-08,
+      "loss": 3.1885,
+      "step": 60
+    },
+    {
+      "epoch": 0.060039370078740155,
+      "grad_norm": 11.967391014099121,
+      "learning_rate": 5.666003976143142e-08,
+      "loss": 3.6013,
+      "step": 61
+    },
+    {
+      "epoch": 0.0610236220472441,
+      "grad_norm": 10.79468822479248,
+      "learning_rate": 5.7654075546719694e-08,
+      "loss": 3.392,
+      "step": 62
+    },
+    {
+      "epoch": 0.06200787401574803,
+      "grad_norm": 13.18323802947998,
+      "learning_rate": 5.864811133200796e-08,
+      "loss": 3.3814,
+      "step": 63
+    },
+    {
+      "epoch": 0.06299212598425197,
+      "grad_norm": 15.541842460632324,
+      "learning_rate": 5.964214711729623e-08,
+      "loss": 4.0428,
+      "step": 64
+    },
+    {
+      "epoch": 0.0639763779527559,
+      "grad_norm": 13.166780471801758,
+      "learning_rate": 6.06361829025845e-08,
+      "loss": 3.7825,
+      "step": 65
+    },
+    {
+      "epoch": 0.06496062992125984,
+      "grad_norm": 12.48865795135498,
+      "learning_rate": 6.163021868787278e-08,
+      "loss": 3.4181,
+      "step": 66
+    },
+    {
+      "epoch": 0.06594488188976377,
+      "grad_norm": 14.15877628326416,
+      "learning_rate": 6.262425447316104e-08,
+      "loss": 3.7793,
+      "step": 67
+    },
+    {
+      "epoch": 0.06692913385826772,
+      "grad_norm": 18.16546630859375,
+      "learning_rate": 6.361829025844931e-08,
+      "loss": 3.8344,
+      "step": 68
+    },
+    {
+      "epoch": 0.06791338582677166,
+      "grad_norm": 14.758286476135254,
+      "learning_rate": 6.461232604373759e-08,
+      "loss": 3.2165,
+      "step": 69
+    },
+    {
+      "epoch": 0.0688976377952756,
+      "grad_norm": 15.924538612365723,
+      "learning_rate": 6.560636182902586e-08,
+      "loss": 3.3811,
+      "step": 70
+    },
+    {
+      "epoch": 0.06988188976377953,
+      "grad_norm": 17.181415557861328,
+      "learning_rate": 6.660039761431412e-08,
+      "loss": 3.5984,
+      "step": 71
+    },
+    {
+      "epoch": 0.07086614173228346,
+      "grad_norm": 16.691272735595703,
+      "learning_rate": 6.75944333996024e-08,
+      "loss": 3.8583,
+      "step": 72
+    },
+    {
+      "epoch": 0.0718503937007874,
+      "grad_norm": 17.478809356689453,
+      "learning_rate": 6.858846918489067e-08,
+      "loss": 3.296,
+      "step": 73
+    },
+    {
+      "epoch": 0.07283464566929133,
+      "grad_norm": 15.149248123168945,
+      "learning_rate": 6.958250497017893e-08,
+      "loss": 2.7661,
+      "step": 74
+    },
+    {
+      "epoch": 0.07381889763779527,
+      "grad_norm": 15.139701843261719,
+      "learning_rate": 7.057654075546721e-08,
+      "loss": 2.9805,
+      "step": 75
+    },
+    {
+      "epoch": 0.07480314960629922,
+      "grad_norm": 14.794472694396973,
+      "learning_rate": 7.157057654075548e-08,
+      "loss": 2.566,
+      "step": 76
+    },
+    {
+      "epoch": 0.07578740157480315,
+      "grad_norm": 15.433120727539062,
+      "learning_rate": 7.256461232604374e-08,
+      "loss": 3.258,
+      "step": 77
+    },
+    {
+      "epoch": 0.07677165354330709,
+      "grad_norm": 22.558115005493164,
+      "learning_rate": 7.355864811133201e-08,
+      "loss": 3.3804,
+      "step": 78
+    },
+    {
+      "epoch": 0.07775590551181102,
+      "grad_norm": 19.630340576171875,
+      "learning_rate": 7.455268389662029e-08,
+      "loss": 2.8828,
+      "step": 79
+    },
+    {
+      "epoch": 0.07874015748031496,
+      "grad_norm": 17.1998233795166,
+      "learning_rate": 7.554671968190855e-08,
+      "loss": 3.1077,
+      "step": 80
+    },
+    {
+      "epoch": 0.0797244094488189,
+      "grad_norm": 13.535666465759277,
+      "learning_rate": 7.654075546719683e-08,
+      "loss": 2.9441,
+      "step": 81
+    },
+    {
+      "epoch": 0.08070866141732283,
+      "grad_norm": 14.031355857849121,
+      "learning_rate": 7.753479125248511e-08,
+      "loss": 2.9465,
+      "step": 82
+    },
+    {
+      "epoch": 0.08169291338582677,
+      "grad_norm": 13.701343536376953,
+      "learning_rate": 7.852882703777338e-08,
+      "loss": 2.7088,
+      "step": 83
+    },
+    {
+      "epoch": 0.08267716535433071,
+      "grad_norm": 15.102946281433105,
+      "learning_rate": 7.952286282306164e-08,
+      "loss": 2.9215,
+      "step": 84
+    },
+    {
+      "epoch": 0.08366141732283465,
+      "grad_norm": 20.15928077697754,
+      "learning_rate": 8.051689860834992e-08,
+      "loss": 3.4698,
+      "step": 85
+    },
+    {
+      "epoch": 0.08464566929133858,
+      "grad_norm": 14.807561874389648,
+      "learning_rate": 8.151093439363819e-08,
+      "loss": 2.2414,
+      "step": 86
+    },
+    {
+      "epoch": 0.08562992125984252,
+      "grad_norm": 18.390995025634766,
+      "learning_rate": 8.250497017892645e-08,
+      "loss": 3.1601,
+      "step": 87
+    },
+    {
+      "epoch": 0.08661417322834646,
+      "grad_norm": 17.47426986694336,
+      "learning_rate": 8.349900596421472e-08,
+      "loss": 2.7714,
+      "step": 88
+    },
+    {
+      "epoch": 0.08759842519685039,
+      "grad_norm": 19.336660385131836,
+      "learning_rate": 8.4493041749503e-08,
+      "loss": 3.0311,
+      "step": 89
+    },
+    {
+      "epoch": 0.08858267716535433,
+      "grad_norm": 22.308195114135742,
+      "learning_rate": 8.548707753479126e-08,
+      "loss": 3.0336,
+      "step": 90
+    },
+    {
+      "epoch": 0.08956692913385826,
+      "grad_norm": 16.85112762451172,
+      "learning_rate": 8.648111332007953e-08,
+      "loss": 1.9358,
+      "step": 91
+    },
+    {
+      "epoch": 0.09055118110236221,
+      "grad_norm": 18.357120513916016,
+      "learning_rate": 8.747514910536782e-08,
+      "loss": 2.6031,
+      "step": 92
+    },
+    {
+      "epoch": 0.09153543307086615,
+      "grad_norm": 21.082101821899414,
+      "learning_rate": 8.846918489065609e-08,
+      "loss": 2.7515,
+      "step": 93
+    },
+    {
+      "epoch": 0.09251968503937008,
+      "grad_norm": 21.116485595703125,
+      "learning_rate": 8.946322067594435e-08,
+      "loss": 2.8496,
+      "step": 94
+    },
+    {
+      "epoch": 0.09350393700787402,
+      "grad_norm": 15.967782020568848,
+      "learning_rate": 9.045725646123262e-08,
+      "loss": 1.8015,
+      "step": 95
+    },
+    {
+      "epoch": 0.09448818897637795,
+      "grad_norm": 20.430376052856445,
+      "learning_rate": 9.14512922465209e-08,
+      "loss": 2.8138,
+      "step": 96
+    },
+    {
+      "epoch": 0.09547244094488189,
+      "grad_norm": 18.769359588623047,
+      "learning_rate": 9.244532803180916e-08,
+      "loss": 2.0597,
+      "step": 97
+    },
+    {
+      "epoch": 0.09645669291338582,
+      "grad_norm": 19.360021591186523,
+      "learning_rate": 9.343936381709743e-08,
+      "loss": 2.1053,
+      "step": 98
+    },
+    {
+      "epoch": 0.09744094488188976,
+      "grad_norm": 22.48013687133789,
+      "learning_rate": 9.44333996023857e-08,
+      "loss": 2.6785,
+      "step": 99
+    },
+    {
+      "epoch": 0.0984251968503937,
+      "grad_norm": 21.50940704345703,
+      "learning_rate": 9.542743538767397e-08,
+      "loss": 2.588,
+      "step": 100
+    },
+    {
+      "epoch": 0.09940944881889764,
+      "grad_norm": 17.409393310546875,
+      "learning_rate": 9.642147117296224e-08,
+      "loss": 2.0099,
+      "step": 101
+    },
+    {
+      "epoch": 0.10039370078740158,
+      "grad_norm": 22.641996383666992,
+      "learning_rate": 9.74155069582505e-08,
+      "loss": 2.7947,
+      "step": 102
+    },
+    {
+      "epoch": 0.10137795275590551,
+      "grad_norm": 18.91701889038086,
+      "learning_rate": 9.840954274353878e-08,
+      "loss": 2.3274,
+      "step": 103
+    },
+    {
+      "epoch": 0.10236220472440945,
+      "grad_norm": 21.317251205444336,
+      "learning_rate": 9.940357852882706e-08,
+      "loss": 2.2545,
+      "step": 104
+    },
+    {
+      "epoch": 0.10334645669291338,
+      "grad_norm": 24.298248291015625,
+      "learning_rate": 1.0039761431411533e-07,
+      "loss": 2.4575,
+      "step": 105
+    },
+    {
+      "epoch": 0.10433070866141732,
+      "grad_norm": 19.284252166748047,
+      "learning_rate": 1.013916500994036e-07,
+      "loss": 2.4413,
+      "step": 106
+    },
+    {
+      "epoch": 0.10531496062992125,
+      "grad_norm": 19.619426727294922,
+      "learning_rate": 1.0238568588469187e-07,
+      "loss": 2.3185,
+      "step": 107
+    },
+    {
+      "epoch": 0.1062992125984252,
+      "grad_norm": 20.323917388916016,
+      "learning_rate": 1.0337972166998014e-07,
+      "loss": 2.1577,
+      "step": 108
+    },
+    {
+      "epoch": 0.10728346456692914,
+      "grad_norm": 20.885499954223633,
+      "learning_rate": 1.043737574552684e-07,
+      "loss": 2.1278,
+      "step": 109
+    },
+    {
+      "epoch": 0.10826771653543307,
+      "grad_norm": 21.687036514282227,
+      "learning_rate": 1.0536779324055668e-07,
+      "loss": 2.0967,
+      "step": 110
+    },
+    {
+      "epoch": 0.10925196850393701,
+      "grad_norm": 23.031898498535156,
+      "learning_rate": 1.0636182902584495e-07,
+      "loss": 2.6142,
+      "step": 111
+    },
+    {
+      "epoch": 0.11023622047244094,
+      "grad_norm": 20.64027976989746,
+      "learning_rate": 1.0735586481113321e-07,
+      "loss": 1.8553,
+      "step": 112
+    },
+    {
+      "epoch": 0.11122047244094488,
+      "grad_norm": 18.268877029418945,
+      "learning_rate": 1.0834990059642149e-07,
+      "loss": 2.1523,
+      "step": 113
+    },
+    {
+      "epoch": 0.11220472440944881,
+      "grad_norm": 22.190549850463867,
+      "learning_rate": 1.0934393638170976e-07,
+      "loss": 2.1726,
+      "step": 114
+    },
+    {
+      "epoch": 0.11318897637795275,
+      "grad_norm": 18.314851760864258,
+      "learning_rate": 1.1033797216699802e-07,
+      "loss": 1.8564,
+      "step": 115
+    },
+    {
+      "epoch": 0.1141732283464567,
+      "grad_norm": 19.17725944519043,
+      "learning_rate": 1.1133200795228631e-07,
+      "loss": 1.8413,
+      "step": 116
+    },
+    {
+      "epoch": 0.11515748031496063,
+      "grad_norm": 24.518449783325195,
+      "learning_rate": 1.1232604373757458e-07,
+      "loss": 2.0441,
+      "step": 117
+    },
+    {
+      "epoch": 0.11614173228346457,
+      "grad_norm": 24.78040313720703,
+      "learning_rate": 1.1332007952286284e-07,
+      "loss": 2.2159,
+      "step": 118
+    },
+    {
+      "epoch": 0.1171259842519685,
+      "grad_norm": 28.552289962768555,
+      "learning_rate": 1.1431411530815111e-07,
+      "loss": 2.6779,
+      "step": 119
+    },
+    {
+      "epoch": 0.11811023622047244,
+      "grad_norm": 21.049219131469727,
+      "learning_rate": 1.1530815109343939e-07,
+      "loss": 2.2976,
+      "step": 120
+    },
+    {
+      "epoch": 0.11909448818897637,
+      "grad_norm": 23.962575912475586,
+      "learning_rate": 1.1630218687872765e-07,
+      "loss": 1.9407,
+      "step": 121
+    },
+    {
+      "epoch": 0.12007874015748031,
+      "grad_norm": 20.15193748474121,
+      "learning_rate": 1.1729622266401592e-07,
+      "loss": 1.9019,
+      "step": 122
+    },
+    {
+      "epoch": 0.12106299212598425,
+      "grad_norm": 22.563459396362305,
+      "learning_rate": 1.182902584493042e-07,
+      "loss": 2.2149,
+      "step": 123
+    },
+    {
+      "epoch": 0.1220472440944882,
+      "grad_norm": 16.86638832092285,
+      "learning_rate": 1.1928429423459245e-07,
+      "loss": 1.6823,
+      "step": 124
+    },
+    {
+      "epoch": 0.12303149606299213,
+      "grad_norm": 20.249584197998047,
+      "learning_rate": 1.2027833001988073e-07,
+      "loss": 1.8402,
+      "step": 125
+    },
+    {
+      "epoch": 0.12401574803149606,
+      "grad_norm": 19.5487117767334,
+      "learning_rate": 1.21272365805169e-07,
+      "loss": 1.6914,
+      "step": 126
+    },
+    {
+      "epoch": 0.125,
+      "grad_norm": 19.093505859375,
+      "learning_rate": 1.222664015904573e-07,
+      "loss": 2.1626,
+      "step": 127
+    },
+    {
+      "epoch": 0.12598425196850394,
+      "grad_norm": 17.598743438720703,
+      "learning_rate": 1.2326043737574557e-07,
+      "loss": 1.6414,
+      "step": 128
+    },
+    {
+      "epoch": 0.12696850393700787,
+      "grad_norm": 22.712181091308594,
+      "learning_rate": 1.2425447316103382e-07,
+      "loss": 2.2043,
+      "step": 129
+    },
+    {
+      "epoch": 0.1279527559055118,
+      "grad_norm": 20.516571044921875,
+      "learning_rate": 1.2524850894632207e-07,
+      "loss": 1.9987,
+      "step": 130
+    },
+    {
+      "epoch": 0.12893700787401574,
+      "grad_norm": 22.58023452758789,
+      "learning_rate": 1.2624254473161035e-07,
+      "loss": 1.8868,
+      "step": 131
+    },
+    {
+      "epoch": 0.12992125984251968,
+      "grad_norm": 17.800390243530273,
+      "learning_rate": 1.2723658051689863e-07,
+      "loss": 1.8262,
+      "step": 132
+    },
+    {
+      "epoch": 0.1309055118110236,
+      "grad_norm": 20.7684383392334,
+      "learning_rate": 1.282306163021869e-07,
+      "loss": 2.0404,
+      "step": 133
+    },
+    {
+      "epoch": 0.13188976377952755,
+      "grad_norm": 20.88973617553711,
+      "learning_rate": 1.2922465208747519e-07,
+      "loss": 1.9134,
+      "step": 134
+    },
+    {
+      "epoch": 0.1328740157480315,
+      "grad_norm": 21.78728485107422,
+      "learning_rate": 1.3021868787276344e-07,
+      "loss": 2.3725,
+      "step": 135
+    },
+    {
+      "epoch": 0.13385826771653545,
+      "grad_norm": 16.885374069213867,
+      "learning_rate": 1.3121272365805172e-07,
+      "loss": 1.4127,
+      "step": 136
+    },
+    {
+      "epoch": 0.13484251968503938,
+      "grad_norm": 18.944265365600586,
+      "learning_rate": 1.3220675944333997e-07,
+      "loss": 1.6876,
+      "step": 137
+    },
+    {
+      "epoch": 0.13582677165354332,
+      "grad_norm": 18.579692840576172,
+      "learning_rate": 1.3320079522862825e-07,
+      "loss": 1.8376,
+      "step": 138
+    },
+    {
+      "epoch": 0.13681102362204725,
+      "grad_norm": 19.68845558166504,
+      "learning_rate": 1.3419483101391653e-07,
+      "loss": 1.6992,
+      "step": 139
+    },
+    {
+      "epoch": 0.1377952755905512,
+      "grad_norm": 21.27842903137207,
+      "learning_rate": 1.351888667992048e-07,
+      "loss": 1.5032,
+      "step": 140
+    },
+    {
+      "epoch": 0.13877952755905512,
+      "grad_norm": 28.506824493408203,
+      "learning_rate": 1.3618290258449306e-07,
+      "loss": 2.0334,
+      "step": 141
+    },
+    {
+      "epoch": 0.13976377952755906,
+      "grad_norm": 24.97527313232422,
+      "learning_rate": 1.3717693836978134e-07,
+      "loss": 2.3581,
+      "step": 142
+    },
+    {
+      "epoch": 0.140748031496063,
+      "grad_norm": 22.391145706176758,
+      "learning_rate": 1.381709741550696e-07,
+      "loss": 1.4236,
+      "step": 143
+    },
+    {
+      "epoch": 0.14173228346456693,
+      "grad_norm": 22.977148056030273,
+      "learning_rate": 1.3916500994035787e-07,
+      "loss": 2.202,
+      "step": 144
+    },
+    {
+      "epoch": 0.14271653543307086,
+      "grad_norm": 25.096189498901367,
+      "learning_rate": 1.4015904572564615e-07,
+      "loss": 1.7654,
+      "step": 145
+    },
+    {
+      "epoch": 0.1437007874015748,
+      "grad_norm": 28.528640747070312,
+      "learning_rate": 1.4115308151093443e-07,
+      "loss": 1.5748,
+      "step": 146
+    },
+    {
+      "epoch": 0.14468503937007873,
+      "grad_norm": 30.80350685119629,
+      "learning_rate": 1.421471172962227e-07,
+      "loss": 1.7996,
+      "step": 147
+    },
+    {
+      "epoch": 0.14566929133858267,
+      "grad_norm": 22.726850509643555,
+      "learning_rate": 1.4314115308151096e-07,
+      "loss": 1.7517,
+      "step": 148
+    },
+    {
+      "epoch": 0.1466535433070866,
+      "grad_norm": 24.926170349121094,
+      "learning_rate": 1.4413518886679924e-07,
+      "loss": 1.8933,
+      "step": 149
+    },
+    {
+      "epoch": 0.14763779527559054,
+      "grad_norm": 20.570375442504883,
+      "learning_rate": 1.451292246520875e-07,
+      "loss": 1.2836,
+      "step": 150
+    },
+    {
+      "epoch": 0.1486220472440945,
+      "grad_norm": 21.08650779724121,
+      "learning_rate": 1.4612326043737577e-07,
+      "loss": 1.7145,
+      "step": 151
+    },
+    {
+      "epoch": 0.14960629921259844,
+      "grad_norm": 24.37209701538086,
+      "learning_rate": 1.4711729622266402e-07,
+      "loss": 1.6499,
+      "step": 152
+    },
+    {
+      "epoch": 0.15059055118110237,
+      "grad_norm": 26.801511764526367,
+      "learning_rate": 1.4811133200795232e-07,
+      "loss": 1.8273,
+      "step": 153
+    },
+    {
+      "epoch": 0.15059055118110237,
+      "eval_Qnli-dev_cosine_accuracy": 0.646484375,
+      "eval_Qnli-dev_cosine_accuracy_threshold": 0.8057259321212769,
+      "eval_Qnli-dev_cosine_ap": 0.6720663622193426,
+      "eval_Qnli-dev_cosine_f1": 0.6688102893890675,
+      "eval_Qnli-dev_cosine_f1_threshold": 0.7187118530273438,
+      "eval_Qnli-dev_cosine_precision": 0.538860103626943,
+      "eval_Qnli-dev_cosine_recall": 0.8813559322033898,
+      "eval_Qnli-dev_dot_accuracy": 0.646484375,
+      "eval_Qnli-dev_dot_accuracy_threshold": 618.8643798828125,
+      "eval_Qnli-dev_dot_ap": 0.672083506527328,
+      "eval_Qnli-dev_dot_f1": 0.6688102893890675,
+      "eval_Qnli-dev_dot_f1_threshold": 552.0260009765625,
+      "eval_Qnli-dev_dot_precision": 0.538860103626943,
+      "eval_Qnli-dev_dot_recall": 0.8813559322033898,
+      "eval_Qnli-dev_euclidean_accuracy": 0.646484375,
+      "eval_Qnli-dev_euclidean_accuracy_threshold": 17.27533721923828,
+      "eval_Qnli-dev_euclidean_ap": 0.6720591998758361,
+      "eval_Qnli-dev_euclidean_f1": 0.6688102893890675,
+      "eval_Qnli-dev_euclidean_f1_threshold": 20.787063598632812,
+      "eval_Qnli-dev_euclidean_precision": 0.538860103626943,
+      "eval_Qnli-dev_euclidean_recall": 0.8813559322033898,
+      "eval_Qnli-dev_manhattan_accuracy": 0.6484375,
+      "eval_Qnli-dev_manhattan_accuracy_threshold": 386.58905029296875,
+      "eval_Qnli-dev_manhattan_ap": 0.6724653688821339,
+      "eval_Qnli-dev_manhattan_f1": 0.6645569620253164,
+      "eval_Qnli-dev_manhattan_f1_threshold": 462.609130859375,
+      "eval_Qnli-dev_manhattan_precision": 0.5303030303030303,
+      "eval_Qnli-dev_manhattan_recall": 0.8898305084745762,
+      "eval_Qnli-dev_max_accuracy": 0.6484375,
+      "eval_Qnli-dev_max_accuracy_threshold": 618.8643798828125,
+      "eval_Qnli-dev_max_ap": 0.6724653688821339,
+      "eval_Qnli-dev_max_f1": 0.6688102893890675,
+      "eval_Qnli-dev_max_f1_threshold": 552.0260009765625,
+      "eval_Qnli-dev_max_precision": 0.538860103626943,
+      "eval_Qnli-dev_max_recall": 0.8898305084745762,
+      "eval_allNLI-dev_cosine_accuracy": 0.67578125,
+      "eval_allNLI-dev_cosine_accuracy_threshold": 0.9427558183670044,
+      "eval_allNLI-dev_cosine_ap": 0.4368751759846574,
+      "eval_allNLI-dev_cosine_f1": 0.5225225225225225,
+      "eval_allNLI-dev_cosine_f1_threshold": 0.8046966791152954,
+      "eval_allNLI-dev_cosine_precision": 0.3795811518324607,
+      "eval_allNLI-dev_cosine_recall": 0.838150289017341,
+      "eval_allNLI-dev_dot_accuracy": 0.67578125,
+      "eval_allNLI-dev_dot_accuracy_threshold": 724.1080322265625,
+      "eval_allNLI-dev_dot_ap": 0.436842886797982,
+      "eval_allNLI-dev_dot_f1": 0.5225225225225225,
+      "eval_allNLI-dev_dot_f1_threshold": 618.074951171875,
+      "eval_allNLI-dev_dot_precision": 0.3795811518324607,
+      "eval_allNLI-dev_dot_recall": 0.838150289017341,
+      "eval_allNLI-dev_euclidean_accuracy": 0.67578125,
+      "eval_allNLI-dev_euclidean_accuracy_threshold": 9.377331733703613,
+      "eval_allNLI-dev_euclidean_ap": 0.4368602200677977,
+      "eval_allNLI-dev_euclidean_f1": 0.5225225225225225,
+      "eval_allNLI-dev_euclidean_f1_threshold": 17.321048736572266,
+      "eval_allNLI-dev_euclidean_precision": 0.3795811518324607,
+      "eval_allNLI-dev_euclidean_recall": 0.838150289017341,
+      "eval_allNLI-dev_manhattan_accuracy": 0.677734375,
+      "eval_allNLI-dev_manhattan_accuracy_threshold": 223.6764373779297,
+      "eval_allNLI-dev_manhattan_ap": 0.43892484929307635,
+      "eval_allNLI-dev_manhattan_f1": 0.5239852398523985,
+      "eval_allNLI-dev_manhattan_f1_threshold": 372.31396484375,
+      "eval_allNLI-dev_manhattan_precision": 0.38482384823848237,
+      "eval_allNLI-dev_manhattan_recall": 0.8208092485549133,
+      "eval_allNLI-dev_max_accuracy": 0.677734375,
+      "eval_allNLI-dev_max_accuracy_threshold": 724.1080322265625,
+      "eval_allNLI-dev_max_ap": 0.43892484929307635,
+      "eval_allNLI-dev_max_f1": 0.5239852398523985,
+      "eval_allNLI-dev_max_f1_threshold": 618.074951171875,
+      "eval_allNLI-dev_max_precision": 0.38482384823848237,
+      "eval_allNLI-dev_max_recall": 0.838150289017341,
+      "eval_runtime": 1.2344,
+      "eval_samples_per_second": 0.0,
+      "eval_sequential_score": 0.6724653688821339,
+      "eval_steps_per_second": 0.0,
+      "eval_sts-test_pearson_cosine": 0.3774946012125992,
+      "eval_sts-test_pearson_dot": 0.3774648453085433,
+      "eval_sts-test_pearson_euclidean": 0.38652243004790016,
+      "eval_sts-test_pearson_manhattan": 0.3861982631744407,
+      "eval_sts-test_pearson_max": 0.38652243004790016,
+      "eval_sts-test_spearman_cosine": 0.4056589966976888,
+      "eval_sts-test_spearman_dot": 0.40563469676275316,
+      "eval_sts-test_spearman_euclidean": 0.4056589966976888,
+      "eval_sts-test_spearman_manhattan": 0.4059364545183154,
+      "eval_sts-test_spearman_max": 0.4059364545183154,
+      "step": 153
+    },
+    {
+      "epoch": 0.1515748031496063,
+      "grad_norm": 27.144521713256836,
+      "learning_rate": 1.4910536779324058e-07,
+      "loss": 2.2859,
+      "step": 154
+    },
+    {
+      "epoch": 0.15255905511811024,
+      "grad_norm": 16.310321807861328,
+      "learning_rate": 1.5009940357852886e-07,
+      "loss": 1.0833,
+      "step": 155
+    },
+    {
+      "epoch": 0.15354330708661418,
+      "grad_norm": 18.788604736328125,
+      "learning_rate": 1.510934393638171e-07,
+      "loss": 1.6829,
+      "step": 156
+    },
+    {
+      "epoch": 0.1545275590551181,
+      "grad_norm": 23.34528923034668,
+      "learning_rate": 1.5208747514910539e-07,
+      "loss": 2.1464,
+      "step": 157
+    },
+    {
+      "epoch": 0.15551181102362205,
+      "grad_norm": 19.06661033630371,
+      "learning_rate": 1.5308151093439367e-07,
+      "loss": 1.745,
+      "step": 158
+    },
+    {
+      "epoch": 0.15649606299212598,
+      "grad_norm": 23.650297164916992,
+      "learning_rate": 1.5407554671968192e-07,
+      "loss": 1.7319,
+      "step": 159
+    },
+    {
+      "epoch": 0.15748031496062992,
+      "grad_norm": 22.01628303527832,
+      "learning_rate": 1.5506958250497022e-07,
+      "loss": 1.6968,
+      "step": 160
+    },
+    {
+      "epoch": 0.15846456692913385,
+      "grad_norm": 20.905855178833008,
+      "learning_rate": 1.5606361829025848e-07,
+      "loss": 1.7401,
+      "step": 161
+    },
+    {
+      "epoch": 0.1594488188976378,
+      "grad_norm": 22.40980339050293,
+      "learning_rate": 1.5705765407554675e-07,
+      "loss": 1.729,
+      "step": 162
+    },
+    {
+      "epoch": 0.16043307086614172,
+      "grad_norm": 23.574447631835938,
+      "learning_rate": 1.58051689860835e-07,
+      "loss": 2.0782,
+      "step": 163
+    },
+    {
+      "epoch": 0.16141732283464566,
+      "grad_norm": 28.43741798400879,
+      "learning_rate": 1.5904572564612329e-07,
+      "loss": 2.6545,
+      "step": 164
+    },
+    {
+      "epoch": 0.1624015748031496,
+      "grad_norm": 18.536827087402344,
+      "learning_rate": 1.6003976143141154e-07,
+      "loss": 1.4045,
+      "step": 165
+    },
+    {
+      "epoch": 0.16338582677165353,
+      "grad_norm": 17.013965606689453,
+      "learning_rate": 1.6103379721669984e-07,
+      "loss": 1.2937,
+      "step": 166
+    },
+    {
+      "epoch": 0.1643700787401575,
+      "grad_norm": 16.021455764770508,
+      "learning_rate": 1.620278330019881e-07,
+      "loss": 1.1171,
+      "step": 167
+    },
+    {
+      "epoch": 0.16535433070866143,
+      "grad_norm": 20.248655319213867,
+      "learning_rate": 1.6302186878727637e-07,
+      "loss": 1.3537,
+      "step": 168
+    },
+    {
+      "epoch": 0.16633858267716536,
+      "grad_norm": 21.731477737426758,
+      "learning_rate": 1.6401590457256465e-07,
+      "loss": 1.7028,
+      "step": 169
+    },
+    {
+      "epoch": 0.1673228346456693,
+      "grad_norm": 20.372398376464844,
+      "learning_rate": 1.650099403578529e-07,
+      "loss": 1.4143,
+      "step": 170
+    },
+    {
+      "epoch": 0.16830708661417323,
+      "grad_norm": 21.89658546447754,
+      "learning_rate": 1.6600397614314118e-07,
+      "loss": 1.8648,
+      "step": 171
+    },
+    {
+      "epoch": 0.16929133858267717,
+      "grad_norm": 24.402984619140625,
+      "learning_rate": 1.6699801192842944e-07,
+      "loss": 1.6768,
+      "step": 172
+    },
+    {
+      "epoch": 0.1702755905511811,
+      "grad_norm": 28.650039672851562,
+      "learning_rate": 1.6799204771371774e-07,
+      "loss": 1.9528,
+      "step": 173
+    },
+    {
+      "epoch": 0.17125984251968504,
+      "grad_norm": 18.586729049682617,
+      "learning_rate": 1.68986083499006e-07,
+      "loss": 1.1718,
+      "step": 174
+    },
+    {
+      "epoch": 0.17224409448818898,
+      "grad_norm": 22.66750717163086,
+      "learning_rate": 1.6998011928429427e-07,
+      "loss": 1.8176,
+      "step": 175
+    },
+    {
+      "epoch": 0.1732283464566929,
+      "grad_norm": 15.157270431518555,
+      "learning_rate": 1.7097415506958253e-07,
+      "loss": 0.8439,
+      "step": 176
+    },
+    {
+      "epoch": 0.17421259842519685,
+      "grad_norm": 23.08325958251953,
+      "learning_rate": 1.719681908548708e-07,
+      "loss": 1.5092,
+      "step": 177
+    },
+    {
+      "epoch": 0.17519685039370078,
+      "grad_norm": 19.215696334838867,
+      "learning_rate": 1.7296222664015906e-07,
+      "loss": 1.1947,
+      "step": 178
+    },
+    {
+      "epoch": 0.17618110236220472,
+      "grad_norm": 23.268930435180664,
+      "learning_rate": 1.7395626242544734e-07,
+      "loss": 1.6395,
+      "step": 179
+    },
+    {
+      "epoch": 0.17716535433070865,
+      "grad_norm": 24.91169548034668,
+      "learning_rate": 1.7495029821073564e-07,
+      "loss": 1.4394,
+      "step": 180
+    },
+    {
+      "epoch": 0.1781496062992126,
+      "grad_norm": 26.92424201965332,
+      "learning_rate": 1.759443339960239e-07,
+      "loss": 1.7548,
+      "step": 181
+    },
+    {
+      "epoch": 0.17913385826771652,
+      "grad_norm": 22.831315994262695,
+      "learning_rate": 1.7693836978131217e-07,
+      "loss": 1.1181,
+      "step": 182
+    },
+    {
+      "epoch": 0.18011811023622049,
+      "grad_norm": 18.39922332763672,
+      "learning_rate": 1.7793240556660042e-07,
+      "loss": 1.0271,
+      "step": 183
+    },
+    {
+      "epoch": 0.18110236220472442,
+      "grad_norm": 30.0064697265625,
+      "learning_rate": 1.789264413518887e-07,
+      "loss": 2.3108,
+      "step": 184
+    },
+    {
+      "epoch": 0.18208661417322836,
+      "grad_norm": 25.531293869018555,
+      "learning_rate": 1.7992047713717695e-07,
+      "loss": 2.1242,
+      "step": 185
+    },
+    {
+      "epoch": 0.1830708661417323,
+      "grad_norm": 25.3813533782959,
+      "learning_rate": 1.8091451292246523e-07,
+      "loss": 1.9822,
+      "step": 186
+    },
+    {
+      "epoch": 0.18405511811023623,
+      "grad_norm": 22.24681282043457,
+      "learning_rate": 1.819085487077535e-07,
+      "loss": 2.3605,
+      "step": 187
+    },
+    {
+      "epoch": 0.18503937007874016,
+      "grad_norm": 19.291316986083984,
+      "learning_rate": 1.829025844930418e-07,
+      "loss": 1.5251,
+      "step": 188
+    },
+    {
+      "epoch": 0.1860236220472441,
+      "grad_norm": 18.280820846557617,
+      "learning_rate": 1.8389662027833004e-07,
+      "loss": 1.2351,
+      "step": 189
+    },
+    {
+      "epoch": 0.18700787401574803,
+      "grad_norm": 18.341115951538086,
+      "learning_rate": 1.8489065606361832e-07,
+      "loss": 1.5859,
+      "step": 190
+    },
+    {
+      "epoch": 0.18799212598425197,
+      "grad_norm": 21.981475830078125,
+      "learning_rate": 1.8588469184890657e-07,
+      "loss": 1.8056,
+      "step": 191
+    },
+    {
+      "epoch": 0.1889763779527559,
+      "grad_norm": 23.482725143432617,
+      "learning_rate": 1.8687872763419485e-07,
+      "loss": 1.349,
+      "step": 192
+    },
+    {
+      "epoch": 0.18996062992125984,
+      "grad_norm": 15.451715469360352,
+      "learning_rate": 1.8787276341948313e-07,
+      "loss": 0.893,
+      "step": 193
+    },
+    {
+      "epoch": 0.19094488188976377,
+      "grad_norm": 25.24738121032715,
+      "learning_rate": 1.888667992047714e-07,
+      "loss": 1.5122,
+      "step": 194
+    },
+    {
+      "epoch": 0.1919291338582677,
+      "grad_norm": 22.348981857299805,
+      "learning_rate": 1.898608349900597e-07,
+      "loss": 1.3875,
+      "step": 195
+    },
+    {
+      "epoch": 0.19291338582677164,
+      "grad_norm": 22.770132064819336,
+      "learning_rate": 1.9085487077534794e-07,
+      "loss": 1.29,
+      "step": 196
+    },
+    {
+      "epoch": 0.19389763779527558,
+      "grad_norm": 31.219194412231445,
+      "learning_rate": 1.9184890656063622e-07,
+      "loss": 2.2931,
+      "step": 197
+    },
+    {
+      "epoch": 0.19488188976377951,
+      "grad_norm": 24.657859802246094,
+      "learning_rate": 1.9284294234592447e-07,
+      "loss": 1.2663,
+      "step": 198
+    },
+    {
+      "epoch": 0.19586614173228348,
+      "grad_norm": 26.825185775756836,
+      "learning_rate": 1.9383697813121275e-07,
+      "loss": 1.9712,
+      "step": 199
+    },
+    {
+      "epoch": 0.1968503937007874,
+      "grad_norm": 30.97873306274414,
+      "learning_rate": 1.94831013916501e-07,
+      "loss": 2.3307,
+      "step": 200
+    },
+    {
+      "epoch": 0.19783464566929135,
+      "grad_norm": 24.922365188598633,
+      "learning_rate": 1.958250497017893e-07,
+      "loss": 1.6544,
+      "step": 201
+    },
+    {
+      "epoch": 0.19881889763779528,
+      "grad_norm": 22.842063903808594,
+      "learning_rate": 1.9681908548707756e-07,
+      "loss": 1.638,
+      "step": 202
+    },
+    {
+      "epoch": 0.19980314960629922,
+      "grad_norm": 17.376962661743164,
+      "learning_rate": 1.9781312127236584e-07,
+      "loss": 1.3412,
+      "step": 203
+    },
+    {
+      "epoch": 0.20078740157480315,
+      "grad_norm": 17.828535079956055,
+      "learning_rate": 1.9880715705765412e-07,
+      "loss": 1.4454,
+      "step": 204
+    },
+    {
+      "epoch": 0.2017716535433071,
+      "grad_norm": 18.73072624206543,
+      "learning_rate": 1.9980119284294237e-07,
+      "loss": 1.5437,
+      "step": 205
+    },
+    {
+      "epoch": 0.20275590551181102,
+      "grad_norm": 22.05230140686035,
+      "learning_rate": 2.0079522862823065e-07,
+      "loss": 1.4921,
+      "step": 206
+    },
+    {
+      "epoch": 0.20374015748031496,
+      "grad_norm": 18.090892791748047,
+      "learning_rate": 2.017892644135189e-07,
+      "loss": 1.4298,
+      "step": 207
+    },
+    {
+      "epoch": 0.2047244094488189,
+      "grad_norm": 22.94427490234375,
+      "learning_rate": 2.027833001988072e-07,
+      "loss": 1.6174,
+      "step": 208
+    },
+    {
+      "epoch": 0.20570866141732283,
+      "grad_norm": 20.903331756591797,
+      "learning_rate": 2.0377733598409546e-07,
+      "loss": 1.4137,
+      "step": 209
+    },
+    {
+      "epoch": 0.20669291338582677,
+      "grad_norm": 22.67809295654297,
+      "learning_rate": 2.0477137176938374e-07,
+      "loss": 1.5652,
+      "step": 210
+    },
+    {
+      "epoch": 0.2076771653543307,
+      "grad_norm": 16.824064254760742,
+      "learning_rate": 2.05765407554672e-07,
+      "loss": 1.1631,
+      "step": 211
+    },
+    {
+      "epoch": 0.20866141732283464,
+      "grad_norm": 21.46413230895996,
+      "learning_rate": 2.0675944333996027e-07,
+      "loss": 1.2351,
+      "step": 212
+    },
+    {
+      "epoch": 0.20964566929133857,
+      "grad_norm": 22.461610794067383,
+      "learning_rate": 2.0775347912524852e-07,
+      "loss": 1.7537,
+      "step": 213
+    },
+    {
+      "epoch": 0.2106299212598425,
+      "grad_norm": 22.150938034057617,
+      "learning_rate": 2.087475149105368e-07,
+      "loss": 1.3186,
+      "step": 214
+    },
+    {
+      "epoch": 0.21161417322834647,
+      "grad_norm": 22.82622718811035,
+      "learning_rate": 2.097415506958251e-07,
+      "loss": 1.2258,
+      "step": 215
+    },
+    {
+      "epoch": 0.2125984251968504,
+      "grad_norm": 13.514169692993164,
+      "learning_rate": 2.1073558648111336e-07,
+      "loss": 0.7695,
+      "step": 216
+    },
+    {
+      "epoch": 0.21358267716535434,
+      "grad_norm": 22.254596710205078,
+      "learning_rate": 2.1172962226640164e-07,
+      "loss": 1.2775,
+      "step": 217
+    },
+    {
+      "epoch": 0.21456692913385828,
+      "grad_norm": 23.31028938293457,
+      "learning_rate": 2.127236580516899e-07,
+      "loss": 1.6795,
+      "step": 218
+    },
+    {
+      "epoch": 0.2155511811023622,
+      "grad_norm": 24.057025909423828,
+      "learning_rate": 2.1371769383697817e-07,
+      "loss": 1.2862,
+      "step": 219
+    },
+    {
+      "epoch": 0.21653543307086615,
+      "grad_norm": 17.363821029663086,
+      "learning_rate": 2.1471172962226642e-07,
+      "loss": 1.1723,
+      "step": 220
+    },
+    {
+      "epoch": 0.21751968503937008,
+      "grad_norm": 22.69175148010254,
+      "learning_rate": 2.1570576540755473e-07,
+      "loss": 1.3322,
+      "step": 221
+    },
+    {
+      "epoch": 0.21850393700787402,
+      "grad_norm": 24.083465576171875,
+      "learning_rate": 2.1669980119284298e-07,
+      "loss": 1.7564,
+      "step": 222
+    },
+    {
+      "epoch": 0.21948818897637795,
+      "grad_norm": 17.775938034057617,
+      "learning_rate": 2.1769383697813126e-07,
+      "loss": 1.1071,
+      "step": 223
+    },
+    {
+      "epoch": 0.2204724409448819,
+      "grad_norm": 18.539440155029297,
+      "learning_rate": 2.186878727634195e-07,
+      "loss": 1.2011,
+      "step": 224
+    },
+    {
+      "epoch": 0.22145669291338582,
+      "grad_norm": 18.234455108642578,
+      "learning_rate": 2.196819085487078e-07,
+      "loss": 1.2303,
+      "step": 225
+    },
+    {
+      "epoch": 0.22244094488188976,
+      "grad_norm": 20.291522979736328,
+      "learning_rate": 2.2067594433399604e-07,
+      "loss": 1.212,
+      "step": 226
+    },
+    {
+      "epoch": 0.2234251968503937,
+      "grad_norm": 15.60973072052002,
+      "learning_rate": 2.2166998011928432e-07,
+      "loss": 1.0117,
+      "step": 227
+    },
+    {
+      "epoch": 0.22440944881889763,
+      "grad_norm": 20.482995986938477,
+      "learning_rate": 2.2266401590457263e-07,
+      "loss": 1.1907,
+      "step": 228
+    },
+    {
+      "epoch": 0.22539370078740156,
+      "grad_norm": 24.126340866088867,
+      "learning_rate": 2.2365805168986088e-07,
+      "loss": 2.1293,
+      "step": 229
+    },
+    {
+      "epoch": 0.2263779527559055,
+      "grad_norm": 21.279150009155273,
+      "learning_rate": 2.2465208747514916e-07,
+      "loss": 1.3063,
+      "step": 230
+    },
+    {
+      "epoch": 0.22736220472440946,
+      "grad_norm": 20.62582778930664,
+      "learning_rate": 2.256461232604374e-07,
+      "loss": 1.2841,
+      "step": 231
+    },
+    {
+      "epoch": 0.2283464566929134,
+      "grad_norm": 20.63606834411621,
+      "learning_rate": 2.266401590457257e-07,
+      "loss": 1.3778,
+      "step": 232
+    },
+    {
+      "epoch": 0.22933070866141733,
+      "grad_norm": 18.099950790405273,
+      "learning_rate": 2.2763419483101394e-07,
+      "loss": 1.2242,
+      "step": 233
+    },
+    {
+      "epoch": 0.23031496062992127,
+      "grad_norm": 18.96858024597168,
+      "learning_rate": 2.2862823061630222e-07,
+      "loss": 0.9227,
+      "step": 234
+    },
+    {
+      "epoch": 0.2312992125984252,
+      "grad_norm": 22.201169967651367,
+      "learning_rate": 2.296222664015905e-07,
+      "loss": 1.2221,
+      "step": 235
+    },
+    {
+      "epoch": 0.23228346456692914,
+      "grad_norm": 26.657480239868164,
+      "learning_rate": 2.3061630218687878e-07,
+      "loss": 2.1041,
+      "step": 236
+    },
+    {
+      "epoch": 0.23326771653543307,
+      "grad_norm": 25.558757781982422,
+      "learning_rate": 2.3161033797216703e-07,
+      "loss": 1.3341,
+      "step": 237
+    },
+    {
+      "epoch": 0.234251968503937,
+      "grad_norm": 22.0543212890625,
+      "learning_rate": 2.326043737574553e-07,
+      "loss": 1.0876,
+      "step": 238
+    },
+    {
+      "epoch": 0.23523622047244094,
+      "grad_norm": 22.258333206176758,
+      "learning_rate": 2.335984095427436e-07,
+      "loss": 1.3328,
+      "step": 239
+    },
+    {
+      "epoch": 0.23622047244094488,
+      "grad_norm": 21.519908905029297,
+      "learning_rate": 2.3459244532803184e-07,
+      "loss": 1.2958,
+      "step": 240
+    },
+    {
+      "epoch": 0.2372047244094488,
+      "grad_norm": 23.345388412475586,
+      "learning_rate": 2.3558648111332012e-07,
+      "loss": 1.1522,
+      "step": 241
+    },
+    {
+      "epoch": 0.23818897637795275,
+      "grad_norm": 25.306140899658203,
+      "learning_rate": 2.365805168986084e-07,
+      "loss": 1.7942,
+      "step": 242
+    },
+    {
+      "epoch": 0.23917322834645668,
+      "grad_norm": 21.840364456176758,
+      "learning_rate": 2.3757455268389668e-07,
+      "loss": 1.1325,
+      "step": 243
+    },
+    {
+      "epoch": 0.24015748031496062,
+      "grad_norm": 26.5495662689209,
+      "learning_rate": 2.385685884691849e-07,
+      "loss": 1.6466,
+      "step": 244
+    },
+    {
+      "epoch": 0.24114173228346455,
+      "grad_norm": 21.928937911987305,
+      "learning_rate": 2.395626242544732e-07,
+      "loss": 1.4608,
+      "step": 245
+    },
+    {
+      "epoch": 0.2421259842519685,
+      "grad_norm": 16.23383903503418,
+      "learning_rate": 2.4055666003976146e-07,
+      "loss": 0.6375,
+      "step": 246
+    },
+    {
+      "epoch": 0.24311023622047245,
+      "grad_norm": 26.922739028930664,
+      "learning_rate": 2.4155069582504976e-07,
+      "loss": 2.0177,
+      "step": 247
+    },
+    {
+      "epoch": 0.2440944881889764,
+      "grad_norm": 23.482742309570312,
+      "learning_rate": 2.42544731610338e-07,
+      "loss": 1.2069,
+      "step": 248
+    },
+    {
+      "epoch": 0.24507874015748032,
+      "grad_norm": 14.740228652954102,
+      "learning_rate": 2.4353876739562627e-07,
+      "loss": 0.7639,
+      "step": 249
+    },
+    {
+      "epoch": 0.24606299212598426,
+      "grad_norm": 20.238948822021484,
+      "learning_rate": 2.445328031809146e-07,
+      "loss": 1.3465,
+      "step": 250
+    },
+    {
+      "epoch": 0.2470472440944882,
+      "grad_norm": 16.91303253173828,
+      "learning_rate": 2.4552683896620283e-07,
+      "loss": 1.064,
+      "step": 251
+    },
+    {
+      "epoch": 0.24803149606299213,
+      "grad_norm": 22.081308364868164,
+      "learning_rate": 2.4652087475149113e-07,
+      "loss": 1.3757,
+      "step": 252
+    },
+    {
+      "epoch": 0.24901574803149606,
+      "grad_norm": 21.756267547607422,
+      "learning_rate": 2.475149105367794e-07,
+      "loss": 1.612,
+      "step": 253
+    },
+    {
+      "epoch": 0.25,
+      "grad_norm": 16.87566566467285,
+      "learning_rate": 2.4850894632206764e-07,
+      "loss": 0.7917,
+      "step": 254
+    },
+    {
+      "epoch": 0.25098425196850394,
+      "grad_norm": 22.13374900817871,
+      "learning_rate": 2.495029821073559e-07,
+      "loss": 1.5515,
+      "step": 255
+    },
+    {
+      "epoch": 0.25196850393700787,
+      "grad_norm": 18.96138572692871,
+      "learning_rate": 2.5049701789264414e-07,
+      "loss": 0.799,
+      "step": 256
+    },
+    {
+      "epoch": 0.2529527559055118,
+      "grad_norm": 17.10991859436035,
+      "learning_rate": 2.5149105367793245e-07,
+      "loss": 0.9882,
+      "step": 257
+    },
+    {
+      "epoch": 0.25393700787401574,
+      "grad_norm": 15.347769737243652,
+      "learning_rate": 2.524850894632207e-07,
+      "loss": 1.1814,
+      "step": 258
+    },
+    {
+      "epoch": 0.2549212598425197,
+      "grad_norm": 11.857462882995605,
+      "learning_rate": 2.53479125248509e-07,
+      "loss": 0.6394,
+      "step": 259
+    },
+    {
+      "epoch": 0.2559055118110236,
+      "grad_norm": 20.00450325012207,
+      "learning_rate": 2.5447316103379726e-07,
+      "loss": 1.4756,
+      "step": 260
+    },
+    {
+      "epoch": 0.25688976377952755,
+      "grad_norm": 12.062335968017578,
+      "learning_rate": 2.554671968190855e-07,
+      "loss": 0.5338,
+      "step": 261
+    },
+    {
+      "epoch": 0.2578740157480315,
+      "grad_norm": 16.912168502807617,
+      "learning_rate": 2.564612326043738e-07,
+      "loss": 0.9779,
+      "step": 262
+    },
+    {
+      "epoch": 0.2588582677165354,
+      "grad_norm": 22.713825225830078,
+      "learning_rate": 2.5745526838966207e-07,
+      "loss": 1.5307,
+      "step": 263
+    },
+    {
+      "epoch": 0.25984251968503935,
+      "grad_norm": 22.88834571838379,
+      "learning_rate": 2.5844930417495037e-07,
+      "loss": 1.1213,
+      "step": 264
+    },
+    {
+      "epoch": 0.2608267716535433,
+      "grad_norm": 18.133310317993164,
+      "learning_rate": 2.5944333996023857e-07,
+      "loss": 0.9482,
+      "step": 265
+    },
+    {
+      "epoch": 0.2618110236220472,
+      "grad_norm": 19.092018127441406,
+      "learning_rate": 2.604373757455269e-07,
+      "loss": 0.9599,
+      "step": 266
+    },
+    {
+      "epoch": 0.26279527559055116,
+      "grad_norm": 25.283647537231445,
+      "learning_rate": 2.614314115308152e-07,
+      "loss": 1.4455,
+      "step": 267
+    },
+    {
+      "epoch": 0.2637795275590551,
+      "grad_norm": 28.519742965698242,
+      "learning_rate": 2.6242544731610343e-07,
+      "loss": 1.6496,
+      "step": 268
+    },
+    {
+      "epoch": 0.26476377952755903,
+      "grad_norm": 15.56734561920166,
+      "learning_rate": 2.634194831013917e-07,
+      "loss": 0.7402,
+      "step": 269
+    },
+    {
+      "epoch": 0.265748031496063,
+      "grad_norm": 16.450769424438477,
+      "learning_rate": 2.6441351888667994e-07,
+      "loss": 0.7835,
+      "step": 270
+    },
+    {
+      "epoch": 0.26673228346456695,
+      "grad_norm": 18.447402954101562,
+      "learning_rate": 2.6540755467196824e-07,
+      "loss": 0.7821,
+      "step": 271
+    },
+    {
+      "epoch": 0.2677165354330709,
+      "grad_norm": 22.634441375732422,
+      "learning_rate": 2.664015904572565e-07,
+      "loss": 1.5422,
+      "step": 272
+    },
+    {
+      "epoch": 0.2687007874015748,
+      "grad_norm": 20.091428756713867,
+      "learning_rate": 2.6739562624254475e-07,
+      "loss": 1.0995,
+      "step": 273
+    },
+    {
+      "epoch": 0.26968503937007876,
+      "grad_norm": 22.89579200744629,
+      "learning_rate": 2.6838966202783305e-07,
+      "loss": 1.378,
+      "step": 274
+    },
+    {
+      "epoch": 0.2706692913385827,
+      "grad_norm": 25.36764144897461,
+      "learning_rate": 2.693836978131213e-07,
+      "loss": 1.3562,
+      "step": 275
+    },
+    {
+      "epoch": 0.27165354330708663,
+      "grad_norm": 15.717765808105469,
+      "learning_rate": 2.703777335984096e-07,
+      "loss": 0.7376,
+      "step": 276
+    },
+    {
+      "epoch": 0.27263779527559057,
+      "grad_norm": 21.739999771118164,
+      "learning_rate": 2.7137176938369786e-07,
+      "loss": 1.1678,
+      "step": 277
+    },
+    {
+      "epoch": 0.2736220472440945,
+      "grad_norm": 21.12672233581543,
+      "learning_rate": 2.723658051689861e-07,
+      "loss": 1.2989,
+      "step": 278
+    },
+    {
+      "epoch": 0.27460629921259844,
+      "grad_norm": 22.614158630371094,
+      "learning_rate": 2.7335984095427437e-07,
+      "loss": 1.9559,
+      "step": 279
+    },
+    {
+      "epoch": 0.2755905511811024,
+      "grad_norm": 18.742107391357422,
+      "learning_rate": 2.743538767395627e-07,
+      "loss": 1.1237,
+      "step": 280
+    },
+    {
+      "epoch": 0.2765748031496063,
+      "grad_norm": 17.969554901123047,
+      "learning_rate": 2.75347912524851e-07,
+      "loss": 0.952,
+      "step": 281
+    },
+    {
+      "epoch": 0.27755905511811024,
+      "grad_norm": 24.01414680480957,
+      "learning_rate": 2.763419483101392e-07,
+      "loss": 1.6629,
+      "step": 282
+    },
+    {
+      "epoch": 0.2785433070866142,
+      "grad_norm": 21.362796783447266,
+      "learning_rate": 2.773359840954275e-07,
+      "loss": 1.871,
+      "step": 283
+    },
+    {
+      "epoch": 0.2795275590551181,
+      "grad_norm": 19.05644989013672,
+      "learning_rate": 2.7833001988071574e-07,
+      "loss": 1.5946,
+      "step": 284
+    },
+    {
+      "epoch": 0.28051181102362205,
+      "grad_norm": 18.40645408630371,
+      "learning_rate": 2.7932405566600404e-07,
+      "loss": 1.4456,
+      "step": 285
+    },
+    {
+      "epoch": 0.281496062992126,
+      "grad_norm": 18.53467559814453,
+      "learning_rate": 2.803180914512923e-07,
+      "loss": 1.4085,
+      "step": 286
+    },
+    {
+      "epoch": 0.2824803149606299,
+      "grad_norm": 18.1446533203125,
+      "learning_rate": 2.8131212723658055e-07,
+      "loss": 1.1394,
+      "step": 287
+    },
+    {
+      "epoch": 0.28346456692913385,
+      "grad_norm": 15.109747886657715,
+      "learning_rate": 2.8230616302186885e-07,
+      "loss": 1.0315,
+      "step": 288
+    },
+    {
+      "epoch": 0.2844488188976378,
+      "grad_norm": 22.63469696044922,
+      "learning_rate": 2.833001988071571e-07,
+      "loss": 1.488,
+      "step": 289
+    },
+    {
+      "epoch": 0.2854330708661417,
+      "grad_norm": 18.841909408569336,
+      "learning_rate": 2.842942345924454e-07,
+      "loss": 1.4006,
+      "step": 290
+    },
+    {
+      "epoch": 0.28641732283464566,
+      "grad_norm": 20.073745727539062,
+      "learning_rate": 2.852882703777336e-07,
+      "loss": 0.9237,
+      "step": 291
+    },
+    {
+      "epoch": 0.2874015748031496,
+      "grad_norm": 18.586259841918945,
+      "learning_rate": 2.862823061630219e-07,
+      "loss": 1.163,
+      "step": 292
+    },
+    {
+      "epoch": 0.28838582677165353,
+      "grad_norm": 23.599292755126953,
+      "learning_rate": 2.8727634194831017e-07,
+      "loss": 1.7037,
+      "step": 293
+    },
+    {
+      "epoch": 0.28937007874015747,
+      "grad_norm": 16.384347915649414,
+      "learning_rate": 2.8827037773359847e-07,
+      "loss": 0.8715,
+      "step": 294
+    },
+    {
+      "epoch": 0.2903543307086614,
+      "grad_norm": 20.26093101501465,
+      "learning_rate": 2.892644135188867e-07,
+      "loss": 1.2101,
+      "step": 295
+    },
+    {
+      "epoch": 0.29133858267716534,
+      "grad_norm": 19.598831176757812,
+      "learning_rate": 2.90258449304175e-07,
+      "loss": 1.1179,
+      "step": 296
+    },
+    {
+      "epoch": 0.29232283464566927,
+      "grad_norm": 22.65572738647461,
+      "learning_rate": 2.912524850894633e-07,
+      "loss": 1.3986,
+      "step": 297
+    },
+    {
+      "epoch": 0.2933070866141732,
+      "grad_norm": 22.658931732177734,
+      "learning_rate": 2.9224652087475153e-07,
+      "loss": 1.7068,
+      "step": 298
+    },
+    {
+      "epoch": 0.29429133858267714,
+      "grad_norm": 16.94671058654785,
+      "learning_rate": 2.9324055666003984e-07,
+      "loss": 0.8695,
+      "step": 299
+    },
+    {
+      "epoch": 0.2952755905511811,
+      "grad_norm": 20.13766860961914,
+      "learning_rate": 2.9423459244532804e-07,
+      "loss": 1.3778,
+      "step": 300
+    },
+    {
+      "epoch": 0.296259842519685,
+      "grad_norm": 16.939062118530273,
+      "learning_rate": 2.9522862823061634e-07,
+      "loss": 1.2834,
+      "step": 301
+    },
+    {
+      "epoch": 0.297244094488189,
+      "grad_norm": 14.732866287231445,
+      "learning_rate": 2.9622266401590465e-07,
+      "loss": 0.8123,
+      "step": 302
+    },
+    {
+      "epoch": 0.29822834645669294,
+      "grad_norm": 23.4341983795166,
+      "learning_rate": 2.972166998011929e-07,
+      "loss": 1.6521,
+      "step": 303
+    },
+    {
+      "epoch": 0.2992125984251969,
+      "grad_norm": 15.455023765563965,
+      "learning_rate": 2.9821073558648115e-07,
+      "loss": 1.1064,
+      "step": 304
+    },
+    {
+      "epoch": 0.3001968503937008,
+      "grad_norm": 15.683412551879883,
+      "learning_rate": 2.992047713717694e-07,
+      "loss": 0.9578,
+      "step": 305
+    }
+  ],
+  "logging_steps": 1,
+  "max_steps": 3048,
+  "num_input_tokens_seen": 0,
+  "num_train_epochs": 3,
+  "save_steps": 305,
+  "stateful_callbacks": {
+    "TrainerControl": {
+      "args": {
+        "should_epoch_stop": false,
+        "should_evaluate": false,
+        "should_log": false,
+        "should_save": true,
+        "should_training_stop": false
+      },
+      "attributes": {}
+    }
+  },
+  "total_flos": 0.0,
+  "train_batch_size": 32,
+  "trial_name": null,
+  "trial_params": null
+}

checkpoint-305/training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e4e5a2c389c69a314b44e2abcb7834dfc6e25823a2070d8ca3efd3fc97499c9b
+size 5688