bobox/DeBERTa3-s-CustomPoolin-v3-step1

SentenceTransformer based on microsoft/deberta-v3-small

This is a sentence-transformers model finetuned from 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
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 tokens
• Similarity Function: Cosine Similarity
• Language: en

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DebertaV2Model 
  (1): AdvancedWeightedPooling(
    (linear_cls): Linear(in_features=768, out_features=768, bias=True)
    (linear_mean): Linear(in_features=768, out_features=768, bias=True)
    (mha): MultiheadAttention(
      (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
    )
    (layernorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
    (layernorm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
    (layernorm_cls): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
    (layernorm_mean): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
  )
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("bobox/DeBERTa3-s-CustomPoolin-v3-step1")
# Run inference
sentences = [
    'What was the name of eleven rulers of the 19th and 20th Egyptian dynasties?',
    'List of Rulers of Ancient Egypt and Nubia | Lists of Rulers | Heilbrunn Timeline of Art History | The Metropolitan Museum of Art The Metropolitan Museum of Art List of Rulers of Ancient Egypt and Nubia See works of art 30.8.234 52.127.4 Our knowledge of the succession of Egyptian kings is based on kinglists kept by the ancient Egyptians themselves. The most famous are the Palermo Stone, which covers the period from the earliest dynasties to the middle of Dynasty 5; the Abydos Kinglist, which Seti I had carved on his temple at Abydos; and the Turin Canon, a papyrus that covers the period from the earliest dynasties to the reign of Ramesses II. All are incomplete or fragmentary. We also rely on the History of Egypt written by Manetho in the third century B.C. A priest in the temple at Heliopolis, Manetho had access to many original sources and it was he who divided the kings into the thirty dynasties we use today. It is to this structure of dynasties and listed kings that we now attempt to link an absolute chronology of dates in terms of our own calendrical system. The process is made difficult by the fragmentary condition of the kinglists and by differences in the calendrical years used at various times. Some astronomical observations from the ancient Egyptians have survived, allowing us to calculate absolute dates within a margin of error. Synchronisms with the other civilizations of the ancient world are also of limited use.',
    'What is the "Jack Sprat" nursery rhyme? | Reference.com What is the "Jack Sprat" nursery rhyme? A: Quick Answer "Jack Sprat" is a traditional English nursery rhyme whose main verse says, "Jack Sprat could eat no fat. His wife could eat no lean. And so between them both, you see, they licked the platter clean." Though it was likely sung by children long before, "Jack Sprat" was first published around 1765 in the compilation "Mother Goose\'s Melody." Full Answer According to Rhymes.org, a U.K. website devoted to nursery rhyme lyrics and origins, the "Jack Sprat" nursery rhyme has its origins in British history. In one interpretation, Jack Sprat was King Charles I, who ruled England in the early part of the 17th century, and his wife was Queen Henrietta Maria. Parliament refused to finance the king\'s war with Spain, which made him lean. However, the queen fattened the coffers by levying an illegal war tax. In an alternative version, the "Jack Sprat" nursery rhyme is linked to King Richard and his brother John of the Robin Hood legend. Jack Sprat was King John, the usurper who tried to take over the crown when King Richard went off to fight in the Crusades in the 12th century. When King Richard was captured, John had to raise a ransom to rescue him, leaving the country lean. The wife was Joan, daughter of the Earl of Gloucester, the greedy wife of King John. However, after King Richard died and John became king, he had his marriage with Joan annulled.',
]
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]

Evaluation

Metrics

Semantic Similarity

• Dataset: sts-test
• Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7674
spearman_cosine	0.7776
pearson_manhattan	0.7824
spearman_manhattan	0.7721
pearson_euclidean	0.7883
spearman_euclidean	0.7775
pearson_dot	0.7669
spearman_dot	0.7763
pearson_max	0.7883
spearman_max	0.7776

Binary Classification

• Dataset: allNLI-dev
• Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.709
cosine_accuracy_threshold	0.8715
cosine_f1	0.5913
cosine_f1_threshold	0.7769
cosine_precision	0.4739
cosine_recall	0.7861
cosine_ap	0.5644
dot_accuracy	0.7109
dot_accuracy_threshold	674.426
dot_f1	0.5913
dot_f1_threshold	603.4353
dot_precision	0.4739
dot_recall	0.7861
dot_ap	0.5665
manhattan_accuracy	0.7109
manhattan_accuracy_threshold	294.4728
manhattan_f1	0.5935
manhattan_f1_threshold	401.1483
manhattan_precision	0.4726
manhattan_recall	0.7977
manhattan_ap	0.5643
euclidean_accuracy	0.7109
euclidean_accuracy_threshold	14.5655
euclidean_f1	0.5913
euclidean_f1_threshold	18.6041
euclidean_precision	0.4739
euclidean_recall	0.7861
euclidean_ap	0.5646
max_accuracy	0.7109
max_accuracy_threshold	674.426
max_f1	0.5935
max_f1_threshold	603.4353
max_precision	0.4739
max_recall	0.7977
max_ap	0.5665

Binary Classification

• Dataset: Qnli-dev
• Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.6797
cosine_accuracy_threshold	0.7727
cosine_f1	0.6926
cosine_f1_threshold	0.7318
cosine_precision	0.5758
cosine_recall	0.8686
cosine_ap	0.7303
dot_accuracy	0.6758
dot_accuracy_threshold	598.042
dot_f1	0.6913
dot_f1_threshold	565.4718
dot_precision	0.5722
dot_recall	0.8729
dot_ap	0.73
manhattan_accuracy	0.6797
manhattan_accuracy_threshold	404.8309
manhattan_f1	0.6933
manhattan_f1_threshold	444.9922
manhattan_precision	0.5714
manhattan_recall	0.8814
manhattan_ap	0.7369
euclidean_accuracy	0.6797
euclidean_accuracy_threshold	18.7907
euclidean_f1	0.6934
euclidean_f1_threshold	19.3513
euclidean_precision	0.609
euclidean_recall	0.8051
euclidean_ap	0.7307
max_accuracy	0.6797
max_accuracy_threshold	598.042
max_f1	0.6934
max_f1_threshold	565.4718
max_precision	0.609
max_recall	0.8814
max_ap	0.7369

Training Details

Evaluation Dataset

vitaminc-pairs

• Dataset: vitaminc-pairs at be6febb
• Size: 128 evaluation samples
• Columns: claim and evidence
• Approximate statistics based on the first 128 samples:

  	claim	evidence
  type	string	string
  details	min: 9 tokens mean: 21.42 tokens max: 41 tokens	min: 11 tokens mean: 35.55 tokens max: 79 tokens

• Samples:

  claim	evidence
  Dragon Con had over 5000 guests .	Among the more than 6000 guests and musical performers at the 2009 convention were such notables as Patrick Stewart , William Shatner , Leonard Nimoy , Terry Gilliam , Bruce Boxleitner , James Marsters , and Mary McDonnell .
  COVID-19 has reached more than 185 countries .	As of , more than cases of COVID-19 have been reported in more than 190 countries and 200 territories , resulting in more than deaths .
  In March , Italy had 3.6x times more cases of coronavirus than China .	As of 12 March , among nations with at least one million citizens , Italy has the world 's highest per capita rate of positive coronavirus cases at 206.1 cases per million people ( 3.6x times the rate of China ) and is the country with the second-highest number of positive cases as well as of deaths in the world , after China .

• Loss: CachedGISTEmbedLoss with these parameters:

  {'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: 100
• per_device_eval_batch_size: 256
• gradient_accumulation_steps: 2
• lr_scheduler_type: cosine_with_min_lr
• lr_scheduler_kwargs: {'num_cycles': 0.5, 'min_lr': 1.6666666666666667e-05}
• warmup_ratio: 0.33
• save_safetensors: False
• fp16: True
• push_to_hub: True
• hub_model_id: bobox/DeBERTa3-s-CustomPoolin-v3-step1-checkpoints-tmp
• hub_strategy: all_checkpoints
• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 100
• per_device_eval_batch_size: 256
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 2
• 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': 1.6666666666666667e-05}
• 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-v3-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

Training Logs

Click to expand

Epoch	Step	Training Loss	vitaminc-pairs loss	negation-triplets loss	scitail-pairs-pos loss	scitail-pairs-qa loss	xsum-pairs loss	sciq pairs loss	qasc pairs loss	openbookqa pairs loss	msmarco pairs loss	nq pairs loss	trivia pairs loss	gooaq pairs loss	paws-pos loss	global dataset loss	sts-test_spearman_cosine	allNLI-dev_max_ap	Qnli-dev_max_ap
0.0168	8	10.2928	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0336	16	9.2166	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0504	24	9.4858	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0672	32	10.6143	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0840	40	8.7553	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1008	48	10.9939	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1176	56	7.6039	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1345	64	5.9498	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1513	72	7.3051	3.2988	3.9604	1.9818	2.1997	6.0515	0.6095	6.3199	4.8391	6.4886	6.6406	6.4894	6.1527	2.0082	4.9577	0.3066	0.3444	0.5627
0.1681	80	8.3034	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1849	88	7.6669	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2017	96	6.6415	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2185	104	5.7797	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2353	112	5.8361	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2521	120	5.3339	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2689	128	5.5908	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2857	136	5.3209	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3025	144	5.5359	3.3310	3.8580	1.4769	1.6994	5.4819	0.5385	5.2021	4.4410	5.3419	5.5506	5.6972	5.3376	1.4170	3.9169	0.2954	0.3795	0.6317
0.3193	152	5.4713	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3361	160	4.9368	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3529	168	4.6594	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3697	176	4.8392	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3866	184	4.414	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4034	192	4.891	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4202	200	4.4553	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4370	208	3.9729	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4538	216	3.7705	3.2468	3.6435	0.7890	0.7356	3.9327	0.4082	3.7175	3.5404	3.5351	4.0506	3.9953	3.6074	0.4195	2.4726	0.3791	0.4133	0.6779
0.4706	224	3.8409	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4874	232	3.7894	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5042	240	3.3523	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5210	248	3.2407	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5378	256	3.3203	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5546	264	2.8457	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5714	272	2.4181	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5882	280	3.4589	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6050	288	2.8203	3.1119	3.1485	0.4531	0.2652	2.6895	0.2656	2.5542	2.7523	2.6600	3.1773	3.2099	2.7316	0.2006	1.6342	0.5257	0.4717	0.7078
0.6218	296	2.4697	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6387	304	2.4654	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6555	312	2.4236	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6723	320	2.2879	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6891	328	2.2145	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7059	336	1.8464	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7227	344	2.0086	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7395	352	2.0635	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7563	360	1.8584	3.3202	2.5793	0.3434	0.1618	1.6759	0.1834	1.6454	2.1257	2.1938	2.5316	2.4558	2.0596	0.0984	1.2206	0.6610	0.5199	0.7119
0.7731	368	2.0286	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7899	376	1.9389	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8067	384	1.7453	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8235	392	1.6629	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8403	400	1.2724	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8571	408	1.7824	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8739	416	1.5826	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8908	424	1.1971	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9076	432	1.5228	3.3624	2.1952	0.3006	0.1223	1.1091	0.1582	1.2383	1.8664	1.7434	2.3959	2.0697	1.7563	0.0766	1.0193	0.7292	0.5194	0.7126
0.9244	440	1.3323	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9412	448	1.5124	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9580	456	1.5565	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9748	464	1.3672	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9916	472	1.0382	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0084	480	1.0626	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0252	488	1.3539	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0420	496	1.1723	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0588	504	1.4235	3.4031	1.9759	0.2554	0.0814	0.9034	0.1378	1.1603	1.7589	1.5608	2.1230	1.7719	1.6633	0.0720	0.9380	0.7523	0.5297	0.7129
1.0756	512	1.2283	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0924	520	1.2455	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1092	528	1.4265	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1261	536	1.296	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1429	544	0.8763	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1597	552	1.5678	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1765	560	1.2548	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1933	568	1.3731	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2101	576	1.3023	3.3815	1.8740	0.2373	0.0769	0.7711	0.1237	0.9432	1.6871	1.5070	1.9947	1.6041	1.5579	0.0721	0.8661	0.7642	0.5412	0.7159
1.2269	584	0.8135	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2437	592	1.0259	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2605	600	1.1896	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2773	608	1.0532	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2941	616	1.3221	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3109	624	1.3136	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3277	632	1.2238	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3445	640	1.2407	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3613	648	1.2245	3.4717	1.7962	0.2242	0.0488	0.7472	0.1108	0.9272	1.6692	1.3845	1.9117	1.3410	1.4387	0.0701	0.8505	0.7680	0.5471	0.7227
1.3782	656	1.0428	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3950	664	1.1391	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4118	672	1.2632	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4286	680	0.9403	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4454	688	0.7571	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4622	696	0.9436	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4790	704	1.1239	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4958	712	0.9499	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5126	720	1.0945	3.6495	1.6693	0.2157	0.0492	0.6830	0.1049	0.9140	1.5967	1.4397	1.7394	1.3303	1.4334	0.0603	0.8185	0.7815	0.5606	0.7098
1.5294	728	1.1161	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5462	736	1.0056	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5630	744	1.1743	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5798	752	0.9153	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5966	760	1.1589	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6134	768	0.9187	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6303	776	0.6937	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6471	784	0.9704	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6639	792	0.7343	3.5442	1.6493	0.2208	0.0249	0.6152	0.0969	0.7111	1.5369	1.4058	1.7066	1.2784	1.3419	0.0585	0.7827	0.7749	0.5627	0.7284
1.6807	800	1.2878	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6975	808	0.9898	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7143	816	0.7613	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7311	824	0.9612	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7479	832	1.1524	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7647	840	0.827	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7815	848	1.1898	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7983	856	1.0117	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8151	864	0.7019	3.4544	1.6149	0.2035	0.0181	0.5525	0.0999	0.6641	1.5456	1.3911	1.7188	1.2547	1.3517	0.0562	0.7473	0.7684	0.5697	0.7329
1.8319	872	0.8352	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8487	880	0.7836	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8655	888	1.0187	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8824	896	0.74	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8992	904	0.7263	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9160	912	0.8073	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9328	920	0.8185	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9496	928	1.0992	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9664	936	0.9973	3.5110	1.5776	0.2035	0.0250	0.5881	0.0934	0.6719	1.5059	1.2970	1.6186	1.1815	1.2714	0.0564	0.7213	0.7799	0.5544	0.7341
1.9832	944	0.6662	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.0	952	0.533	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.0168	960	0.7712	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.0336	968	0.6879	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.0504	976	0.7975	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.0672	984	0.873	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.0840	992	0.7995	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.1008	1000	1.0119	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.1176	1008	0.6317	3.6778	1.5845	0.2102	0.0228	0.5851	0.0977	0.6411	1.4752	1.2992	1.6314	1.1260	1.2683	0.0556	0.7329	0.7693	0.5614	0.7274
2.1345	1016	0.72	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.1513	1024	0.9418	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.1681	1032	0.7848	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.1849	1040	0.6965	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.2017	1048	1.0447	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.2185	1056	0.6361	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.2353	1064	0.6837	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.2521	1072	0.5713	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.2689	1080	0.8193	3.6399	1.5565	0.2069	0.0213	0.5440	0.0904	0.6057	1.4815	1.2856	1.6441	1.1469	1.2540	0.0543	0.7216	0.7765	0.5599	0.7322
2.2857	1088	0.9754	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.3025	1096	0.8932	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.3193	1104	0.8716	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.3361	1112	0.8787	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.3529	1120	0.9529	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.3697	1128	0.775	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.3866	1136	0.6178	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.4034	1144	0.8384	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.4202	1152	0.9425	3.5672	1.5244	0.2111	0.0162	0.5593	0.0893	0.5759	1.4933	1.2703	1.5815	1.1202	1.2132	0.0531	0.7058	0.7730	0.5635	0.7350
2.4370	1160	0.4551	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.4538	1168	0.6392	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.4706	1176	0.8341	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.4874	1184	0.7392	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.5042	1192	0.7646	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.5210	1200	0.8613	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.5378	1208	0.7585	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.5546	1216	1.0611	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.5714	1224	0.6506	3.6439	1.5040	0.2125	0.0162	0.5282	0.0863	0.5858	1.5073	1.2444	1.5493	1.1014	1.2073	0.0532	0.7022	0.7774	0.5647	0.7328
2.5882	1232	0.8525	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.6050	1240	0.6304	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.6218	1248	0.6354	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.6387	1256	0.6583	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.6555	1264	0.5964	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.6723	1272	0.818	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.6891	1280	0.8635	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.7059	1288	0.6389	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.7227	1296	0.6819	3.6131	1.5104	0.2084	0.0148	0.5229	0.0854	0.5588	1.4963	1.2766	1.5679	1.0982	1.2203	0.0529	0.7059	0.7762	0.5659	0.7355
2.7395	1304	0.7878	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.7563	1312	0.7638	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.7731	1320	0.8885	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.7899	1328	0.8184	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.8067	1336	0.7472	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.8235	1344	0.7012	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.8403	1352	0.4622	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.8571	1360	0.846	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.8739	1368	0.8308	3.6224	1.5088	0.2084	0.0148	0.5118	0.0858	0.5523	1.4941	1.2756	1.5808	1.0925	1.2114	0.0521	0.7022	0.7765	0.5662	0.7366
2.8908	1376	0.5334	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.9076	1384	0.7893	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.9244	1392	0.6897	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.9412	1400	0.7803	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.9580	1408	0.841	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.9748	1416	0.787	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.9916	1424	0.5861	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
3.0	1428	-	3.6139	1.5071	0.2084	0.0150	0.5124	0.0862	0.5532	1.4924	1.2700	1.5806	1.0905	1.2081	0.0519	0.6997	0.7776	0.5665	0.7369

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.2.0
• Transformers: 4.44.2
• PyTorch: 2.4.1+cu121
• Accelerate: 0.34.2
• Datasets: 3.0.1
• Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers

@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",
}