README.md

metadata

base_model: sentence-transformers/all-MiniLM-L6-v2
datasets: []
language: []
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
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:724
  - loss:CoSENTLoss
widget:
  - source_sentence: Financials
    sentences:
      - What is the financial performance of ABC?
      - What companies operate in the same space as ABC?
      - What standards are used to evaluate the industry?
  - source_sentence: Research
    sentences:
      - What recent studies have been conducted on ABC?
      - What are the key factors considered in rating ABC?
      - How is the rating framework applied to the sector?
  - source_sentence: Criteria
    sentences:
      - >-
        What are the projected economic impacts of inflation on the technology
        industry?
      - What is the process for assessing the creditworthiness of ABC?
      - What are the primary ESG challenges faced by ABC?
  - source_sentence: Financials
    sentences:
      - Can you list the strengths and weaknesses of ABC?
      - What is understood by the term sovereign risk?
      - Can you provide the financial history of ABC?
  - source_sentence: Research
    sentences:
      - >-
        What macroeconomic trends are influencing the credit ratings of the
        automotive industry?
      - Who are the main rivals of ABC?
      - Can you provide the latest research insights on ABC?
model-index:
  - name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts dev
          type: sts-dev
        metrics:
          - type: pearson_cosine
            value: .nan
            name: Pearson Cosine
          - type: spearman_cosine
            value: .nan
            name: Spearman Cosine
          - type: pearson_manhattan
            value: .nan
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: .nan
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: .nan
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: .nan
            name: Spearman Euclidean
          - type: pearson_dot
            value: .nan
            name: Pearson Dot
          - type: spearman_dot
            value: .nan
            name: Spearman Dot
          - type: pearson_max
            value: .nan
            name: Pearson Max
          - type: spearman_max
            value: .nan
            name: Spearman Max

SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. It maps sentences & paragraphs to a 384-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: sentence-transformers/all-MiniLM-L6-v2
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 384 tokens
• Similarity Function: Cosine Similarity

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: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': 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("ManishThota/QueryRouter")
# Run inference
sentences = [
    'Research',
    'Can you provide the latest research insights on ABC?',
    'Who are the main rivals of ABC?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Semantic Similarity

• Dataset: sts-dev
• Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	nan
spearman_cosine	nan
pearson_manhattan	nan
spearman_manhattan	nan
pearson_euclidean	nan
spearman_euclidean	nan
pearson_dot	nan
spearman_dot	nan
pearson_max	nan
spearman_max	nan

Training Details

Training Dataset

Unnamed Dataset

• Size: 724 training samples
• Columns: sentence1, sentence2, and score
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 3 tokens mean: 3.27 tokens max: 4 tokens	min: 9 tokens mean: 14.23 tokens max: 29 tokens	min: 1.0 mean: 1.0 max: 1.0

• Samples:

  sentence1	sentence2	score
  Rating	What rating does XYZ have?	1.0
  Rating	Can you provide the rating for XYZ?	1.0
  Rating	How is XYZ rated?	1.0

• Loss: CoSENTLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  

Evaluation Dataset

Unnamed Dataset

• Size: 60 evaluation samples
• Columns: sentence1, sentence2, and score
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 3 tokens mean: 3.25 tokens max: 4 tokens	min: 9 tokens mean: 12.48 tokens max: 20 tokens	min: 1.0 mean: 1.0 max: 1.0

• Samples:

  sentence1	sentence2	score
  Rating	What is the current rating of ABC?	1.0
  Rating	Can you tell me the rating for ABC?	1.0
  Rating	What rating has ABC been assigned?	1.0

• Loss: CoSENTLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• learning_rate: 2e-05
• num_train_epochs: 10
• warmup_ratio: 0.1
• save_only_model: True
• seed: 33
• fp16: True
• load_best_model_at_end: True

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 8
• per_device_eval_batch_size: 8
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• learning_rate: 2e-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: 10
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.1
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: True
• save_on_each_node: False
• save_only_model: True
• restore_callback_states_from_checkpoint: False
• no_cuda: False
• use_cpu: False
• use_mps_device: False
• seed: 33
• 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: True
• 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: False
• resume_from_checkpoint: None
• hub_model_id: None
• hub_strategy: every_save
• 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
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	loss	sts-dev_spearman_cosine
0.0220	2	-	0.0	nan
0.0440	4	-	0.0	nan
0.0659	6	-	0.0	nan
0.0879	8	-	0.0	nan
0.1099	10	-	0.0	nan
0.1319	12	-	0.0	nan
0.1538	14	-	0.0	nan
0.1758	16	-	0.0	nan
0.1978	18	-	0.0	nan
0.2198	20	-	0.0	nan
0.2418	22	-	0.0	nan
0.2637	24	-	0.0	nan
0.2857	26	-	0.0	nan
0.3077	28	-	0.0	nan
0.3297	30	-	0.0	nan
0.3516	32	-	0.0	nan
0.3736	34	-	0.0	nan
0.3956	36	-	0.0	nan
0.4176	38	-	0.0	nan
0.4396	40	-	0.0	nan
0.4615	42	-	0.0	nan
0.4835	44	-	0.0	nan
0.5055	46	-	0.0	nan
0.5275	48	-	0.0	nan
0.5495	50	-	0.0	nan
0.5714	52	-	0.0	nan
0.5934	54	-	0.0	nan
0.6154	56	-	0.0	nan
0.6374	58	-	0.0	nan
0.6593	60	-	0.0	nan
0.6813	62	-	0.0	nan
0.7033	64	-	0.0	nan
0.7253	66	-	0.0	nan
0.7473	68	-	0.0	nan
0.7692	70	-	0.0	nan
0.7912	72	-	0.0	nan
0.8132	74	-	0.0	nan
0.8352	76	-	0.0	nan
0.8571	78	-	0.0	nan
0.8791	80	-	0.0	nan
0.9011	82	-	0.0	nan
0.9231	84	-	0.0	nan
0.9451	86	-	0.0	nan
0.9670	88	-	0.0	nan
0.9890	90	-	0.0	nan
1.0110	92	-	0.0	nan
1.0330	94	-	0.0	nan
1.0549	96	-	0.0	nan
1.0769	98	-	0.0	nan
1.0989	100	-	0.0	nan
1.1209	102	-	0.0	nan
1.1429	104	-	0.0	nan
1.1648	106	-	0.0	nan
1.1868	108	-	0.0	nan
1.2088	110	-	0.0	nan
1.2308	112	-	0.0	nan
1.2527	114	-	0.0	nan
1.2747	116	-	0.0	nan
1.2967	118	-	0.0	nan
1.3187	120	-	0.0	nan
1.3407	122	-	0.0	nan
1.3626	124	-	0.0	nan
1.3846	126	-	0.0	nan
1.4066	128	-	0.0	nan
1.4286	130	-	0.0	nan
1.4505	132	-	0.0	nan
1.4725	134	-	0.0	nan
1.4945	136	-	0.0	nan
1.5165	138	-	0.0	nan
1.5385	140	-	0.0	nan
1.5604	142	-	0.0	nan
1.5824	144	-	0.0	nan
1.6044	146	-	0.0	nan
1.6264	148	-	0.0	nan
1.6484	150	-	0.0	nan
1.6703	152	-	0.0	nan
1.6923	154	-	0.0	nan
1.7143	156	-	0.0	nan
1.7363	158	-	0.0	nan
1.7582	160	-	0.0	nan
1.7802	162	-	0.0	nan
1.8022	164	-	0.0	nan
1.8242	166	-	0.0	nan
1.8462	168	-	0.0	nan
1.8681	170	-	0.0	nan
1.8901	172	-	0.0	nan
1.9121	174	-	0.0	nan
1.9341	176	-	0.0	nan
1.9560	178	-	0.0	nan
1.9780	180	-	0.0	nan
2.0	182	-	0.0	nan
2.0220	184	-	0.0	nan
2.0440	186	-	0.0	nan
2.0659	188	-	0.0	nan
2.0879	190	-	0.0	nan
2.1099	192	-	0.0	nan
2.1319	194	-	0.0	nan
2.1538	196	-	0.0	nan
2.1758	198	-	0.0	nan
2.1978	200	-	0.0	nan
2.2198	202	-	0.0	nan
2.2418	204	-	0.0	nan
2.2637	206	-	0.0	nan
2.2857	208	-	0.0	nan
2.3077	210	-	0.0	nan
2.3297	212	-	0.0	nan
2.3516	214	-	0.0	nan
2.3736	216	-	0.0	nan
2.3956	218	-	0.0	nan
2.4176	220	-	0.0	nan
2.4396	222	-	0.0	nan
2.4615	224	-	0.0	nan
2.4835	226	-	0.0	nan
2.5055	228	-	0.0	nan
2.5275	230	-	0.0	nan
2.5495	232	-	0.0	nan
2.5714	234	-	0.0	nan
2.5934	236	-	0.0	nan
2.6154	238	-	0.0	nan
2.6374	240	-	0.0	nan
2.6593	242	-	0.0	nan
2.6813	244	-	0.0	nan
2.7033	246	-	0.0	nan
2.7253	248	-	0.0	nan
2.7473	250	-	0.0	nan
2.7692	252	-	0.0	nan
2.7912	254	-	0.0	nan
2.8132	256	-	0.0	nan
2.8352	258	-	0.0	nan
2.8571	260	-	0.0	nan
2.8791	262	-	0.0	nan
2.9011	264	-	0.0	nan
2.9231	266	-	0.0	nan
2.9451	268	-	0.0	nan
2.9670	270	-	0.0	nan
2.9890	272	-	0.0	nan
3.0110	274	-	0.0	nan
3.0330	276	-	0.0	nan
3.0549	278	-	0.0	nan
3.0769	280	-	0.0	nan
3.0989	282	-	0.0	nan
3.1209	284	-	0.0	nan
3.1429	286	-	0.0	nan
3.1648	288	-	0.0	nan
3.1868	290	-	0.0	nan
3.2088	292	-	0.0	nan
3.2308	294	-	0.0	nan
3.2527	296	-	0.0	nan
3.2747	298	-	0.0	nan
3.2967	300	-	0.0	nan
3.3187	302	-	0.0	nan
3.3407	304	-	0.0	nan
3.3626	306	-	0.0	nan
3.3846	308	-	0.0	nan
3.4066	310	-	0.0	nan
3.4286	312	-	0.0	nan
3.4505	314	-	0.0	nan
3.4725	316	-	0.0	nan
3.4945	318	-	0.0	nan
3.5165	320	-	0.0	nan
3.5385	322	-	0.0	nan
3.5604	324	-	0.0	nan
3.5824	326	-	0.0	nan
3.6044	328	-	0.0	nan
3.6264	330	-	0.0	nan
3.6484	332	-	0.0	nan
3.6703	334	-	0.0	nan
3.6923	336	-	0.0	nan
3.7143	338	-	0.0	nan
3.7363	340	-	0.0	nan
3.7582	342	-	0.0	nan
3.7802	344	-	0.0	nan
3.8022	346	-	0.0	nan
3.8242	348	-	0.0	nan
3.8462	350	-	0.0	nan
3.8681	352	-	0.0	nan
3.8901	354	-	0.0	nan
3.9121	356	-	0.0	nan
3.9341	358	-	0.0	nan
3.9560	360	-	0.0	nan
3.9780	362	-	0.0	nan
4.0	364	-	0.0	nan
4.0220	366	-	0.0	nan
4.0440	368	-	0.0	nan
4.0659	370	-	0.0	nan
4.0879	372	-	0.0	nan
4.1099	374	-	0.0	nan
4.1319	376	-	0.0	nan
4.1538	378	-	0.0	nan
4.1758	380	-	0.0	nan
4.1978	382	-	0.0	nan
4.2198	384	-	0.0	nan
4.2418	386	-	0.0	nan
4.2637	388	-	0.0	nan
4.2857	390	-	0.0	nan
4.3077	392	-	0.0	nan
4.3297	394	-	0.0	nan
4.3516	396	-	0.0	nan
4.3736	398	-	0.0	nan
4.3956	400	-	0.0	nan
4.4176	402	-	0.0	nan
4.4396	404	-	0.0	nan
4.4615	406	-	0.0	nan
4.4835	408	-	0.0	nan
4.5055	410	-	0.0	nan
4.5275	412	-	0.0	nan
4.5495	414	-	0.0	nan
4.5714	416	-	0.0	nan
4.5934	418	-	0.0	nan
4.6154	420	-	0.0	nan
4.6374	422	-	0.0	nan
4.6593	424	-	0.0	nan
4.6813	426	-	0.0	nan
4.7033	428	-	0.0	nan
4.7253	430	-	0.0	nan
4.7473	432	-	0.0	nan
4.7692	434	-	0.0	nan
4.7912	436	-	0.0	nan
4.8132	438	-	0.0	nan
4.8352	440	-	0.0	nan
4.8571	442	-	0.0	nan
4.8791	444	-	0.0	nan
4.9011	446	-	0.0	nan
4.9231	448	-	0.0	nan
4.9451	450	-	0.0	nan
4.9670	452	-	0.0	nan
4.9890	454	-	0.0	nan
5.0110	456	-	0.0	nan
5.0330	458	-	0.0	nan
5.0549	460	-	0.0	nan
5.0769	462	-	0.0	nan
5.0989	464	-	0.0	nan
5.1209	466	-	0.0	nan
5.1429	468	-	0.0	nan
5.1648	470	-	0.0	nan
5.1868	472	-	0.0	nan
5.2088	474	-	0.0	nan
5.2308	476	-	0.0	nan
5.2527	478	-	0.0	nan
5.2747	480	-	0.0	nan
5.2967	482	-	0.0	nan
5.3187	484	-	0.0	nan
5.3407	486	-	0.0	nan
5.3626	488	-	0.0	nan
5.3846	490	-	0.0	nan
5.4066	492	-	0.0	nan
5.4286	494	-	0.0	nan
5.4505	496	-	0.0	nan
5.4725	498	-	0.0	nan
5.4945	500	0.0	0.0	nan
5.5165	502	-	0.0	nan
5.5385	504	-	0.0	nan
5.5604	506	-	0.0	nan
5.5824	508	-	0.0	nan
5.6044	510	-	0.0	nan
5.6264	512	-	0.0	nan
5.6484	514	-	0.0	nan
5.6703	516	-	0.0	nan
5.6923	518	-	0.0	nan
5.7143	520	-	0.0	nan
5.7363	522	-	0.0	nan
5.7582	524	-	0.0	nan
5.7802	526	-	0.0	nan
5.8022	528	-	0.0	nan
5.8242	530	-	0.0	nan
5.8462	532	-	0.0	nan
5.8681	534	-	0.0	nan
5.8901	536	-	0.0	nan
5.9121	538	-	0.0	nan
5.9341	540	-	0.0	nan
5.9560	542	-	0.0	nan
5.9780	544	-	0.0	nan
6.0	546	-	0.0	nan
6.0220	548	-	0.0	nan
6.0440	550	-	0.0	nan
6.0659	552	-	0.0	nan
6.0879	554	-	0.0	nan
6.1099	556	-	0.0	nan
6.1319	558	-	0.0	nan
6.1538	560	-	0.0	nan
6.1758	562	-	0.0	nan
6.1978	564	-	0.0	nan
6.2198	566	-	0.0	nan
6.2418	568	-	0.0	nan
6.2637	570	-	0.0	nan
6.2857	572	-	0.0	nan
6.3077	574	-	0.0	nan
6.3297	576	-	0.0	nan
6.3516	578	-	0.0	nan
6.3736	580	-	0.0	nan
6.3956	582	-	0.0	nan
6.4176	584	-	0.0	nan
6.4396	586	-	0.0	nan
6.4615	588	-	0.0	nan
6.4835	590	-	0.0	nan
6.5055	592	-	0.0	nan
6.5275	594	-	0.0	nan
6.5495	596	-	0.0	nan
6.5714	598	-	0.0	nan
6.5934	600	-	0.0	nan
6.6154	602	-	0.0	nan
6.6374	604	-	0.0	nan
6.6593	606	-	0.0	nan
6.6813	608	-	0.0	nan
6.7033	610	-	0.0	nan
6.7253	612	-	0.0	nan
6.7473	614	-	0.0	nan
6.7692	616	-	0.0	nan
6.7912	618	-	0.0	nan
6.8132	620	-	0.0	nan
6.8352	622	-	0.0	nan
6.8571	624	-	0.0	nan
6.8791	626	-	0.0	nan
6.9011	628	-	0.0	nan
6.9231	630	-	0.0	nan
6.9451	632	-	0.0	nan
6.9670	634	-	0.0	nan
6.9890	636	-	0.0	nan
7.0110	638	-	0.0	nan
7.0330	640	-	0.0	nan
7.0549	642	-	0.0	nan
7.0769	644	-	0.0	nan
7.0989	646	-	0.0	nan
7.1209	648	-	0.0	nan
7.1429	650	-	0.0	nan
7.1648	652	-	0.0	nan
7.1868	654	-	0.0	nan
7.2088	656	-	0.0	nan
7.2308	658	-	0.0	nan
7.2527	660	-	0.0	nan
7.2747	662	-	0.0	nan
7.2967	664	-	0.0	nan
7.3187	666	-	0.0	nan
7.3407	668	-	0.0	nan
7.3626	670	-	0.0	nan
7.3846	672	-	0.0	nan
7.4066	674	-	0.0	nan
7.4286	676	-	0.0	nan
7.4505	678	-	0.0	nan
7.4725	680	-	0.0	nan
7.4945	682	-	0.0	nan
7.5165	684	-	0.0	nan
7.5385	686	-	0.0	nan
7.5604	688	-	0.0	nan
7.5824	690	-	0.0	nan
7.6044	692	-	0.0	nan
7.6264	694	-	0.0	nan
7.6484	696	-	0.0	nan
7.6703	698	-	0.0	nan
7.6923	700	-	0.0	nan
7.7143	702	-	0.0	nan
7.7363	704	-	0.0	nan
7.7582	706	-	0.0	nan
7.7802	708	-	0.0	nan
7.8022	710	-	0.0	nan
7.8242	712	-	0.0	nan
7.8462	714	-	0.0	nan
7.8681	716	-	0.0	nan
7.8901	718	-	0.0	nan
7.9121	720	-	0.0	nan
7.9341	722	-	0.0	nan
7.9560	724	-	0.0	nan
7.9780	726	-	0.0	nan
8.0	728	-	0.0	nan
8.0220	730	-	0.0	nan
8.0440	732	-	0.0	nan
8.0659	734	-	0.0	nan
8.0879	736	-	0.0	nan
8.1099	738	-	0.0	nan
8.1319	740	-	0.0	nan
8.1538	742	-	0.0	nan
8.1758	744	-	0.0	nan
8.1978	746	-	0.0	nan
8.2198	748	-	0.0	nan
8.2418	750	-	0.0	nan
8.2637	752	-	0.0	nan
8.2857	754	-	0.0	nan
8.3077	756	-	0.0	nan
8.3297	758	-	0.0	nan
8.3516	760	-	0.0	nan
8.3736	762	-	0.0	nan
8.3956	764	-	0.0	nan
8.4176	766	-	0.0	nan
8.4396	768	-	0.0	nan
8.4615	770	-	0.0	nan
8.4835	772	-	0.0	nan
8.5055	774	-	0.0	nan
8.5275	776	-	0.0	nan
8.5495	778	-	0.0	nan
8.5714	780	-	0.0	nan
8.5934	782	-	0.0	nan
8.6154	784	-	0.0	nan
8.6374	786	-	0.0	nan
8.6593	788	-	0.0	nan
8.6813	790	-	0.0	nan
8.7033	792	-	0.0	nan
8.7253	794	-	0.0	nan
8.7473	796	-	0.0	nan
8.7692	798	-	0.0	nan
8.7912	800	-	0.0	nan
8.8132	802	-	0.0	nan
8.8352	804	-	0.0	nan
8.8571	806	-	0.0	nan
8.8791	808	-	0.0	nan
8.9011	810	-	0.0	nan
8.9231	812	-	0.0	nan
8.9451	814	-	0.0	nan
8.9670	816	-	0.0	nan
8.9890	818	-	0.0	nan
9.0110	820	-	0.0	nan
9.0330	822	-	0.0	nan
9.0549	824	-	0.0	nan
9.0769	826	-	0.0	nan
9.0989	828	-	0.0	nan
9.1209	830	-	0.0	nan
9.1429	832	-	0.0	nan
9.1648	834	-	0.0	nan
9.1868	836	-	0.0	nan
9.2088	838	-	0.0	nan
9.2308	840	-	0.0	nan
9.2527	842	-	0.0	nan
9.2747	844	-	0.0	nan
9.2967	846	-	0.0	nan
9.3187	848	-	0.0	nan
9.3407	850	-	0.0	nan
9.3626	852	-	0.0	nan
9.3846	854	-	0.0	nan
9.4066	856	-	0.0	nan
9.4286	858	-	0.0	nan
9.4505	860	-	0.0	nan
9.4725	862	-	0.0	nan
9.4945	864	-	0.0	nan
9.5165	866	-	0.0	nan
9.5385	868	-	0.0	nan
9.5604	870	-	0.0	nan
9.5824	872	-	0.0	nan
9.6044	874	-	0.0	nan
9.6264	876	-	0.0	nan
9.6484	878	-	0.0	nan
9.6703	880	-	0.0	nan
9.6923	882	-	0.0	nan
9.7143	884	-	0.0	nan
9.7363	886	-	0.0	nan
9.7582	888	-	0.0	nan
9.7802	890	-	0.0	nan
9.8022	892	-	0.0	nan
9.8242	894	-	0.0	nan
9.8462	896	-	0.0	nan
9.8681	898	-	0.0	nan
9.8901	900	-	0.0	nan
9.9121	902	-	0.0	nan
9.9341	904	-	0.0	nan
9.9560	906	-	0.0	nan
9.9780	908	-	0.0	nan
10.0	910	-	0.0	nan

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.0.1
• Transformers: 4.41.2
• PyTorch: 2.0.1+cu118
• Accelerate: 0.31.0
• Datasets: 2.20.0
• 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",
}

CoSENTLoss

@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}