HassanCS/TCRb_HLA_peptide_esm2_t6_8M_UR50D_best

SentenceTransformer based on facebook/esm2t68M_UR50D

This is a sentence-transformers model finetuned from facebook/esm2_t6_8M_UR50D. It maps sentences & paragraphs to a 320-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: facebook/esm2_t6_8M_UR50D <!-- at revision c731040fcd8d73dceaa04b0a8e6329b345b0f5df -->
• —Maximum Sequence Length: 1026 tokens
• —Output Dimensionality: 320 dimensions
• —Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown -->

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': 1026, 'do_lower_case': False}) with Transformer model: EsmModel 
  (1): Pooling({'word_embedding_dimension': 320, '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("HassanCS/TCRb_HLA_peptide_esm2_t6_8M_UR50D_best")
# Run inference
sentences = [
    'D A G V T Q S P T H L I K T R G Q Q V T L R C S P I S G H K S V S W Y Q Q V L G Q G P Q F I F Q Y Y E K E E R G R G N F P D R F S A R Q F P N Y S S E L N V N A L L L G D S A L Y L C C A S S P G T D Y G Y T F F G S G T R L T V V E',
    'E T G V T Q S P T H L I K T R G Q Q V T L R C S S Q S G H N T V S W Y Q Q A L G Q G P Q F I F Q Y Y R E E E N G R G N F P P R F S G L Q F P N Y S S E L N V N A L E L D D S A L Y L C C A S S S R T S G I N E Q F F F G P G T R L T V L E',
    'G A G V S Q S L R H K V A K K G K D V A L R Y D P I S G H N A L Y W Y R Q S L G Q G L E F P I Y F Q G K D A A D K S G L P R D R F S A Q R S E G S I S T L K F Q R T Q Q G D L A V Y L C A S S S T R G S R G E Q F F G P G T R L T V L E',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 320]

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

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Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details> -->

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Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details> -->

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Evaluation

Metrics

Semantic Similarity

• —Dataset: all-dev
• —Evaluated with <code>EmbeddingSimilarityEvaluator</code>

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Training Details

Training Dataset

Unnamed Dataset

• —Size: 504,071 training samples
• —Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
• —Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | <ul><li>min: 111 tokens</li><li>mean: 117.87 tokens</li><li>max: 125 tokens</li></ul> | <ul><li>min: 109 tokens</li><li>mean: 117.88 tokens</li><li>max: 132 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.36</li><li>max: 1.0</li></ul> |
• —Samples: | sentence1 | sentence2 | score | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------| | <code>N A G V T Q T P K F Q V L K T G Q S M T L Q C A Q D M N H N S M Y W Y R Q D P G M G L R L I Y Y S A S E G T T D K G E V P N G Y N V S R L N K R E F S L R L E S A A P S Q T S V Y F C A S R S G S G T N Y N E Q F F G P G T R L T V L E</code> | <code>G A V V S Q H P S W V I C K S G T S V K I E C R S L D F Q A T T M F W Y R Q F P K Q S L M L M A T S N E G S K A T Y E Q G V E K D K F L I N H A S L T L S T L T V T S A H P E D S S F Y I C S A P T S G G H N E Q F G P G T R L T V L E</code> | <code>0.36983471074380164</code> | | <code>D T G V S Q N P R H K I T K R G Q N V T F R C D P I S E H N R L Y W Y R Q T L G Q G P E F L T Y F Q N E A Q L E K S R L L S D R F S A E R P K G S F S T L E I Q R T E Q G D S A M Y L C A S S L I Q G A S W G Y T F G S G T R L T V V E</code> | <code>N A G V T Q T P K F Q V L K T G Q S M T L Q C A Q D M N H E Y M S W Y R Q D P G M G L R L I H Y S V G A G I T D Q G E V P N G Y N V S R S T T E D F P L R L L S A A P S Q T S V Y F C A S S S L D G N Y G Y T F G S G T R L T V V E</code> | <code>0.8450413223140495</code> | | <code>D V K V T Q S S R Y L V K R T G E K V F L E C V Q D M D H E N M F W Y R Q D P G L G L R L I Y F S Y D V K M K E K G D I P E G Y S V S R E K K E R F S L I L E S A S T N Q T S M Y L C C A S R V R D R G R L D Y G Y T F F G S G T R L T V V E</code> | <code>D G G I T Q S P K Y L F R K E G Q N V T L S C E Q N L N H D A M Y W Y R Q V P G Q G L R L I Y Y S H I V N D F Q K G D I A E G Y S V S R E K K E S F P L T V T S A Q K N P T A F Y L C C A S S S R S G N E K L F F F G S G T Q L S V L E</code> | <code>0.8347107438016529</code> |
• —Loss: <code>CoSENTLoss</code> with these parameters:

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

Evaluation Dataset

Unnamed Dataset

• —Size: 56,008 evaluation samples
• —Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
• —Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | <ul><li>min: 110 tokens</li><li>mean: 117.92 tokens</li><li>max: 125 tokens</li></ul> | <ul><li>min: 111 tokens</li><li>mean: 117.93 tokens</li><li>max: 127 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.38</li><li>max: 1.0</li></ul> |
• —Samples: | sentence1 | sentence2 | score | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------| | <code>N A G V T Q T P K F Q V L K T G Q S M T L Q C A Q D M N H E Y M S W Y R Q D P G M G L R L I H Y S V G A G I T D Q G E V P N G Y N V S R S T T E D F P L R L L S A A P S Q T S V Y F C C A S S P I T G T G I Y G Y T F F G S G T R L T V V E</code> | <code>D G G I T Q S P K Y L F R K E G Q N V T L S C E Q N L N H D A M Y W Y R Q D P G Q G L R L I Y Y S Q I V N D F Q K G D I A E G Y S V S R E K K E S F P L T V T S A Q K N P T A F Y L C C A S S M I P D M N T E A F F F G Q G T R L T V V E</code> | <code>0.03925619834710743</code> | | <code>G A V V S Q H P S W V I C K S G T S V K I E C R S L D F Q A T T M F W Y R Q F P K Q S L M L M A T S N E G S K A T Y E Q G V E K D K F L I N H A S L T L S T L T V T S A H P E D S S F Y I C S A R D S T G N G Y T F G S G T R L T V V E</code> | <code>S A V I S Q K P S R D I C Q R G T S L T I Q C Q V D S Q V T M M F W Y R Q Q P G Q S L T L I A T A N Q G S E A T Y E S G F V I D K F P I S R P N L T F S T L T V S N M S P E D S S I Y L C S V G T G G T N E K L F F G Q G T R L T V V E</code> | <code>0.8347107438016529</code> | | <code>D A R V T Q T P R H K V T E M G Q E V T M R C Q P I L G H N T V F W Y R Q T M M Q G L E L L A Y F R N R A P L D D S G M P K D R F S A E M P D A T L A T L K I Q P S E P R D S A V Y F C A S G T G E G S Y N E Q F F G P G T R L T V L E</code> | <code>D A R V T Q T P R H K V T E M G Q E V T M R C Q P I L G H N T V F W Y R Q T M M Q G L E L L A Y F R N R A P L D D S G M P K D R F S A E M P D A T L A T L K I Q P S E P R D S A V Y F C A S G D Y G N R G P Y S N Q P Q H F G D G T R L S I L E</code> | <code>0.07024793388429751</code> |
• —Loss: <code>CoSENTLoss</code> with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

• —eval_strategy: epoch
• —per_device_train_batch_size: 128
• —per_device_eval_batch_size: 128
• —learning_rate: 0.001
• —weight_decay: 0.0001
• —num_train_epochs: 10
• —fp16: True
• —load_best_model_at_end: True

All Hyperparameters

<details><summary>Click to expand</summary>

• —overwrite_output_dir: False
• —do_predict: False
• —eval_strategy: epoch
• —prediction_loss_only: True
• —per_device_train_batch_size: 128
• —per_device_eval_batch_size: 128
• —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: 0.001
• —weight_decay: 0.0001
• —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: 0.0
• —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: 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: True
• —ignore_data_skip: False
• —fsdp: []
• —fsdp_min_num_params: 0
• —fsdp_config: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —fsdp_transformer_layer_cls_to_wrap: None
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': 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: None
• —hub_always_push: False
• —hub_revision: None
• —gradient_checkpointing: False
• —gradient_checkpointing_kwargs: None
• —include_inputs_for_metrics: False
• —include_for_metrics: []
• —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
• —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
• —use_liger_kernel: False
• —liger_kernel_config: None
• —eval_use_gather_object: False
• —average_tokens_across_devices: False
• —prompts: None
• —batch_sampler: batch_sampler
• —multi_dataset_batch_sampler: proportional

</details>

Training Logs

<details><summary>Click to expand</summary>

• —The bold row denotes the saved checkpoint. </details>

Framework Versions

• —Python: 3.11.13
• —Sentence Transformers: 4.1.0
• —Transformers: 4.53.3
• —PyTorch: 2.6.0+cu124
• —Accelerate: 1.9.0
• —Datasets: 4.4.1
• —Tokenizers: 0.21.2

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},
}

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