Modelpublic

HassanCS/TCRa_HLA_peptide_ESM

sourceHugging Faceupdated 2y agoView on Hugging Face

0likes19downloads

Model Card

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
—Maximum Sequence Length: 1026 tokens
—Output Dimensionality: 320 dimensions
—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': 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:

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("HassanCS/TCRa_HLA_peptide_ESM")
# Run inference
sentences = [
    'A Q K V T Q A Q T E I S V V E K E D V T L D C V Y E T R D T T Y Y L F W Y K Q P P S G E L V F L I R R N S F D E Q N E I S G R Y S W N F Q K S T S S F N F T I T A S Q V V D S A V Y F C C A L L Y N N N D M R F F G A G T R L T V K P N',
    'A Q K V T Q A Q T E I S V V E K E D V T L D C V Y E T R D T T Y Y L F W Y K Q P P S G E L V F L I R R N S F D E Q N E I S G R Y S W N F Q K S T S S F N F T I T A S Q V V D S A V Y F C C A L S E T P R G G G T S Y G K L T F F G Q G T I L T V H P N',
    'Q K E V E Q N S G P L S V P E G A I A S L N C T Y S D R G S Q S F F W Y R Q Y S G K S P E L I M F I Y S N G D K E D G R F T A Q L N K A S Q Y V S L L I R D S Q P S D S A T Y L C C A V A D D K I I F F G K G T R L H I L P N',
]
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)

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Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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

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Evaluation

Metrics

Semantic Similarity

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

Metric	Value
pearson_cosine	0.8254
spearman_cosine	0.8706

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Training Details

Training Dataset

Unnamed Dataset

—Size: 753,444 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: 108 tokens</li><li>mean: 116.0 tokens</li><li>max: 126 tokens</li></ul> | <ul><li>min: 107 tokens</li><li>mean: 116.16 tokens</li><li>max: 126 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.38</li><li>max: 0.97</li></ul> |
—Samples: | sentence1 | sentence2 | score | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------| | <code>T Q L L E Q S P Q F L S I Q E G E N L T V Y C N S S S V F S S L Q W Y R Q E P G E G P V L L V T V V T G G E V K K L K R L T F Q F G D A R K D S S L H I T A A Q P G D T G L Y L C C A G A G G G S Q G N L I F F G K G T K L S V K P N</code> | <code>T Q L L E Q S P Q F L S I Q E G E N L T V Y C N S S S V F S S L Q W Y R Q E P G E G P V L L V T V V T G G E V K K L K R L T F Q F G D A R K D S S L H I T A A Q P G D T G L Y L C C A G G N G G S Q G N L I F F G K G T K L S V K P N</code> | <code>0.8347107438016529</code> | | <code>A Q T V T Q S Q P E M S V Q E A E T V T L S C T Y D T S E N N Y Y L F W Y K Q P P S R Q M I L V I R Q E A Y K Q Q N A T E N R F S V N F Q K A A K S F S L K I S D S Q L G D T A M Y F C A F A E Y G N K L V F G A G T I L R V K S Y</code> | <code>A Q T V T Q S Q P E M S V Q E A E T V T L S C T Y D T S E S D Y Y L F W Y K Q P P S R Q M I L V I R Q E A Y K Q Q N A T E N R F S V N F Q K A A K S F S L K I S D S Q L G D A A M Y F C A L F S G S R L T F G E G T Q L T V N P D</code> | <code>0.0</code> | | <code>A Q K V T Q A Q T E I S V V E K E D V T L D C V Y E T R D T T Y Y L F W Y K Q P P S G E L V F L I R R N S F D E Q N E I S G R Y S W N F Q K S T S S F N F T I T A S Q V V D S A V Y F C C A L L I F S G G Y N K L I F F G A G T R L A V H P Y</code> | <code>A Q K V T Q A Q T E I S V V E K E D V T L D C V Y E T R D T T Y Y L F W Y K Q P P S G E L V F L I R R N S F D E Q N E I S G R Y S W N F Q K S T S S F N F T I T A S Q V V D S A V Y F C C A L S E A G S G Y S T L T F F G K G T M L L V S P D</code> | <code>0.4008264462809917</code> |
—Loss: <code>CoSENTLoss</code> with these parameters:

json

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

Evaluation Dataset

Unnamed Dataset

—Size: 83,716 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: 106 tokens</li><li>mean: 116.08 tokens</li><li>max: 126 tokens</li></ul> | <ul><li>min: 109 tokens</li><li>mean: 116.05 tokens</li><li>max: 125 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.39</li><li>max: 0.97</li></ul> |
—Samples: | sentence1 | sentence2 | score | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------| | <code>G E N V E Q H P S T L S V Q E G D S A V I K C T Y S D S A S N Y F P W Y K Q E L G K G P Q L I I D I R S N V G E K K D Q R I A V T L N K T A K H F S L H I T E T Q P E D S A V Y F C A A S M N N Y G Q N F V F G P G T R L S V L P Y</code> | <code>G E D V E Q S L F L S V R E G D S S V I N C T Y T D S S S T Y L Y W Y K Q E P G A G L Q L L T Y I F S N M D M K Q D Q R L T V L L N K K D K H L S L R I A D T Q T G D S A I Y F C A E R A G A N N L F F G T G T R L T V I P Y</code> | <code>0.09297520661157023</code> | | <code>A Q T V T Q S Q P E M S V Q E A E T V T L S C T Y D T S E N N Y Y L F W Y K Q P P S R Q M I L V I R Q E A Y K Q Q N A T E N R F S V N F Q K A A K S F S L K I S D S Q L G D T A M Y F C C A S H M N N A R L M F F G D G T Q L V V K P N</code> | <code>A Q T V T Q S Q P E M S V Q E A E T V T L S C T Y D T S E N N Y Y L F W Y K Q P P S R Q M I L V I R Q E A Y K Q Q N A T E N R F S V N F Q K A A K S F S L K I S D S Q L G D T A M Y F C C S S G G G A D G L T F F G K G T H L I I Q P Y</code> | <code>0.00826446280991735</code> | | <code>G Q S L E Q P S E V T A V E G A I V Q I N C T Y Q T S G F Y G L S W Y Q Q H D G G A P T F L S Y N A L D G L E E T G R F S S F L S R S D S Y G Y L L L Q E L Q M K D S A S Y F C C A L A G G G N K L T F F G T G T Q L K V E L N</code> | <code>K N Q V E Q S P Q S L I I L E G K N C T L Q C N Y T V S P F S N L R W Y K Q D T G R G P V S L T I M T F S E N T K S N G R Y T A T L D A D T K Q S S L H I T A S Q L S D S A S Y I C C V V S S Y S S A S K I I F F G S G T R L S I R P N</code> | <code>0.9690082644628099</code> |
—Loss: <code>CoSENTLoss</code> with these parameters:

json

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

Training Hyperparameters

Non-Default Hyperparameters

—eval_strategy: steps
—per_device_train_batch_size: 128
—per_device_eval_batch_size: 128
—learning_rate: 0.001
—weight_decay: 0.0001
—num_train_epochs: 2
—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: steps
—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: 2
—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
—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
—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
—use_liger_kernel: False
—eval_use_gather_object: False
—average_tokens_across_devices: False
—prompts: None
—batch_sampler: batch_sampler
—multi_dataset_batch_sampler: proportional

</details>

Training Logs

Epoch	Step	Training Loss	Validation Loss	all-dev_spearman_cosine
0.3397	2000	8.8932	8.8505	0.5332
0.6795	4000	8.8096	8.7699	0.6565
1.0192	6000	8.7188	8.6631	0.7476
1.3589	8000	8.592	8.5352	0.8242
1.6987	10000	8.4614	8.4169	0.8706

—The bold row denotes the saved checkpoint.

Framework Versions

—Python: 3.10.12
—Sentence Transformers: 3.3.1
—Transformers: 4.47.0
—PyTorch: 2.5.1+cu121
—Accelerate: 1.2.1
—Datasets: 3.3.1
—Tokenizers: 0.21.0

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",
}

CoSENTLoss

bibtex

@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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