HassanCS/TCRa_HLA_peptide_esm2_t6_8M_UR50D_up_to_epoch_8

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/TCRa_HLA_peptide_esm2_t6_8M_UR50D_up_to_epoch_8")
# Run inference
sentences = [
    'G E S V G L H L P T L S V Q E G D N S I I N C A Y S N S A S D Y F I W Y K Q E S G K G P Q F I I D I R S N M D K R Q G Q R V T V L L N K T V K H L S L Q I A A T Q P G D S A V Y F C C A E I W D Y G Q N F V F F G P G T R L S V L P Y',
    'R K E V E Q D P G P F N V P E G A T V A F N C T Y S N S A S Q S F F W Y R Q D C R K E P K L L M S V Y S S G N E D G R F T A Q L N R A S Q Y I S L L I R D S K L S D S A T Y L C C V V I K A A G N K L T F F G G G T R V L V K P N',
    'G Q N I D Q P T E M T A T E G A I V Q I N C T Y Q T S G F N G L F W Y Q Q H A G E A P T F L S Y N V L D G L E E K G R F S S F L S R S K G Y S Y L L L K E L Q M K D S A S Y L C A V R E G G G A D G L T F G K G T H L I I Q P Y',
]
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>

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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>

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Training Details

Training Dataset

Unnamed Dataset

• —Size: 528,048 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: 116.06 tokens</li><li>max: 126 tokens</li></ul> | <ul><li>min: 107 tokens</li><li>mean: 116.14 tokens</li><li>max: 125 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.37</li><li>max: 0.97</li></ul> |
• —Samples: | sentence1 | sentence2 | score | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------| | <code>A Q S V S Q H N H H V I L S E A A S L E L G C N Y S Y G G T V N L F W Y V Q Y P G Q H L Q L L L K Y F S G D P L V K G I K G F E A E F I K S K F S F N L R K P S V Q W S D T A E Y F C A V N A R R N T P L V F G K G T R L S V I A N</code> | <code>A Q S V S Q H N H H V I L S E A A S L E L G C N Y S Y G G T V N L F W Y V Q Y P G Q H L Q L L L K Y F S G D P L V K G I K G F E A E F I K S K F S F N L R K P S V Q W S D T A E Y F C A V T S G R G S Q G N L I F G K G T K L S V K P N</code> | <code>0.05165289256198347</code> | | <code>K Q E V T Q I P A A L S V P E G E N L V L N C S F T D S A I Y N L Q W F R Q D P G K G L T S L L L I Q S S Q R E Q T S G R L N A S L D K S S G R S T L Y I A A S Q P G D S A T Y L C C A V N S V S G A G S Y Q L T F F G K G T K L S V I P N</code> | <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 C A A N N Q G G K L I F F G Q G T E L S V K P N</code> | <code>0.04132231404958678</code> | | <code>K Q E V T Q I P A A L S V P E G E N L V L N C S F T D S A I Y N L Q W F R Q D P G K G L T S L L L I Q S S Q R E Q T S G R L N A S L D K S S G R S T L Y I A A S Q P G D S A T Y L C C A V A G G T S Y G K L T F F G Q G T I L T V H P N</code> | <code>K Q E V T Q I P A A L S V P E G E N L V L N C S F T D S A I Y N L Q W F R Q D P G K G L T S L L L I Q S S Q R E Q T S G R L N A S L D K S S G R S T L Y I A A S Q P G D S A T Y L C C A V N S P G S G A G S Y Q L T F F G K G T K L S V I P N</code> | <code>0.018595041322314043</code> |
• —Loss: <code>CoSENTLoss</code> with these parameters:

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

Evaluation Dataset

Unnamed Dataset

• —Size: 58,673 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: 107 tokens</li><li>mean: 116.01 tokens</li><li>max: 124 tokens</li></ul> | <ul><li>min: 106 tokens</li><li>mean: 116.0 tokens</li><li>max: 126 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>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 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 L W S G G G A D G L T F G K G T H L I I Q P Y</code> | <code>S Q Q G E E D P Q A L S I Q E G E N A T M N C S Y K T S I N N L Q W Y R Q N S G R G L V H L I L I R S N E R E K H S G R L R V T L D T S K K S S S L L I T A S R A A D T A S Y F C A R S R N K Q G G I F F F G Q G T E L S V K P N</code> | <code>0.04132231404958678</code> | | <code>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 T T Q G G S E K L V F F G K G T K L T V N 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 C A E D K D A R L M F F G D G T Q L V V K P N</code> | <code>0.018595041322314043</code> | | <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 C A A S I G Q G G K L I F F G Q G T E L S V K P N</code> | <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 C A A S N P G G G N K L T F F G T G T Q L K V E L N</code> | <code>0.8347107438016529</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: 8
• —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: 8
• —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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