SentenceTransformer based on facebook/esm2_t6_8M_UR50D

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)

Evaluation

Metrics

Semantic Similarity

• Dataset: all-dev
• Evaluated with EmbeddingSimilarityEvaluator

Training Details

Training Dataset

Unnamed Dataset

• Size: 528,048 training samples
• Columns: sentence1, sentence2, and score
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 108 tokens mean: 116.06 tokens max: 126 tokens	min: 107 tokens mean: 116.14 tokens max: 125 tokens	min: 0.0 mean: 0.37 max: 0.97

• Samples:

  sentence1	sentence2	score
  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	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	0.05165289256198347
  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	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	0.04132231404958678
  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	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	0.018595041322314043

• Loss: CoSENTLoss with these parameters:
  json

  1{ 2 "scale": 20.0, 3 "similarity_fct": "pairwise_cos_sim" 4}

Evaluation Dataset

Unnamed Dataset

• Size: 58,673 evaluation samples
• Columns: sentence1, sentence2, and score
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 107 tokens mean: 116.01 tokens max: 124 tokens	min: 106 tokens mean: 116.0 tokens max: 126 tokens	min: 0.0 mean: 0.39 max: 0.97

• Samples:

  sentence1	sentence2	score
  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	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	0.04132231404958678
  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	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	0.018595041322314043
  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	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	0.8347107438016529

• Loss: CoSENTLoss with these parameters:
  json

  1{ 2 "scale": 20.0, 3 "similarity_fct": "pairwise_cos_sim" 4}

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: 6
• fp16: True
• load_best_model_at_end: True

All Hyperparameters

Training Logs

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

CoSENTLoss