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--- |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:753444 |
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- loss:CoSENTLoss |
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base_model: facebook/esm2_t6_8M_UR50D |
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widget: |
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- source_sentence: 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 |
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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 |
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A K S F S L K I S D S Q L G D A A M Y F C C A Y R S M S N Y Q L I W W G A G T |
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K L I I K P D |
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sentences: |
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- 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 |
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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 |
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I S D S Q L G D T A M Y F C C A F V A N A G G T S Y G K L T F F G Q G T I L T |
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V H P N |
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- 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 |
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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 |
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I S D S Q L G D A A M Y F C C A Y R S P N Y G G S Q G N L I F F G K G T K L S |
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V K P N |
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- A Q S V A Q P E D Q V N V A E G N P L T V K C T Y S V S G N P Y L F W Y V Q Y |
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P N R G L Q F L L K Y I T G D N L V K G S Y G F E A E F N K S Q T S F H L K K |
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P S A L V S D S A L Y F C A L D Q A G T A L I F G K G T T L S V S S N |
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- source_sentence: L A K T T Q P I S M D S Y E G Q E V N I T C S H N N I A T N D Y |
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I T W Y Q Q F P S Q G P R F I I Q G Y K T K V T N E V A S L F I P A D R K S S |
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T L S L P R V S L S D T A V Y Y C C L P S G M N Y G G S Q G N L I F F G K G T |
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K L S V K P N |
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sentences: |
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- I L N V E Q S P Q S L H V Q E G D S T N F T C S F P S S N F Y A L H W Y R W E |
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T A K S P E A L F V M T L N G D E K K K G R I S A T L N T K E G Y S Y L Y I K |
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G S Q P E D S A T Y L C A F I T G N Q F Y F G T G T S L T V I P N |
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- A Q K I T Q T Q P G M F V Q E K E A V T L D C T Y D T S D P S Y G L F W Y K Q |
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P S S G E M I F L I Y Q G S Y D Q Q N A T E G R Y S L N F Q K A R K S A N L V |
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I S A S Q L G D S A M Y F C C A M R G D A G G T S Y G K L T F F G Q G T I L T |
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V H P N |
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- Q K E V E Q D P G P L S V P E G A I V S L N C T Y S N S A F Q Y F M W Y R Q Y |
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S R K G P E L L M Y T Y S S G N K E D G R F T A Q V D K S S K Y I S L F I R D |
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S Q P S D S A T Y L C C A M R V I G S D D K I I F F G K G T R L H I L P N |
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- source_sentence: 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 |
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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 |
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S S L H I T A A Q P G D T G L Y L C C A G V P Y N N N D M R F F G A G T R L T |
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V K P N |
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sentences: |
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- 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 |
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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 |
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A Q P G D T G L Y L C C A G A A H P L N Y G G S Q G N L I F F G K G T K L S V |
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K P N |
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- G N S V T Q M E G P V T L S E E A F L T I N C T Y T A T G Y P S L F W Y V Q Y |
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P G E G L Q L L L K A T K A D D K G S N K G F E A T Y R K E T T S F H L E K G |
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S V Q V S D S A V Y F C C A F N D Y K L S F F G A G T T V T V R A N |
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- D A K T T Q P P S M D C A E G R A A N L P C N H S T I S G N E Y V Y W Y R Q I |
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H S Q G P Q Y I I H G L K N N E T N E M A S L I I T E D R K S S T L I L P H A |
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T L R D T A V Y Y C C I V R A G G G G W S G G G A D G L T F F G K G T H L I I |
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Q P Y |
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- source_sentence: L A K T T Q P I S M D S Y E G Q E V N I T C S H N N I A T N D Y |
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I T W Y Q Q F P S Q G P R F I I Q G Y K T K V T N E V A S L F I P A D R K S S |
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T L S L P R V S L S D T A V Y Y C C L V G E G P S G G Y Q K V T F F G I G T K |
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L Q V I P N |
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sentences: |
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- 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 |
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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 |
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I T A S Q V V D S A V Y F C C A L S D A Y N F N K F Y F F G S G T K L N V K P |
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N |
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- A Q R V T Q P E K L L S V F K G A P V E L K C N Y S Y S G S P E L F W Y V Q Y |
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S R Q R L Q L L L R H I S R E S I K G F T A D L N K G E T S F H L K K P F A Q |
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E E D S A M Y Y C A L R A R G S T L G R L Y F G R G T Q L T V W P D |
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- Q K E V E Q D P G P L S V P E G A I V S L N C T Y S N S A F Q Y F M W Y R Q Y |
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S R K G P E L L M Y T Y S S G N K E D G R F T A Q V D K S S K Y I S L F I R D |
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S Q P S D S A T Y L C C A M R G Y Q K V T F F G I G T K L Q V I P N |
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- source_sentence: 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 |
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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 |
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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 |
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L T V K P N |
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sentences: |
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- 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 |
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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 |
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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 |
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L T V H P N |
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- 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 |
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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 |
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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 |
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- 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 |
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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 |
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Q M K D S A S Y F C A V S P Y G Q N F V F G P G T R L S V L P Y |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- pearson_cosine |
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- spearman_cosine |
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model-index: |
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- name: SentenceTransformer based on facebook/esm2_t6_8M_UR50D |
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results: |
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- task: |
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type: semantic-similarity |
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name: Semantic Similarity |
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dataset: |
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name: all dev |
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type: all-dev |
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metrics: |
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- type: pearson_cosine |
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value: 0.8253873350708476 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.8706098612115536 |
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name: Spearman Cosine |
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--- |
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# SentenceTransformer based on facebook/esm2_t6_8M_UR50D |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [facebook/esm2_t6_8M_UR50D](https://huggingface.co/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. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [facebook/esm2_t6_8M_UR50D](https://huggingface.co/facebook/esm2_t6_8M_UR50D) <!-- at revision c731040fcd8d73dceaa04b0a8e6329b345b0f5df --> |
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- **Maximum Sequence Length:** 1026 tokens |
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- **Output Dimensionality:** 320 dimensions |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 1026, 'do_lower_case': False}) with Transformer model: EsmModel |
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(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}) |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("HassanCS/TCRa_HLA_peptide_ESM") |
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# Run inference |
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sentences = [ |
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'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', |
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'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', |
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'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', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 320] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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## Evaluation |
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### Metrics |
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#### Semantic Similarity |
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* Dataset: `all-dev` |
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* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
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| Metric | Value | |
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|:--------------------|:-----------| |
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| pearson_cosine | 0.8254 | |
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| **spearman_cosine** | **0.8706** | |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 753,444 training samples |
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence1 | sentence2 | score | |
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|:--------|:-------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:----------------------------------------------------------------| |
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| type | string | string | float | |
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| 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> | |
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* Samples: |
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| sentence1 | sentence2 | score | |
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|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------| |
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| <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> | |
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| <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> | |
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| <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> | |
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* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "pairwise_cos_sim" |
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} |
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``` |
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### Evaluation Dataset |
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#### Unnamed Dataset |
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* Size: 83,716 evaluation samples |
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence1 | sentence2 | score | |
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|:--------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:----------------------------------------------------------------| |
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| type | string | string | float | |
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| 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> | |
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* Samples: |
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| sentence1 | sentence2 | score | |
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|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------| |
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| <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> | |
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| <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> | |
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| <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> | |
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* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "pairwise_cos_sim" |
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} |
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``` |
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|
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 128 |
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- `per_device_eval_batch_size`: 128 |
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- `learning_rate`: 0.001 |
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- `weight_decay`: 0.0001 |
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- `num_train_epochs`: 2 |
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- `fp16`: True |
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- `load_best_model_at_end`: True |
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|
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 128 |
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- `per_device_eval_batch_size`: 128 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 0.001 |
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- `weight_decay`: 0.0001 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 2 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.0 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: True |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: True |
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- `ignore_data_skip`: False |
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|
- `fsdp`: [] |
|
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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|
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
|
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
|
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- `optim_args`: None |
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|
- `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`: [] |
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|
- `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 |
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|
- `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 | |
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|:----------:|:---------:|:-------------:|:---------------:|:-----------------------:| |
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| 0.3397 | 2000 | 8.8932 | 8.8505 | 0.5332 | |
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| 0.6795 | 4000 | 8.8096 | 8.7699 | 0.6565 | |
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| 1.0192 | 6000 | 8.7188 | 8.6631 | 0.7476 | |
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| 1.3589 | 8000 | 8.592 | 8.5352 | 0.8242 | |
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| **1.6987** | **10000** | **8.4614** | **8.4169** | **0.8706** | |
|
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|
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|
* The bold row denotes the saved checkpoint. |
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|
|
|
|
### 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 |
|
|
|
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|
### BibTeX |
|
|
|
|
|
#### Sentence Transformers |
|
|
```bibtex |
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|
@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}, |
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|
url={https://kexue.fm/archives/8847}, |
|
|
} |
|
|
``` |
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