HassanCS / chemberta-clintox-tunned-3

SentenceTransformer based on DeepChem/ChemBERTa-77M-MLM

This is a sentence-transformers model finetuned from DeepChem/ChemBERTa-77M-MLM . It maps sentences & paragraphs to a 384-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: DeepChem/ChemBERTa-77M-MLM
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 384 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': 512, 'do_lower_case': False}) with Transformer model: RobertaModel 
  (1): Pooling({'word_embedding_dimension': 384, '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/chemberta-clintox-tunned-3")
# Run inference
sentences = [
    'C[NH+](C)CCc1c[nH]c2ccc(Cn3cncn3)cc12',
    'C[N+]12CCC(CC1)C(OC(=O)C(O)(c1ccccc1)c1ccccc1)C2',
    'CC12CCCCCC(Cc3ccc(O)cc31)C2[NH3+]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

Evaluation

Metrics

Binary Classification

• Dataset: all-dev
• Evaluated with BinaryClassificationEvaluator

Training Details

Training Dataset

Unnamed Dataset

• Size: 4,000 training samples
• Columns: smiles1 , smiles2 , and label
• Approximate statistics based on the first 1000 samples:

  	smiles1	smiles2	label
  type	string	string	int
  details	min: 3 tokens mean: 46.57 tokens max: 221 tokens	min: 3 tokens mean: 54.2 tokens max: 221 tokens	0: ~49.40% 1: ~50.60%

• Samples:

  smiles1	smiles2	label
  C=C1CCC(O)CC1=CC=C1CCCC2(C)C1CCC2C(C)C=CC(C)C(C)C	CCCCCc1cc(O)c2c(c1)OC(C)(C)C1CCC(C)=CC21	1
  CNC1=[NH+]c2ccc(Cl)cc2C(c2ccccc2)= N+ C1	O=C1CCC(N2C(=O)c3ccccc3C2=O)C(=O)N1	0
  CC(=O)Oc1c(C)c(C)c2c(c1C)CCC(C)(CCCC(C)CCCC(C)CCCC(C)C)O2	Nc1nc(Cl)nc2c1ncn2C1OC(CO)C(O)C1F	0

• Loss: ContrastiveLoss with these parameters:

  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.5,
      "size_average": true
  }
  

Evaluation Dataset

Unnamed Dataset

• Size: 2,008 evaluation samples
• Columns: smiles1 , smiles2 , and label
• Approximate statistics based on the first 1000 samples:

  	smiles1	smiles2	label
  type	string	string	int
  details	min: 18 tokens mean: 56.98 tokens max: 244 tokens	min: 18 tokens mean: 65.99 tokens max: 244 tokens	0: ~47.90% 1: ~52.10%

• Samples:

  smiles1	smiles2	label
  CC(C)(C)NC(=O)C1CC2CCCCC2C[NH+]1CC(O)C(Cc1ccccc1)NC(=O)C(CC(N)=O)NC(=O)c1ccc2ccccc2n1	C NH+ CCc1c[nH]c2ccc(Cn3cncn3)cc12	1
  CC12CC(=C[O-])C(=O)CC1CCC1C2CCC2(C)C1CCC2(C)O	CC(C)C1(C(=O)NC2CC(=O)OC2(O)CF)CC(c2nccc3ccccc23)=NO1	0
  CC12CCCCCC(Cc3ccc(O)cc31)C2[NH3+]	C[NH2+]C1C(O)C([NH2+]C)C2OC3(O)C(=O)CC(C)OC3OC2C1O	1

• Loss: ContrastiveLoss with these parameters:

  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.5,
      "size_average": true
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy : steps
• per_device_train_batch_size : 32
• per_device_eval_batch_size : 32
• learning_rate : 1e-05
• weight_decay : 0.01
• num_train_epochs : 5
• warmup_ratio : 0.1
• fp16 : True
• load_best_model_at_end : True
• hub_model_id : HassanCS/chemberta-clintox-tunned-3
• batch_sampler : no_duplicates

All Hyperparameters

Training Logs

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.11.11
• Sentence Transformers: 3.4.1
• Transformers: 4.47.1
• PyTorch: 2.5.1+cu124
• Accelerate: 1.2.1
• Datasets: 3.2.0
• Tokenizers: 0.21.0

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",
}

ContrastiveLoss

@inproceedings{hadsell2006dimensionality,
    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
    title={Dimensionality Reduction by Learning an Invariant Mapping},
    year={2006},
    volume={2},
    number={},
    pages={1735-1742},
    doi={10.1109/CVPR.2006.100}
}