README.md

metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dense
  - generated_from_trainer
  - dataset_size:286816
  - loss:SoftmaxLoss
base_model: google-bert/bert-base-cased
widget:
  - source_sentence: CC(C)C[C@H](NC(=O)[C@@H](N)Cc1ccccc1)C(=O)NCc1cc(=O)c(O)c[nH]1
    sentences:
      - CC(=O)N1CCC(Cc2ccc(F)cc2)CC1
      - C=CC(C)(C)c1cc(CCCc2cc(O)c(O)c(CC3OC3(C)C)c2CC=C(C)C)c(O)cc1O
      - COc1cc([N+](=O)[O-])ccc1/C=C/C(=N\O)c1cc2ccccc2cc1O
  - source_sentence: O=C(OCc1ccc(O)cc1)c1cc(O)c(O)c(O)c1
    sentences:
      - COc1ccc(/C=C/C(=O)NCCCNC(=O)/C=C/c2ccc(OC)c(O)c2)cc1O
      - CCCCCCCCSCc1cc(=O)c(O)co1
      - O=C(NCCc1c[nH]c2ccc(O)cc12)c1ccc(O)cc1O
  - source_sentence: O=C(/C=C/c1ccc(O)cc1)c1ccc(NS(=O)(=O)c2ccc([N+](=O)[O-])cc2)cc1
    sentences:
      - Nc1ccc(S(=O)(=O)Nc2ccc(C(=O)/C=C/c3ccc(O)cc3)cc2)cc1
      - O=C(NO)Nc1ccc(O)cc1
      - COc1ccc(C(C)=O)c(OC(=O)/C=C/c2ccc(F)cc2)c1
  - source_sentence: O=C(c1ccc2ccccc2c1)N1CCC(N2CCCCC2)CC1
    sentences:
      - N[C@@H](Cc1ccccc1)C(=O)N[C@@H](Cc1ccccc1)C(=O)OCc1cc(=O)c(O)c[nH]1
      - '[C-]#N'
      - COc1ccc(/C=C/C(=O)NCCCNC(=O)/C=C/c2ccc(OC)c(O)c2)cc1O
  - source_sentence: NC(=S)c1cccnc1
    sentences:
      - COc1ccc(/C=C/C(=N\O)c2cc3ccccc3cc2O)c(OC)c1
      - C/C(=N\NC(N)=S)c1cccc(NC(=O)C(F)(F)F)c1
      - Cc1ccc(C(C)C)c(OC(=O)/C=C/c2ccc(O)cc2)c1
pipeline_tag: sentence-similarity
library_name: sentence-transformers

SentenceTransformer based on google-bert/bert-base-cased

This is a sentence-transformers model finetuned from google-bert/bert-base-cased on the csv dataset. It maps sentences & paragraphs to a 768-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: google-bert/bert-base-cased
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • csv

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, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 768, '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("Jimmy-Ooi/Tyrisonase_test_model")
# Run inference
sentences = [
    'NC(=S)c1cccnc1',
    'Cc1ccc(C(C)C)c(OC(=O)/C=C/c2ccc(O)cc2)c1',
    'C/C(=N\\NC(N)=S)c1cccc(NC(=O)C(F)(F)F)c1',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.9019, 0.8925],
#         [0.9019, 1.0000, 0.9356],
#         [0.8925, 0.9356, 1.0000]])

Training Details

Training Dataset

csv

• Dataset: csv
• Size: 286,816 training samples
• Columns: premise, hypothesis, and label
• Approximate statistics based on the first 1000 samples:

  	premise	hypothesis	label
  type	string	string	int
  details	min: 8 tokens mean: 38.33 tokens max: 213 tokens	min: 8 tokens mean: 37.78 tokens max: 213 tokens	0: ~50.50% 2: ~49.50%

• Samples:

  premise	hypothesis	label
  NC(=O)C@HNC(=O)OCc1cc(=O)c(O)co1	CNC(=S)N/N=C(\C)c1ccc(OC)cc1O	2
  CC/C(=N\NC(N)=S)c1ccc(C2CCCCC2)cc1	COc1cccc(C(=O)N2CCN(Cc3ccc(F)cc3)CC2)c1	2
  O=C(O)CSc1nnc(NC(=S)Nc2cccc(C(F)(F)F)c2)s1	CCCCOc1cccc2c1C(=O)c1c(OCCCC)cc(CO)cc1C2=O	0

• Loss: SoftmaxLoss

Evaluation Dataset

csv

• Dataset: csv
• Size: 50,615 evaluation samples
• Columns: premise, hypothesis, and label
• Approximate statistics based on the first 1000 samples:

  	premise	hypothesis	label
  type	string	string	int
  details	min: 8 tokens mean: 38.78 tokens max: 213 tokens	min: 8 tokens mean: 39.23 tokens max: 213 tokens	0: ~47.40% 2: ~52.60%

• Samples:

  premise	hypothesis	label
  O=Cc1ccoc1	Cn1c2ccccc2c2cc(/C=C/C(=O)c3cccc(NC(=O)c4ccccc4F)c3)ccc21	2
  COc1cc(C=O)ccc1OC(=O)CN1CCN(C)CC1	Oc1ccc(O)cc1	2
  O=C(c1cccc(N+[O-])c1)N1CCN(Cc2ccc(F)cc2)CC1	CNC(=S)N/N=C(\C)c1ccc(OC)cc1O	2

• Loss: SoftmaxLoss

Framework Versions

• Python: 3.12.11
• Sentence Transformers: 5.1.1
• Transformers: 4.56.1
• PyTorch: 2.8.0+cu126
• Accelerate: 1.10.1
• Datasets: 4.0.0
• Tokenizers: 0.22.0

Citation

BibTeX

Sentence Transformers and SoftmaxLoss

@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",
}