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.

python

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)[object Object]NC(=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([object Object][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

bibtex

@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",
}

	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%