---
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](https://www.SBERT.net) model finetuned from [google-bert/bert-base-cased](https://huggingface.co/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](https://huggingface.co/google-bert/bert-base-cased) <!-- at revision cd5ef92a9fb2f889e972770a36d4ed042daf221e -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - csv
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### 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:

```bash
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]])
```

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## Training Details

### Training Dataset

#### csv

* Dataset: csv
* Size: 286,816 training samples
* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | premise                                                                            | hypothesis                                                                         | label                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                             | string                                                                             | int                                             |
  | details | <ul><li>min: 8 tokens</li><li>mean: 38.33 tokens</li><li>max: 213 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 37.78 tokens</li><li>max: 213 tokens</li></ul> | <ul><li>0: ~50.50%</li><li>2: ~49.50%</li></ul> |
* Samples:
  | premise                                                    | hypothesis                                              | label          |
  |:-----------------------------------------------------------|:--------------------------------------------------------|:---------------|
  | <code>NC(=O)[C@H](Cc1ccccc1)NC(=O)OCc1cc(=O)c(O)co1</code> | <code>CNC(=S)N/N=C(\C)c1ccc(OC)cc1O</code>              | <code>2</code> |
  | <code>CC/C(=N\NC(N)=S)c1ccc(C2CCCCC2)cc1</code>            | <code>COc1cccc(C(=O)N2CCN(Cc3ccc(F)cc3)CC2)c1</code>    | <code>2</code> |
  | <code>O=C(O)CSc1nnc(NC(=S)Nc2cccc(C(F)(F)F)c2)s1</code>    | <code>CCCCOc1cccc2c1C(=O)c1c(OCCCC)cc(CO)cc1C2=O</code> | <code>0</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)

### Evaluation Dataset

#### csv

* Dataset: csv
* Size: 50,615 evaluation samples
* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | premise                                                                            | hypothesis                                                                         | label                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                             | string                                                                             | int                                             |
  | details | <ul><li>min: 8 tokens</li><li>mean: 38.78 tokens</li><li>max: 213 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 39.23 tokens</li><li>max: 213 tokens</li></ul> | <ul><li>0: ~47.40%</li><li>2: ~52.60%</li></ul> |
* Samples:
  | premise                                                        | hypothesis                                                             | label          |
  |:---------------------------------------------------------------|:-----------------------------------------------------------------------|:---------------|
  | <code>O=Cc1ccoc1</code>                                        | <code>Cn1c2ccccc2c2cc(/C=C/C(=O)c3cccc(NC(=O)c4ccccc4F)c3)ccc21</code> | <code>2</code> |
  | <code>COc1cc(C=O)ccc1OC(=O)CN1CCN(C)CC1</code>                 | <code>Oc1ccc(O)cc1</code>                                              | <code>2</code> |
  | <code>O=C(c1cccc([N+](=O)[O-])c1)N1CCN(Cc2ccc(F)cc2)CC1</code> | <code>CNC(=S)N/N=C(\C)c1ccc(OC)cc1O</code>                             | <code>2</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#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",
}
```

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