sentence-transformers safetensors roberta sentence-similarity feature-extraction generated_from_trainer dataset_size:10000 loss:ContrastiveLoss arxiv:1908.10084 base_model:DeepChem/ChemBERTa-77M-MLM base_model:finetune:DeepChem/ChemBERTa-77M-MLM model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: DeepChem/ChemBERTa-77M-MLM
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:10000
- loss:ContrastiveLoss
widget:
- source_sentence: CC(C)N=c1cc2n(-c3ccc(Cl)cc3)c3ccccc3nc-2cc1Nc1ccc(Cl)cc1
  sentences:
  - C[NH+]1CCC(=C2c3ccccc3CCn3c(C=O)c[nH+]c32)CC1
  - COc1ccc(C(=O)CC(=O)c2ccc(C(C)(C)C)cc2)cc1
  - CC1CNc2c(cccc2S(=O)(=O)NC(CCC[NH+]=C(N)N)C(=O)N2CCC(C)CC2C(=O)[O-])C1
- source_sentence: CC(C)c1ccc2oc3nc(N)c(C(=O)[O-])cc3c(=O)c2c1
  sentences:
  - COC1=CC(=O)CC(C)C12Oc1c(Cl)c(OC)cc(OC)c1C2=O
  - CON=C(C(=O)NC1C(=O)N2C(C(=O)[O-])=C(C[N+]3(C)CCCC3)CSC12)c1csc(N)n1
  - CC1C=CC=CC=CC=CC=CC=CC=CC(OC2OC(C)C(O)C([NH3+])C2O)CC2OC(O)(CC(O)CC(O)C(O)CCC(O)CC(O)CC(=O)OC(C)C(C)C1O)CC(O)C2C(=O)[O-]
- source_sentence: C[NH2+]C1CCc2[nH]c3ccc(C(N)=O)cc3c2C1
  sentences:
  - CC(OC(=O)c1ccccc1)C1=CCC23OCC[NH+](C)CC12CC(O)C12OC4(O)CCC1(C)C(CC=C32)C4
  - CC(=O)NC(Cc1ccc2ccccc2c1)C(=O)NC(Cc1ccc(Cl)cc1)C(=O)NC(Cc1cccnc1)C(=O)NC(CO)C(=O)NC(Cc1ccc(NC(=O)C2CC(=O)NC(=O)N2)cc1)C(=O)NC(Cc1ccc(NC(N)=O)cc1)C(=O)NC(CC(C)C)C(=O)NC(CCCC[NH2+]C(C)C)C(=O)N1CCCC1C(=O)NC(C)C(N)=O
  - C[NH+](C)CCOC(=O)C(c1ccccc1)C1(O)CCCC1
- source_sentence: CC(C)n1c(C=CC(O)CC(O)CC(=O)[O-])c(-c2ccc(F)cc2)c2ccccc21
  sentences:
  - C#CC1(O)CCC2C3CCC4=C(CCC(=O)C4)C3CCC21C
  - CC(C=CC(C)C(C)(C)O)C1CCC2C(=CC=C3CC(O)CC(O)C3)CCCC21C
  - CC(C)CNCc1ccc(-c2ccccc2S(=O)(=O)N2CCCC2)cc1
- source_sentence: CC#CCn1c(N2CCCC([NH3+])C2)nc2c1c(=O)n(Cc1nc(C)c3ccccc3n1)c(=O)n2C
  sentences:
  - C[N+]1(C)CCCC(OC(=O)C(O)(c2ccccc2)c2ccccc2)C1
  - CC(Cc1ccc(O)c(O)c1)C(C)Cc1ccc(O)c(O)c1
  - CC12CCC(=O)C=C1CCC1C2C(O)CC2(C)C1CCC2(O)C(=O)COC(=O)CCC1CCCC1
model-index:
- name: SentenceTransformer based on DeepChem/ChemBERTa-77M-MLM
  results:
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: all dev
      type: all-dev
    metrics:
    - type: cosine_accuracy
      value: 0.9066
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.5664876699447632
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.9510122731564041
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.5664876699447632
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.9067813562712542
      name: Cosine Precision
    - type: cosine_recall
      value: 0.9997794441993825
      name: Cosine Recall
    - type: cosine_ap
      value: 0.9523113003188102
      name: Cosine Ap
---

# SentenceTransformer based on DeepChem/ChemBERTa-77M-MLM

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [DeepChem/ChemBERTa-77M-MLM](https://huggingface.co/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](https://huggingface.co/DeepChem/ChemBERTa-77M-MLM) <!-- at revision ed8a5374f2024ec8da53760af91a33fb8f6a15ff -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **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}) 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:

```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("HassanCS/chemBERTa-tuned-on-ClinTox-3")
# Run inference
sentences = [
    'CC#CCn1c(N2CCCC([NH3+])C2)nc2c1c(=O)n(Cc1nc(C)c3ccccc3n1)c(=O)n2C',
    'CC12CCC(=O)C=C1CCC1C2C(O)CC2(C)C1CCC2(O)C(=O)COC(=O)CCC1CCCC1',
    'CC(Cc1ccc(O)c(O)c1)C(C)Cc1ccc(O)c(O)c1',
]
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]
```

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## Evaluation

### Metrics

#### Binary Classification

* Dataset: `all-dev`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

| Metric                    | Value      |
|:--------------------------|:-----------|
| cosine_accuracy           | 0.9066     |
| cosine_accuracy_threshold | 0.5665     |
| cosine_f1                 | 0.951      |
| cosine_f1_threshold       | 0.5665     |
| cosine_precision          | 0.9068     |
| cosine_recall             | 0.9998     |
| **cosine_ap**             | **0.9523** |

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 10,000 training samples
* Columns: <code>smiles1</code>, <code>smiles2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | smiles1                                                                            | smiles2                                                                            | label                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                             | string                                                                             | int                                             |
  | details | <ul><li>min: 3 tokens</li><li>mean: 40.69 tokens</li><li>max: 221 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 51.43 tokens</li><li>max: 221 tokens</li></ul> | <ul><li>0: ~14.90%</li><li>1: ~85.10%</li></ul> |
* Samples:
  | smiles1                                 | smiles2                                                      | label          |
  |:----------------------------------------|:-------------------------------------------------------------|:---------------|
  | <code>Cn1c(=O)c2c(ncn2C)n(C)c1=O</code> | <code>Cc1cc2c(s1)=Nc1ccccc1NC=2N1CC[NH+](C)CC1</code>        | <code>1</code> |
  | <code>Oc1ccc(OCc2ccccc2)cc1</code>      | <code>Oc1ccc(CCCC[NH2+]CC(O)c2ccc(O)c(O)c2)cc1</code>        | <code>1</code> |
  | <code>OCC(S)CS</code>                   | <code>CC12CCC(=O)C=C1CCC1C2C(O)CC2(C)C1CCC2(O)C(=O)CO</code> | <code>0</code> |
* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
  ```json
  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.5,
      "size_average": true
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset


* Size: 5,000 evaluation samples
* Columns: <code>smiles1</code>, <code>smiles2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | smiles1                                                                             | smiles2                                                                             | label                                           |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                              | string                                                                              | int                                             |
  | details | <ul><li>min: 18 tokens</li><li>mean: 56.96 tokens</li><li>max: 209 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 61.21 tokens</li><li>max: 244 tokens</li></ul> | <ul><li>0: ~10.00%</li><li>1: ~90.00%</li></ul> |
* Samples:
  | smiles1                                                                                                  | smiles2                                                            | label          |
  |:---------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------|:---------------|
  | <code>CC(=CC(=O)OCCCCCCCCC(=O)[O-])CC1OCC(CC2OC2C(C)C(C)O)C(O)C1O</code>                                 | <code>CC(C=CC(C)C(C)(C)O)C1CCC2C(=CC=C3CC(O)CC(O)C3)CCCC21C</code> | <code>1</code> |
  | <code>C=C1c2cccc([O-])c2C(=O)C2=C([O-])C3(O)C(=O)C(C(N)=O)=C([O-])C([NH+](C)C)C3C(O)C12</code>           | <code>CC(c1ncncc1F)C(O)(Cn1cncn1)c1ccc(F)cc1F</code>               | <code>1</code> |
  | <code>CC(C)CC1C(=O)N2CCCC2C2(O)OC(NC(=O)C3C=C4c5cccc6[nH]c(Br)c(c56)CC4[NH+](C)C3)(C(C)C)C(=O)N12</code> | <code>C[NH+](C)CCC=C1c2ccccc2Sc2ccc(Cl)cc21</code>                 | <code>1</code> |
* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
  ```json
  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.5,
      "size_average": true
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 5
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 5
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `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`: []
- `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
- `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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch | Step | Training Loss | Validation Loss | all-dev_cosine_ap |
|:-----:|:----:|:-------------:|:---------------:|:-----------------:|
| 0.8   | 500  | 0.0264        | 0.0112          | 0.9213            |
| 1.6   | 1000 | 0.0152        | 0.0122          | 0.9362            |
| 2.4   | 1500 | 0.0134        | 0.0128          | 0.9463            |
| 3.2   | 2000 | 0.0112        | 0.0134          | 0.9502            |
| 4.0   | 2500 | 0.01          | 0.0125          | 0.9513            |
| 4.8   | 3000 | 0.0097        | 0.0132          | 0.9523            |


### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.3.1
- 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
```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",
}
```

#### ContrastiveLoss
```bibtex
@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}
}
```

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