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chemBERTa-tuned-on-ClinTox-using-MultipleNegativesRankingLoss

Sentence Similarity
sentence-transformers
Safetensors
roberta
feature-extraction
Generated from Trainer
dataset_size:118400
loss:TripletLoss
Eval Results
text-embeddings-inference
Model card Files
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 ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:118400
- loss:TripletLoss
base_model: DeepChem/ChemBERTa-77M-MLM
widget:
- source_sentence: CC(C)C1CCC(C(=O)NC(Cc2ccccc2)C(=O)[O-])CC1
sentences:
- C[NH2+]CCCC1c2ccccc2C=Cc2ccccc21
- COC(=O)NC(C(=O)NC(Cc1ccccc1)C(O)CN(Cc1ccc(-c2ccccn2)cc1)NC(=O)C(NC(=O)OC)C(C)(C)C)C(C)(C)C
- CC1C=CC=CCCC=CC=CC=CC=CC(OC2OC(C)C(O)C([NH3+])C2O)CC(O)C(C(=O)[O-])C(O)CC(=O)CC(O)C(O)CCC(O)CC(O)CC(O)CC(=O)OC(C)C(C)C1O
- source_sentence: C[NH+]1CCCC1Cc1c[nH]c2ccc(CCS(=O)(=O)c3ccccc3)cc12
sentences:
- CC(C)CC([NH+](C)C)C1(c2ccc(Cl)cc2)CCC1
- CC(C)CNCc1ccc(-c2ccccc2S(=O)(=O)N2CCCC2)cc1
- CC(Oc1cc(-c2cnn(C3CC[NH2+]CC3)c2)cnc1N)c1c(Cl)ccc(F)c1Cl
- source_sentence: C[NH+]1C2CCC1CC(OC(c1ccccc1)c1ccccc1)C2
sentences:
- C[NH2+]C1C(O)C([NH2+]C)C2OC3(O)C(=O)CC(C)OC3OC2C1O
- C=CC1(C)CC(OC(=O)CSC2CC3CCC(C2)[NH+]3C)C2(C)C(C)CCC3(CCC(=O)C32)C(C)C1O
- CC(C)CC(NC(=O)C(CCc1ccccc1)NC(=O)CN1CCOCC1)C(=O)NC(Cc1ccccc1)C(=O)NC(CC(C)C)C(=O)C1(C)CO1
- source_sentence: CC(C)CC(NC(=O)C(Cc1ccc2ccccc2c1)NC(=O)C(Cc1ccc(O)cc1)NC(=O)C(CO)NC(=O)C(Cc1c[nH]c2ccccc12)NC(=O)C(Cc1c[nH]cn1)NC(=O)C1CCC(=O)N1)C(=O)NC(CCCNC(N)=[NH2+])C(=O)N1CCCC1C(=O)NCC(N)=O
sentences:
- C[NH2+]C1CCC(c2ccc(Cl)c(Cl)c2)c2ccccc21
- C=C1CC2CCC34CC5OC6C(OC7CCC(CC(=O)CC8C(CC9OC(CCC1O2)CC(C)C9=C)OC(CC(O)CN)C8OC)OC7C6O3)C5O4
- C[N+]1(C)CCC(=C(c2ccccc2)c2ccccc2)CC1
- source_sentence: CON=C(C(=O)NC1C(=O)N2C(C(=O)[O-])=C(C[N+]3(C)CCCC3)CSC12)c1csc(N)n1
sentences:
- CC1CNc2c(cccc2S(=O)(=O)NC(CCC[NH+]=C(N)N)C(=O)N2CCC(C)CC2C(=O)[O-])C1
- CC1C=CC=CCCC=CC=CC=CC=CC(OC2OC(C)C(O)C([NH3+])C2O)CC(O)C(C(=O)[O-])C(O)CC(=O)CC(O)C(O)CCC(O)CC(O)CC(O)CC(=O)OC(C)C(C)C1O
- CC(C)C1(C(=O)NC2CC(=O)OC2(O)CF)CC(c2nccc3ccccc23)=NO1
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
model-index:
- name: SentenceTransformer based on DeepChem/ChemBERTa-77M-MLM
results:
- task:
type: triplet
name: Triplet
dataset:
name: all dev
type: all-dev
metrics:
- type: cosine_accuracy
value: 0.7135134935379028
name: Cosine Accuracy
---
# 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-using-MultipleNegativesRankingLoss")
# Run inference
sentences = [
'CON=C(C(=O)NC1C(=O)N2C(C(=O)[O-])=C(C[N+]3(C)CCCC3)CSC12)c1csc(N)n1',
'CC1CNc2c(cccc2S(=O)(=O)NC(CCC[NH+]=C(N)N)C(=O)N2CCC(C)CC2C(=O)[O-])C1',
'CC(C)C1(C(=O)NC2CC(=O)OC2(O)CF)CC(c2nccc3ccccc23)=NO1',
]
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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You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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## Evaluation
### Metrics
#### Triplet
* Dataset: `all-dev`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.7135** |
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 118,400 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 20 tokens</li><li>mean: 33.0 tokens</li><li>max: 60 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 47.34 tokens</li><li>max: 212 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 53.88 tokens</li><li>max: 212 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:-------------------------------------------------------|:---------------------------------------------------------------------------------------------|:-----------------------------------------------------|
| <code>CC(C)CC(NC(=O)CNC(=O)c1cc(Cl)ccc1Cl)B(O)O</code> | <code>CC(=O)OC1CCC2(C)C(=CCC3C2CCC2(C)C(c4cccnc4)=CCC32)C1</code> | <code>CCOC(=O)c1ncn2c1CN(C)C(=O)c1cc(F)ccc1-2</code> |
| <code>CC(C)CC(NC(=O)CNC(=O)c1cc(Cl)ccc1Cl)B(O)O</code> | <code>COc1ccc(C(CN(C)C)C2(O)CCCCC2)cc1</code> | <code>C[NH2+]C1(C)C2CCC(C2)C1(C)C</code> |
| <code>CC(C)CC(NC(=O)CNC(=O)c1cc(Cl)ccc1Cl)B(O)O</code> | <code>CNC(=O)c1cc(Oc2ccc(NC(=O)Nc3ccc(Cl)c(C(F)(F)F)c3)cc2)ccn1.Cc1ccc(S(=O)(=O)O)cc1</code> | <code>Nc1ncnc2c1ncn2C1OC(CO)C(O)C1O</code> |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
```json
{
"distance_metric": "TripletDistanceMetric.EUCLIDEAN",
"triplet_margin": 5
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 1,480 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 18 tokens</li><li>mean: 54.07 tokens</li><li>max: 169 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 60.4 tokens</li><li>max: 244 tokens</li></ul> | <ul><li>min: 30 tokens</li><li>mean: 71.25 tokens</li><li>max: 141 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:------------------------------------------------------------------------|:------------------------------------------------------------|:-------------------------------------------------------------------------|
| <code>CC(C)OC(=O)CCCC=CCC1C(O)CC(O)C1C=CC(O)COc1cccc(C(F)(F)F)c1</code> | <code>CC12CCCCCC(Cc3ccc(O)cc31)C2[NH3+]</code> | <code>CC(C)C(CN1CCC(C)(c2cccc(O)c2)C(C)C1)NC(=O)C1Cc2ccc(O)cc2CN1</code> |
| <code>CC(C)OC(=O)CCCC=CCC1C(O)CC(O)C1C=CC(O)COc1cccc(C(F)(F)F)c1</code> | <code>COc1cc2c(cc1OC)C1CC(=O)C(CC(C)C)C[NH+]1CC2</code> | <code>CC(C)C(CN1CCC(C)(c2cccc(O)c2)C(C)C1)NC(=O)C1Cc2ccc(O)cc2CN1</code> |
| <code>CC(C)OC(=O)CCCC=CCC1C(O)CC(O)C1C=CC(O)COc1cccc(C(F)(F)F)c1</code> | <code>C[NH+](C)CCC=C1c2ccccc2COc2ccc(CC(=O)[O-])cc21</code> | <code>CC(C)C1(C(=O)NC2CC(=O)OC2(O)CF)CC(c2nccc3ccccc23)=NO1</code> |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
```json
{
"distance_metric": "TripletDistanceMetric.EUCLIDEAN",
"triplet_margin": 5
}
```
### 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_accuracy |
|:------:|:-----:|:-------------:|:---------------:|:-----------------------:|
| 0.0676 | 500 | 5.0821 | 5.1737 | 0.4047 |
| 0.1351 | 1000 | 4.9869 | 5.1766 | 0.4230 |
| 0.2027 | 1500 | 4.5562 | 4.9102 | 0.5345 |
| 0.2703 | 2000 | 3.2364 | 4.3712 | 0.6534 |
| 0.3378 | 2500 | 2.0738 | 4.0704 | 0.6736 |
| 0.4054 | 3000 | 1.4239 | 4.0200 | 0.6635 |
| 0.4730 | 3500 | 1.1578 | 3.7202 | 0.6791 |
| 0.5405 | 4000 | 0.9669 | 3.7197 | 0.6831 |
| 0.6081 | 4500 | 0.714 | 3.8818 | 0.6547 |
| 0.6757 | 5000 | 0.5359 | 4.0987 | 0.6243 |
| 0.7432 | 5500 | 0.5663 | 3.8127 | 0.6500 |
| 0.8108 | 6000 | 0.4827 | 3.8346 | 0.6676 |
| 0.8784 | 6500 | 0.4758 | 3.8333 | 0.6507 |
| 0.9459 | 7000 | 0.4759 | 3.6872 | 0.6912 |
| 1.0135 | 7500 | 0.4651 | 3.7229 | 0.6831 |
| 1.0811 | 8000 | 0.4739 | 3.8041 | 0.6662 |
| 1.1486 | 8500 | 0.4458 | 3.8235 | 0.6703 |
| 1.2162 | 9000 | 0.4189 | 3.7957 | 0.6716 |
| 1.2838 | 9500 | 0.4504 | 3.7422 | 0.6784 |
| 1.3514 | 10000 | 0.413 | 3.7588 | 0.6770 |
| 1.4189 | 10500 | 0.3808 | 3.9750 | 0.6615 |
| 1.4865 | 11000 | 0.3853 | 3.7417 | 0.6953 |
| 1.5541 | 11500 | 0.379 | 3.7319 | 0.6993 |
| 1.6216 | 12000 | 0.429 | 3.5620 | 0.7209 |
| 1.6892 | 12500 | 0.3735 | 3.6900 | 0.7020 |
| 1.7568 | 13000 | 0.3908 | 3.8182 | 0.6932 |
| 1.8243 | 13500 | 0.3848 | 3.7228 | 0.7101 |
| 1.8919 | 14000 | 0.3777 | 3.6604 | 0.7149 |
| 1.9595 | 14500 | 0.3912 | 3.7849 | 0.6946 |
| 2.0269 | 15000 | 0.3282 | 3.8607 | 0.7014 |
| 2.0945 | 15500 | 0.3324 | 3.8573 | 0.6953 |
| 2.1620 | 16000 | 0.3852 | 3.9420 | 0.7000 |
| 2.2296 | 16500 | 0.3633 | 3.7928 | 0.7189 |
| 2.2972 | 17000 | 0.3493 | 3.8217 | 0.7216 |
| 2.3647 | 17500 | 0.3554 | 3.8546 | 0.6993 |
| 2.4323 | 18000 | 0.3363 | 3.7764 | 0.6993 |
| 2.4999 | 18500 | 0.377 | 3.8224 | 0.6959 |
| 2.5674 | 19000 | 0.3569 | 3.8376 | 0.7155 |
| 2.635 | 19500 | 0.3414 | 4.0017 | 0.7034 |
| 2.7026 | 20000 | 0.3567 | 3.7405 | 0.7135 |
| 2.7701 | 20500 | 0.3524 | 3.9446 | 0.7189 |
| 2.8377 | 21000 | 0.3347 | 3.8140 | 0.7169 |
| 2.9053 | 21500 | 0.3458 | 4.0700 | 0.7088 |
| 2.9728 | 22000 | 0.3632 | 3.7930 | 0.7081 |
| 3.0404 | 22500 | 0.3496 | 3.9884 | 0.7236 |
| 3.1080 | 23000 | 0.3426 | 3.7102 | 0.7155 |
| 3.1755 | 23500 | 0.3579 | 3.9201 | 0.7135 |
| 3.2431 | 24000 | 0.3553 | 4.2237 | 0.7270 |
| 3.3107 | 24500 | 0.345 | 3.8090 | 0.7189 |
| 3.3782 | 25000 | 0.3475 | 3.7802 | 0.7284 |
| 3.4458 | 25500 | 0.3326 | 3.7549 | 0.7250 |
| 3.5134 | 26000 | 0.3228 | 3.6717 | 0.7216 |
| 3.5809 | 26500 | 0.3311 | 3.8241 | 0.7155 |
| 3.6485 | 27000 | 0.3215 | 3.8151 | 0.7142 |
| 3.7161 | 27500 | 0.3534 | 3.8639 | 0.7149 |
| 3.7836 | 28000 | 0.3369 | 4.0947 | 0.7101 |
| 3.8512 | 28500 | 0.3229 | 4.0495 | 0.7101 |
| 3.9188 | 29000 | 0.3442 | 4.0408 | 0.7169 |
| 3.9864 | 29500 | 0.3059 | 3.9493 | 0.6959 |
| 4.0538 | 30000 | 0.3349 | 4.0431 | 0.7108 |
| 4.1214 | 30500 | 0.3266 | 4.0224 | 0.7189 |
| 4.1889 | 31000 | 0.3501 | 3.9502 | 0.7169 |
| 4.2565 | 31500 | 0.3676 | 3.8903 | 0.7196 |
| 4.3241 | 32000 | 0.3191 | 3.7994 | 0.7162 |
| 4.3916 | 32500 | 0.3317 | 3.7889 | 0.7182 |
| 4.4592 | 33000 | 0.3304 | 3.8661 | 0.7108 |
| 4.5268 | 33500 | 0.3332 | 3.8822 | 0.7115 |
| 4.5943 | 34000 | 0.3435 | 3.7945 | 0.7088 |
| 4.6619 | 34500 | 0.317 | 3.8721 | 0.7243 |
| 4.7295 | 35000 | 0.3038 | 3.8615 | 0.7209 |
| 4.7970 | 35500 | 0.3093 | 3.8360 | 0.7162 |
| 4.8646 | 36000 | 0.3309 | 3.8277 | 0.7155 |
| 4.9322 | 36500 | 0.3378 | 3.7988 | 0.7128 |
| 4.9997 | 37000 | 0.311 | 3.8015 | 0.7135 |
### 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
```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",
}
```
#### TripletLoss
```bibtex
@misc{hermans2017defense,
title={In Defense of the Triplet Loss for Person Re-Identification},
author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
year={2017},
eprint={1703.07737},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
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## Model Card Contact
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