HassanCS/chemBERTa-tuned-on-ClinTox-4

SentenceTransformer based on DeepChem/ChemBERTa-77M-MLM

This is a sentence-transformers model finetuned from 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
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity

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}) 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:

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("HassanCS/chemBERTa-tuned-on-ClinTox-4")
# Run inference
sentences = [
    'COc1ccc(C(=O)CC(=O)c2ccc(C(C)(C)C)cc2)cc1',
    'C[N+]1(C)CCC(=C(c2ccccc2)c2ccccc2)CC1',
    'C=C1CC2CCC34CC5OC6C(OC7CCC(CC(=O)CC8C(CC9OC(CCC1O2)CC(C)C9=C)OC(CC(O)CN)C8OC)OC7C6O3)C5O4',
]
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]

Evaluation

Metrics

Triplet

• Dataset: all-dev
• Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.7845

Training Details

Training Dataset

Unnamed Dataset

• Size: 35,520 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 14 tokens mean: 29.75 tokens max: 68 tokens	min: 3 tokens mean: 47.08 tokens max: 221 tokens	min: 3 tokens mean: 53.95 tokens max: 189 tokens

• Samples:

  anchor	positive	negative
  CC(C)CC(NC(=O)CNC(=O)c1cc(Cl)ccc1Cl)B(O)O	CC(=O)OC1CCC2(C)C(=CCC3C2CCC2(C)C(c4cccnc4)=CCC32)C1	CCOC(=O)c1ncn2c1CN(C)C(=O)c1cc(F)ccc1-2
  CC(C)CC(NC(=O)CNC(=O)c1cc(Cl)ccc1Cl)B(O)O	COc1ccc(C(CN(C)C)C2(O)CCCCC2)cc1	C[NH2+]C1(C)C2CCC(C2)C1(C)C
  CC(C)CC(NC(=O)CNC(=O)c1cc(Cl)ccc1Cl)B(O)O	CNC(=O)c1cc(Oc2ccc(NC(=O)Nc3ccc(Cl)c(C(F)(F)F)c3)cc2)ccn1.Cc1ccc(S(=O)(=O)O)cc1	Nc1ncnc2c1ncn2C1OC(CO)C(O)C1O

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Evaluation Dataset

Unnamed Dataset

• Size: 1,480 evaluation samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 18 tokens mean: 54.07 tokens max: 169 tokens	min: 18 tokens mean: 58.71 tokens max: 244 tokens	min: 23 tokens mean: 71.06 tokens max: 209 tokens

• Samples:

  anchor	positive	negative
  CC(C)OC(=O)CCCC=CCC1C(O)CC(O)C1C=CC(O)COc1cccc(C(F)(F)F)c1	C#CC1(O)CCC2C3CCC4=C(CCC(=O)C4)C3CCC21C	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
  CC(C)OC(=O)CCCC=CCC1C(O)CC(O)C1C=CC(O)COc1cccc(C(F)(F)F)c1	C=CC1(C)CC(OC(=O)CSC2CC3CCC(C2)[NH+]3C)C2(C)C(C)CCC3(CCC(=O)C32)C(C)C1O	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
  CC(C)OC(=O)CCCC=CCC1C(O)CC(O)C1C=CC(O)COc1cccc(C(F)(F)F)c1	CC(Cc1cc2c(c(C(N)=O)c1)N(CCCO)CC2)[NH2+]CCOc1ccccc1OCC(F)(F)F	CC(C)C1(C(=O)NC2CC(=O)OC2(O)CF)CC(c2nccc3ccccc23)=NO1

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

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: 10
• warmup_ratio: 0.1
• fp16: True
• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• 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: 10
• 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

Training Logs

Epoch	Step	Training Loss	Validation Loss	all-dev_cosine_accuracy
0.2252	500	4.2712	3.3651	0.45
0.4505	1000	3.5714	2.5580	0.6223
0.6757	1500	3.3655	2.5956	0.6169
0.9009	2000	3.2218	2.6932	0.6493
1.1257	2500	3.0911	2.7852	0.6736
1.3509	3000	3.0007	2.7838	0.6703
1.5761	3500	3.0536	2.5324	0.7311
1.8014	4000	3.0286	2.6623	0.6892
2.0261	4500	2.9539	2.6397	0.7088
2.2514	5000	2.9252	2.5550	0.7419
2.4766	5500	2.944	2.5391	0.7419
2.7018	6000	3.028	2.6421	0.6919
2.9270	6500	2.9389	2.5931	0.7209
3.1518	7000	2.9006	2.6597	0.7365
3.3770	7500	2.9107	2.4841	0.7709
3.6023	8000	2.9802	2.5128	0.7493
3.8275	8500	2.9498	2.5716	0.7439
4.0523	9000	2.9004	2.4889	0.7669
4.2775	9500	2.89	2.5824	0.7453
4.5027	10000	2.9343	2.4388	0.7757
4.7279	10500	2.9666	2.4759	0.7520
4.9532	11000	2.9153	2.6096	0.7399
5.1779	11500	2.873	2.5489	0.7520
5.4032	12000	2.8978	2.5579	0.7527
5.6284	12500	2.9576	2.5336	0.7581
5.8536	13000	2.93	2.4656	0.7730
6.0784	13500	2.8825	2.4987	0.7730
6.3036	14000	2.8863	2.4866	0.7818
6.5288	14500	2.9221	2.4416	0.7818
6.7541	15000	2.9544	2.4705	0.7622
6.9793	15500	2.8929	2.4991	0.7669
7.2041	16000	2.8656	2.5163	0.7689
7.4293	16500	2.8866	2.5390	0.7689
7.6545	17000	2.9675	2.4476	0.7872
7.8797	17500	2.9094	2.4572	0.775
8.1045	18000	2.8743	2.4677	0.7743
8.3297	18500	2.8748	2.4658	0.7872
8.5550	19000	2.9201	2.4412	0.7865
8.7802	19500	2.9437	2.4620	0.7811
9.0050	20000	2.881	2.4608	0.7797
9.2302	20500	2.8628	2.4801	0.7770
9.4554	21000	2.884	2.4699	0.7831
9.6806	21500	2.9658	2.4519	0.7845
9.9059	22000	2.8991	2.4474	0.7845

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

@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",
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}