chemberta-77m-MLM-sBERT-finetuned-on-bace-with-sBERTContrastiveLoss

SentenceTransformer based on kiarashmo/chembberta-77m-MLM-finetuned-on-bace

This is a sentence-transformers model finetuned from kiarashmo/chembberta-77m-MLM-finetuned-on-bace. 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: kiarashmo/chembberta-77m-MLM-finetuned-on-bace <!-- at revision 9187aae7ee73042efdfedec9e69c1c4743ebc840 -->
• 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
• 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': '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'COCC(=O)NC(Cc1cccc(-c2nccn2C)c1)C(O)C[NH2+]C1CC2(CCC2)Oc2ncc(CC(C)(C)C)cc21',
    'C#CC[NH2+]CC(O)C(Cc1ccccc1)NC(=O)c1cc2c3c(c1)c(CC)cn3CCS(=O)(=O)O2',
    'CC1(c2cc(NC(=O)c3ncc(Cl)cn3)ccc2F)N=C(N)OCC1(F)F',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.8762, 0.9047],
#         [0.8762, 1.0000, 0.8644],
#         [0.9047, 0.8644, 1.0000]])

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* -->

Evaluation

Metrics

Binary Classification

• Dataset: val-sim
• Evaluated with <code>BinaryClassificationEvaluator</code>

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Training Details

Training Dataset

Unnamed Dataset

• Size: 30,000 training samples
• Columns: <code>sentence_A</code>, <code>sentence_B</code>, and <code>label</code>
• Approximate statistics based on the first 1000 samples:

  	sentence_A	sentence_B	label
  type	string	string	float
  details	<ul><li>min: 17 tokens</li><li>mean: 61.05 tokens</li><li>max: 129 tokens</li></ul>	<ul><li>min: 17 tokens</li><li>mean: 61.82 tokens</li><li>max: 123 tokens</li></ul>	<ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul>

• Samples:

  sentence_A	sentence_B	label
  <code>CN1C(=O)C(c2ccc(OC(F)F)cc2)(c2cccc(C#CCCCCO)c2)N=C1N</code>	<code>COc1cccc(C[NH2+]CC(O)C(Cc2cc(F)cc(F)c2)NC(=O)c2cc(C(C)=O)cc(N(C)S(C)(=O)=O)c2)c1</code>	<code>0.0</code>
  <code>CN1C(=O)C(c2ccccc2)(c2cccc(Cl)c2)N=C1N</code>	<code>CCCOC1C[NH2+]C(C(O)C(Cc2cc(F)cc(F)c2)NC(=O)C(C)N2CCC(C(C)C)C2=O)C1</code>	<code>1.0</code>
  <code>CCC(CC(=O)NC(COC(C)(C)C)C(=O)[O-])n1c(N)nc2cc(Cl)ccc21</code>	<code>CN1C(=O)C(c2ccccc2)(C23CC4CC(CC(C4)C2)C3)N=C1N</code>	<code>1.0</code>

• Loss: <code>ContrastiveLoss</code> with these parameters:

  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.5,
      "size_average": true
  }
  

Evaluation Dataset

Unnamed Dataset

• Size: 1,000 evaluation samples
• Columns: <code>sentence_A</code>, <code>sentence_B</code>, and <code>label</code>
• Approximate statistics based on the first 1000 samples:

  	sentence_A	sentence_B	label
  type	string	string	float
  details	<ul><li>min: 46 tokens</li><li>mean: 63.21 tokens</li><li>max: 110 tokens</li></ul>	<ul><li>min: 46 tokens</li><li>mean: 63.68 tokens</li><li>max: 110 tokens</li></ul>	<ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul>

• Samples:

  sentence_A	sentence_B	label
  <code>CCNc1cc(C(=O)NC(Cc2ccccc2)C(O)C[NH2+]C2CC2)cc(N2CCCCS2(=O)=O)c1</code>	<code>CC(=O)NC(Cc1cc(F)cc(F)c1)C(O)C[NH2+]C1(c2cccc(C3CCOC3)c2)CCOCC1</code>	<code>1.0</code>
  <code>CC(=O)NC(Cc1cc(F)cc(F)c1)C(O)C[NH2+]C1(c2cccc(C(C)(C)C)c2)CCC(Nc2cccnc2)CC1</code>	<code>CN1C(=O)C(c2ccsc2)(c2cccc(-c3cnccn3)c2)N=C1N</code>	<code>0.0</code>
  <code>CC(=O)NC(Cc1cc(F)cc(F)c1)C(O)C[NH2+]C1(c2cccc(C(C)(C)C)c2)CCC(=O)CC1</code>	<code>CC#Cc1ccnc(-c2cc(C3(C4CC4)N=C(C)C(N)=N3)ccc2O)c1</code>	<code>1.0</code>

• Loss: <code>ContrastiveLoss</code> with these parameters:

  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.5,
      "size_average": true
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 32
• num_train_epochs: 100
• warmup_steps: 100
• load_best_model_at_end: True

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: 32
• per_device_eval_batch_size: 32
• 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: 5e-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: 100
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.0
• warmup_steps: 100
• 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: False
• 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: True
• 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
• hub_revision: None
• 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
• 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
• liger_kernel_config: None
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

</details>

Training Logs

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.9.23
• Sentence Transformers: 5.0.0
• Transformers: 4.53.3
• PyTorch: 2.5.0+cu118
• Accelerate: 1.9.0
• Datasets: 4.0.0
• Tokenizers: 0.21.2

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",
}

ContrastiveLoss

@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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