kiarashmo/sBERT-finetuned-on-hiv-with-contrastive

SentenceTransformer based on kiarashmo/chembberta-77m-mlm-safetensors

This is a sentence-transformers model finetuned from kiarashmo/chembberta-77m-mlm-safetensors. 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-safetensors <!-- at revision 9d0b79d268438177519adce1e36395ea0ae363e9 -->
• —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}) 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("sentence_transformers_model_id")
# Run inference
sentences = [
    'CC(C)C1NC(=O)C(Cc2ccccc2)NC(=O)C2NC(=O)C(Cc3ccccc3)NC(=O)C3CCCN3C(=O)CNC(=O)C(Cc3ccccc3)NC(=O)C3CNCCCCC(C(=O)O)NC(=O)C4NC(=O)C(CC(N)=O)NC(=O)CNC(=O)C(C(O)C(=O)O)NC(=O)C(CSCC(NC(=O)C(CCC(N)=O)NC(=O)C(CCCNC(=N)N)NC(=O)C(N)CSC4C)C(=O)NC(CSC2C)C(=O)N3)NC1=O',
    'CCCCC(C)CCCC1=C(O)C(=CC(=O)O)OC1=O',
    'CC1=CC(=O)CC2(C)CCC3CC12OC3(C)C',
]
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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Downstream Usage (Sentence Transformers)

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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>sentenceA</code>, <code>sentenceB</code>, and <code>label</code>
• —Approximate statistics based on the first 1000 samples: | | sentenceA | sentenceB | label | |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | <ul><li>min: 14 tokens</li><li>mean: 51.28 tokens</li><li>max: 329 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 52.52 tokens</li><li>max: 329 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.48</li><li>max: 1.0</li></ul> |
• —Samples: | sentenceA | sentenceB | label | |:-----------------------------------------------------------------------|:------------------------------------------------------------------------------|:-----------------| | <code>O=C(C=Cc1cc(O)ccc1O)OCCc1ccccc1</code> | <code>Nc1ccc(C(=O)NN2C(=O)C(Cl)C2c2cc(Br)ccc2O)cc1</code> | <code>0.0</code> | | <code>CC(C)=NOC(=O)CNC(=O)C(Cc1ccccc1)NC(=O)C(C)NC(=O)OC(C)(C)C</code> | <code>CCCCCCCCCC=C(c1cc(Cl)c(OC)c(C(=O)O)c1)c1cc(Cl)c(OC)c(C(=O)O)c1.N</code> | <code>0.0</code> | | <code>CCC1(CC)C(=O)N(C(=O)c2ccccc2)N(C(=O)c2ccc(Cl)cc2)C1=O</code> | <code>O=C(NC(=Cc1ccc(N+[O-])cc1)c1nc2c(O)nc(S)nc2[nH]1)c1ccccc1</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>sentenceA</code>, <code>sentenceB</code>, and <code>label</code>
• —Approximate statistics based on the first 1000 samples: | | sentenceA | sentenceB | label | |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | <ul><li>min: 16 tokens</li><li>mean: 61.85 tokens</li><li>max: 255 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 63.05 tokens</li><li>max: 255 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.51</li><li>max: 1.0</li></ul> |
• —Samples: | sentenceA | sentenceB | label | |:--------------------------------------------------------------------|:--------------------------------------------------------------------------|:-----------------| | <code>O=S(=O)(c1ccccc1)c1nccs1</code> | <code>COc1cc(C=CC2=C(C#N)C(=O)OC2(C)C)ccc1O</code> | <code>0.0</code> | | <code>N#CC1=C(N)CCSSSC1</code> | <code>CCCCC(C)C=CC(O)=C1C(=O)OC(=CC(=O)O)C1=O</code> | <code>0.0</code> | | <code>Nc1nc(CCC(=O)Nc2cccc(C(F)(F)F)c2)cc(-c2ccc3ccccc3c2)n1</code> | <code>CC(CCn1[nH]c(=O)ccc1=O)=NNc1ccc(N+[O-])cc1N+[O-]</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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —fsdp_transformer_layer_cls_to_wrap: None
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': 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
• —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: batch_sampler
• —multi_dataset_batch_sampler: proportional

</details>

Training Logs

• —The bold row denotes the saved checkpoint.

Framework Versions

• —Python: 3.11.13
• —Sentence Transformers: 4.1.0
• —Transformers: 4.52.4
• —PyTorch: 2.6.0+cu124
• —Accelerate: 1.8.1
• —Datasets: 3.6.0
• —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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