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cafierom
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905_Statin_Contrastive

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:116941
loss:SoftmaxLoss
text-embeddings-inference
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905_Statin_Contrastive / README.md
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 ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:116941
- loss:SoftmaxLoss
base_model: google-bert/bert-base-cased
widget:
- source_sentence: O[C@@H]1CC(CCc2c(O)cc(Cl)cc2Cl)OC(=O)C1
sentences:
- O[C@@H]1C[C@@H](CC[C@@H]2CCC[C@@H]3CCCC[C@H]23)OC(=O)C1
- O[C@@H]1CC(CCc2cccc3ccccc23)OC(=O)C1
- CC(C)n1c(CC[C@@H](O)C[C@@H](O)CC([O-])=O)c(c(c1C(=O)NCc1ccccc1)-c1ccccn1)-c1ccc(F)cc1
- source_sentence: O[C@@H]1C[C@H](OC(=O)C1)\C=C\c1cnc2c(Sc3ccc(F)cc3)c(Sc3ccc(F)cc3)c(F)cc2c1Sc1ccc(F)cc1
sentences:
- O[C@H](C[C@H](O)\C=C\c1c2CCCc2nn1-c1ccc(F)cc1)CC([O-])=O
- C[C@H](CC\C=C(/C)C(O)=O)[C@H]1C[C@H](O)[C@@]2(C)C3=CC[C@H]4C(C)(C)C(=O)CC[C@]4(C)C3=CC[C@]12C
- CC(C)c1ccc(Sc2c(\C=C\[C@@H]3C[C@@H](O)CC(=O)O3)cnc3cc(Cl)c(F)cc23)cc1
- source_sentence: O[C@H](C[C@H](O)\C=C\c1c2CCCCc2nn1-c1ccc(F)cc1)CC([O-])=O
sentences:
- O[C@@H]1C[C@H](OC(=O)C1)\C=C\c1cnc2cc(Sc3ccccc3)c(Sc3ccccc3)cc2c1Sc1ccccc1
- CC[C@H](C)[C@H](N)C(=O)N[C@@H](C)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CCC(O)=O)C(O)=O
- CC(C)n1c(CC[C@@H](O)C[C@@H](O)CC(O)=O)c(c(c1C(=O)N(C)Cc1ccccc1)-c1ccccc1)-c1ccc(F)cc1
- source_sentence: COc1ccc(CNC(=O)c2nc(-c3ccc(F)cc3)n(CC[C@@H](O)C[C@@H](O)CC([O-])=O)c2C2CC2)cc1
sentences:
- CC(C)c1nc(nc(-c2ccc(F)cc2)c1\C=C\[C@@H](O)C[C@@H](O)CC(O)=O)N(c1nnnn1C)S(C)(=O)=O
- CC(C)c1c(CC[C@@H](O)C[C@@H](O)CC(O)=O)n(nc1C(=O)N(C)Cc1ccccc1)-c1ccc(F)cc1
- Cc1c(OCC(O)C[C@@H](O)CC([O-])=O)c(cc2ccccc12)C(c1ccc(F)cc1)c1ccc(F)cc1
- source_sentence: CC(C)n1c(CC[C@@H](O)C[C@@H](O)CC([O-])=O)c(c(c1C(=O)NCc1cccc(c1)C(N)=O)-c1ccccc1)-c1ccc(F)cc1
sentences:
- CC(C)c1nc(c(-c2ccc(F)cc2)n1\C=C\[C@@H](O)C[C@@H](O)CC(O)=O)-c1ccc(F)cc1
- CCn1nnc(n1)C(\C=C\[C@@H](O)C[C@@H](O)CC([O-])=O)=C(c1ccc(F)cc1)c1ccc(F)cc1
- CC(C)c1nc(nc(-c2ccc(F)cc2)c1\C=C\[C@@H](O)C[C@@H](O)CC(O)=O)N(C)c1ccnn1C
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on google-bert/bert-base-cased
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the csv dataset. It maps sentences & paragraphs to a 768-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:** [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) <!-- at revision cd5ef92a9fb2f889e972770a36d4ed042daf221e -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- csv
<!-- - **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, 'architecture': 'BertModel'})
(1): Pooling({'word_embedding_dimension': 768, '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("cafierom/905_Statin_Contrastive")
# Run inference
sentences = [
'CC(C)n1c(CC[C@@H](O)C[C@@H](O)CC([O-])=O)c(c(c1C(=O)NCc1cccc(c1)C(N)=O)-c1ccccc1)-c1ccc(F)cc1',
'CC(C)c1nc(c(-c2ccc(F)cc2)n1\\C=C\\[C@@H](O)C[C@@H](O)CC(O)=O)-c1ccc(F)cc1',
'CCn1nnc(n1)C(\\C=C\\[C@@H](O)C[C@@H](O)CC([O-])=O)=C(c1ccc(F)cc1)c1ccc(F)cc1',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000, 0.9994, -0.0483],
# [ 0.9994, 1.0000, -0.0453],
# [-0.0483, -0.0453, 1.0000]])
```
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## Training Details
### Training Dataset
#### csv
* Dataset: csv
* Size: 116,941 training samples
* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | premise | hypothesis | label |
|:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 17 tokens</li><li>mean: 70.84 tokens</li><li>max: 147 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 62.37 tokens</li><li>max: 141 tokens</li></ul> | <ul><li>0: ~66.30%</li><li>2: ~33.70%</li></ul> |
* Samples:
| premise | hypothesis | label |
|:--------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------|:---------------|
| <code>CC[C@H](C)C(=O)O[C@H]1C[C@@H](C)C[C@@H]2C=C[C@H](C)[C@H](CCC(O)C[C@@H](O)CC(O)=O)C12</code> | <code>CCCCCCCCCCCCCCCC1(O)CCOC(O)C1</code> | <code>2</code> |
| <code>O[C@H](C[C@H](O)\C=C\c1c(Cl)cc(Cl)cc1-c1ccc(F)cc1)CC([O-])=O</code> | <code>C[C@@]1(O)C[C@H](OC(=O)C1)\C=C\c1ccc(Cl)cc1Cl</code> | <code>2</code> |
| <code>CC(C)c1nc(nc(-c2ccc(F)cc2)c1\C=C\[C@@H]1C[C@@H](O)CC(=O)O1)-c1ccc(F)cc1</code> | <code>CC(C)C[C@H](NC(=O)CN)C(=O)N1CCC[C@H]1C(=O)N[C@@H](CCC(O)=O)C(=O)NCC(=O)NCC(O)=O</code> | <code>2</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
### Evaluation Dataset
#### csv
* Dataset: csv
* Size: 20,637 evaluation samples
* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | premise | hypothesis | label |
|:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 17 tokens</li><li>mean: 69.69 tokens</li><li>max: 147 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 59.63 tokens</li><li>max: 141 tokens</li></ul> | <ul><li>0: ~67.40%</li><li>2: ~32.60%</li></ul> |
* Samples:
| premise | hypothesis | label |
|:-----------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>COC(=O)C[C@H](O)C[C@H](O)\C=C\n1c(C(C)C)c(Br)c(c1-c1ccc(F)cc1)-c1ccc(F)cc1</code> | <code>C[C@H](CC(O)CC(O)CC([O-])=O)[C@H]1CC[C@H]2[C@@H]3[C@@H](C[C@@H]4C[C@@H](CC[C@]4(C)[C@H]3C[C@H](OC(C)=O)[C@]12C)OC(C)=O)OC(C)=O</code> | <code>2</code> |
| <code>CC(C)n1c(CC[C@@H](O)C[C@@H](O)CC([O-])=O)c(c(c1C(=O)Nc1ccc(O)cc1)-c1ccccc1)-c1ccc(F)cc1</code> | <code>CC[C@H](C)C(=O)O[C@H]1C[C@H](C)C=C2C=C[C@H](C)[C@H](CC[C@@H]3C[C@@H](O)CC(=O)O3)[C@@H]12</code> | <code>0</code> |
| <code>CC(C)C(=O)O[C@H]1C[C@@H](C)C=C2C=C[C@H](C)[C@H](CC[C@@H]3C[C@@H](O)CC(=O)O3)C12</code> | <code>CC(C)c1c(nc(-c2ccc(F)cc2)n1\C=C\[C@@H](O)C[C@@H](O)CC([O-])=O)-c1ccc(F)cc1</code> | <code>0</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `weight_decay`: 0.01
- `num_train_epochs`: 10
- `warmup_steps`: 100
- `fp16`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `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`: 0.01
- `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`: 0.0
- `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`: 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}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `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
| Epoch | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.1094 | 100 | 0.4346 |
| 0.2188 | 200 | 0.0656 |
| 0.3282 | 300 | 0.0082 |
| 0.4376 | 400 | 0.007 |
| 0.5470 | 500 | 0.0056 |
| 0.6565 | 600 | 0.0054 |
| 0.7659 | 700 | 0.0006 |
| 0.8753 | 800 | 0.0005 |
| 0.9847 | 900 | 0.0004 |
| 1.0941 | 1000 | 0.0004 |
| 1.2035 | 1100 | 0.0003 |
| 1.3129 | 1200 | 0.0003 |
| 1.4223 | 1300 | 0.0003 |
| 1.5317 | 1400 | 0.0003 |
| 1.6411 | 1500 | 0.0002 |
| 1.7505 | 1600 | 0.0002 |
| 1.8600 | 1700 | 0.0002 |
| 1.9694 | 1800 | 0.0002 |
| 2.0788 | 1900 | 0.0002 |
| 2.1882 | 2000 | 0.0002 |
| 2.2976 | 2100 | 0.0001 |
| 2.4070 | 2200 | 0.0001 |
| 2.5164 | 2300 | 0.0001 |
| 2.6258 | 2400 | 0.0001 |
| 2.7352 | 2500 | 0.0001 |
| 2.8446 | 2600 | 0.0001 |
| 2.9540 | 2700 | 0.0001 |
| 3.0635 | 2800 | 0.0001 |
| 3.1729 | 2900 | 0.0001 |
| 3.2823 | 3000 | 0.0001 |
| 3.3917 | 3100 | 0.0001 |
| 3.5011 | 3200 | 0.0001 |
| 3.6105 | 3300 | 0.0001 |
| 3.7199 | 3400 | 0.0001 |
| 3.8293 | 3500 | 0.0001 |
| 3.9387 | 3600 | 0.0001 |
| 4.0481 | 3700 | 0.0001 |
| 4.1575 | 3800 | 0.0001 |
| 4.2670 | 3900 | 0.0001 |
| 4.3764 | 4000 | 0.0 |
| 4.4858 | 4100 | 0.0 |
| 4.5952 | 4200 | 0.0 |
| 4.7046 | 4300 | 0.0 |
| 4.8140 | 4400 | 0.0 |
| 4.9234 | 4500 | 0.0 |
| 5.0328 | 4600 | 0.0 |
| 5.1422 | 4700 | 0.0 |
| 5.2516 | 4800 | 0.0 |
| 5.3611 | 4900 | 0.0 |
| 5.4705 | 5000 | 0.0 |
| 5.5799 | 5100 | 0.0 |
| 5.6893 | 5200 | 0.0 |
| 5.7987 | 5300 | 0.0 |
| 5.9081 | 5400 | 0.0 |
| 6.0175 | 5500 | 0.0002 |
| 6.1269 | 5600 | 0.0 |
| 6.2363 | 5700 | 0.0 |
| 6.3457 | 5800 | 0.0 |
| 6.4551 | 5900 | 0.0 |
| 6.5646 | 6000 | 0.0 |
| 6.6740 | 6100 | 0.0 |
| 6.7834 | 6200 | 0.0 |
| 6.8928 | 6300 | 0.0 |
| 7.0022 | 6400 | 0.0 |
| 7.1116 | 6500 | 0.0 |
| 7.2210 | 6600 | 0.0 |
| 7.3304 | 6700 | 0.0 |
| 7.4398 | 6800 | 0.0 |
| 7.5492 | 6900 | 0.0 |
| 7.6586 | 7000 | 0.0 |
| 7.7681 | 7100 | 0.0 |
| 7.8775 | 7200 | 0.0 |
| 7.9869 | 7300 | 0.0 |
| 8.0963 | 7400 | 0.0 |
| 8.2057 | 7500 | 0.0 |
| 8.3151 | 7600 | 0.0 |
| 8.4245 | 7700 | 0.0 |
| 8.5339 | 7800 | 0.0 |
| 8.6433 | 7900 | 0.0 |
| 8.7527 | 8000 | 0.0 |
| 8.8621 | 8100 | 0.0 |
| 8.9716 | 8200 | 0.0 |
| 9.0810 | 8300 | 0.0022 |
| 9.1904 | 8400 | 0.0019 |
| 9.2998 | 8500 | 0.0001 |
| 9.4092 | 8600 | 0.0 |
| 9.5186 | 8700 | 0.0 |
| 9.6280 | 8800 | 0.0 |
| 9.7374 | 8900 | 0.0 |
| 9.8468 | 9000 | 0.0 |
| 9.9562 | 9100 | 0.0 |
### Framework Versions
- Python: 3.12.11
- Sentence Transformers: 5.1.0
- Transformers: 4.56.0
- PyTorch: 2.8.0+cu126
- Accelerate: 1.10.1
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## Citation
### BibTeX
#### Sentence Transformers and SoftmaxLoss
```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",
}
```
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