This is a sentence-transformers model finetuned from 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
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- csv

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': '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:

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/5Epoch_905_Statin_Contrastive")
# Run inference
sentences = [
    'COc1cc(CN(C)C(=O)c2nn(c(OC[C@@H](O)C[C@@H](O)CC(O)=O)c2C(C)C)-c2ccc(F)cc2)cc(OC)c1',
    'CC(C)c1sc(c(C2CCCC2)c1\\C=C\\[C@@H](O)C[C@@H](O)CC([O-])=O)-c1ccccc1',
    'CC(C)[C@H](NC(=O)[C@H](Cc1ccc(O)cc1)NC(=O)CNC(=O)[C@H](Cc1ccccc1)NC(=O)CN)C(=O)N[C@@H](C)C(=O)N[C@@H](CCC(O)=O)C(O)=O',
]
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, 0.2038, 0.4376],
#         [0.2038, 1.0000, 0.9656],
#         [0.4376, 0.9656, 1.0000]])

Training Details

Training Dataset

csv

Dataset: csv
Size: 148,695 training samples
Columns: premise, hypothesis, and label
Approximate statistics based on the first 1000 samples:
premise hypothesis label
type string string int
details
min: 23 tokens
mean: 67.64 tokens
max: 145 tokens
min: 23 tokens
mean: 65.13 tokens
max: 126 tokens
0: ~52.20%
2: ~47.80%

Samples:

premise	hypothesis	label
`Cc1cc(-c2ccc(Cl)cc2)c(\C=C[C@@H]2C[object Object]CC(=O)O2)c(C)n1`	`CC[object Object]C(=O)O[C@H]1C[object Object]C=C2C=C[object Object][object Object][C@@H]12`	`2`
`CC(C)c1c(OC[object Object]C[object Object]CC(O)=O)n(nc1C(=O)NCc1ccccc1C)-c1ccc(F)cc1`	`CCOC(=O)c1c(C(C)C)n(CC[C@@H]2C[object Object]CC(=O)O2)c(c1-c1ccccc1)-c1ccccc1`	`0`
`CC[object Object]C(=O)O[C@H]1CCC=C2C=C[object Object][object Object][C@@H]12`	`CC(c1ccc(F)cc1)c1cc(C)cc(C)c1OCC(O)C[object Object]CC([O-])=O`	`0`

Loss: SoftmaxLoss

Evaluation Dataset

csv

Dataset: csv
Size: 26,241 evaluation samples
Columns: premise, hypothesis, and label
Approximate statistics based on the first 1000 samples:
premise hypothesis label
type string string int
details
min: 23 tokens
mean: 67.34 tokens
max: 145 tokens
min: 23 tokens
mean: 65.76 tokens
max: 145 tokens
0: ~52.90%
2: ~47.10%

Samples:

premise	hypothesis	label
`CC(C)n1c(CC[object Object]C[object Object]CC([O-])=O)c(-c2ccc(F)cc2)c2c1c(=O)n(-c1ccccc1)c1ccccc21`	`C[C@@H]1CC(OC(=O)NCCCCCCCCCCC(=O)N[object Object]C(C)(C)C)[C@@H]2[object Object][object Object]C=CC2=C1`	`2`
`CC[object Object]C(=O)O[C@H]1C[object Object]C[C@@H]2C=C[object Object][object Object][C@@H]12`	`CC(C)c1sc(c(C2CCCC2)c1\C=C[C@@H](O)C[object Object]CC([O-])=O)-c1ccccc1`	`0`
`CC(C)c1c(OC[object Object]C[object Object]CC(O)=O)n(nc1C(=O)NCc1ccccc1C)-c1ccc(F)cc1`	`CC[object Object]C(=O)O[C@H]1CCC=C2C=C[object Object][object Object]C12`	`0`

Loss: SoftmaxLoss

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 64
per_device_eval_batch_size: 64
weight_decay: 0.01
num_train_epochs: 5
warmup_steps: 100
fp16: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
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: 5
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: {}

Training Logs

Click to expand

Epoch	Step	Training Loss
0.0430	100	0.7368
0.0861	200	0.6716
0.1291	300	0.6334
0.1721	400	0.6271
0.2151	500	0.6048
0.2582	600	0.5918
0.3012	700	0.5863
0.3442	800	0.5739
0.3873	900	0.5865
0.4303	1000	0.5679
0.4733	1100	0.5724
0.5164	1200	0.5576
0.5594	1300	0.5719
0.6024	1400	0.5628
0.6454	1500	0.5639
0.6885	1600	0.5533
0.7315	1700	0.5495
0.7745	1800	0.5437
0.8176	1900	0.5487
0.8606	2000	0.544
0.9036	2100	0.5431
0.9466	2200	0.5576
0.9897	2300	0.5434
1.0327	2400	0.5436
1.0757	2500	0.5444
1.1188	2600	0.5385
1.1618	2700	0.5377
1.2048	2800	0.5366
1.2478	2900	0.5354
1.2909	3000	0.5384
1.3339	3100	0.5288
1.3769	3200	0.5271
1.4200	3300	0.5364
1.4630	3400	0.5253
1.5060	3500	0.5261
1.5491	3600	0.5279
1.5921	3700	0.532
1.6351	3800	0.535
1.6781	3900	0.5251
1.7212	4000	0.5252
1.7642	4100	0.5302
1.8072	4200	0.5247
1.8503	4300	0.5261
1.8933	4400	0.5224
1.9363	4500	0.5165
1.9793	4600	0.5333
2.0224	4700	0.52
2.0654	4800	0.5246
2.1084	4900	0.5141
2.1515	5000	0.5201
2.1945	5100	0.5218
2.2375	5200	0.5219
2.2806	5300	0.5144
2.3236	5400	0.5225
2.3666	5500	0.5206
2.4096	5600	0.513
2.4527	5700	0.5212
2.4957	5800	0.5211
2.5387	5900	0.5127
2.5818	6000	0.5041
2.6248	6100	0.5152
2.6678	6200	0.5152
2.7108	6300	0.5138
2.7539	6400	0.507
2.7969	6500	0.5182
2.8399	6600	0.4988
2.8830	6700	0.5078
2.9260	6800	0.5113
2.9690	6900	0.5114
3.0120	7000	0.5171
3.0551	7100	0.5108
3.0981	7200	0.5033
3.1411	7300	0.5065
3.1842	7400	0.5057
3.2272	7500	0.5055
3.2702	7600	0.5129
3.3133	7700	0.5122
3.3563	7800	0.5069
3.3993	7900	0.5011
3.4423	8000	0.5129
3.4854	8100	0.5098
3.5284	8200	0.5022
3.5714	8300	0.5039
3.6145	8400	0.5123
3.6575	8500	0.5105
3.7005	8600	0.5056
3.7435	8700	0.5061
3.7866	8800	0.5004
3.8296	8900	0.5001
3.8726	9000	0.5127
3.9157	9100	0.5062
3.9587	9200	0.501
4.0017	9300	0.4928
4.0448	9400	0.4981
4.0878	9500	0.4942
4.1308	9600	0.4982
4.1738	9700	0.5076
4.2169	9800	0.5013
4.2599	9900	0.5051
4.3029	10000	0.4983
4.3460	10100	0.5051
4.3890	10200	0.4948
4.4320	10300	0.496
4.4750	10400	0.503
4.5181	10500	0.5064
4.5611	10600	0.5049
4.6041	10700	0.4986
4.6472	10800	0.492
4.6902	10900	0.4984
4.7332	11000	0.5036
4.7762	11100	0.5017
4.8193	11200	0.5019
4.8623	11300	0.5021
4.9053	11400	0.4991
4.9484	11500	0.494
4.9914	11600	0.5089

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",
}

	premise	hypothesis	label
type	string	string	int
details	min: 23 tokens mean: 67.64 tokens max: 145 tokens	min: 23 tokens mean: 65.13 tokens max: 126 tokens	0: ~52.20% 2: ~47.80%