Jimmy-Ooi/TTM_1239_12_15_0.0001_AdamW

SentenceTransformer based on google-bert/bert-base-cased

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 <!-- 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
• —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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Jimmy-Ooi/TTM_1239_12_15_0.0001_AdamW")
# Run inference
sentences = [
    'OCc1ccc(Nc2nc(-c3ccc(O)cc3O)cs2)cc1',
    'COCCOCCOc1ccc(C=C2C(=O)NC(=S)NC2=O)cc1',
    'CCCCC(=O)NC(=S)Nc1ccc([N+](=O)[O-])cc1[N+](=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, -0.7951,  0.7088],
#         [-0.7951,  1.0000, -0.2010],
#         [ 0.7088, -0.2010,  1.0000]])

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Downstream Usage (Sentence Transformers)

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Training Details

Training Dataset

csv

• —Dataset: csv
• —Size: 286,816 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: 10 tokens</li><li>mean: 37.2 tokens</li><li>max: 213 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 37.9 tokens</li><li>max: 213 tokens</li></ul> | <ul><li>0: ~47.80%</li><li>2: ~52.20%</li></ul> |
• —Samples: | premise | hypothesis | label | |:-------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:---------------| | <code>O=C1/C(=C/c2ccc(O)cc2O)Oc2cccc(O)c21</code> | <code>COc1c(O)cc2c(c1O)[C@@H]1OC@Hc3ccc(O)c(O)c3)C@@HC@H[C@H]1OC2=O</code> | <code>0</code> | | <code>COc1cc(C2CC(c3ccccc3O)=NN2C(C)=O)ccc1O</code> | <code>COc1cc(C=O)ccc1OC(=O)COC(=O)c1ccc(O)cc1O</code> | <code>2</code> | | <code>Cc1nnc(SCc2cc(=O)c(O)co2)n1NCc1ccc(O)cc1O</code> | <code>O=C1/C(=C/c2ccc(O)cc2O)Oc2cc(O)ccc21</code> | <code>2</code> |
• —Loss: <code>SoftmaxLoss</code>

Evaluation Dataset

csv

• —Dataset: csv
• —Size: 50,615 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: 8 tokens</li><li>mean: 37.07 tokens</li><li>max: 213 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 38.08 tokens</li><li>max: 213 tokens</li></ul> | <ul><li>0: ~51.20%</li><li>2: ~48.80%</li></ul> |
• —Samples: | premise | hypothesis | label | |:-----------------------------------------------|:-----------------------------------------------------------|:---------------| | <code>C/C(=N/NC(N)=S)c1c(O)cc(O)cc1O</code> | <code>O=C(CCc1ccc(O)c(O)c1)c1c(O)cc(O)cc1O</code> | <code>0</code> | | <code>O=C(NO)Nc1ccc(C(F)(F)F)cc1</code> | <code>NC(=O)C@HNC(=O)OCc1cc(=O)c(O)co1</code> | <code>2</code> | | <code>O=C1/C(=C/c2ccc(O)cc2O)Sc2ccccc21</code> | <code>O=C(O)CSc1nnc(NC(=S)Nc2ccccc2)s1</code> | <code>0</code> |
• —Loss: <code>SoftmaxLoss</code>

Training Hyperparameters

Non-Default Hyperparameters

• —per_device_train_batch_size: 64
• —num_train_epochs: 15
• —warmup_steps: 100
• —optim: adamw_torch
• —weight_decay: 0.0001
• —fp16: True
• —per_device_eval_batch_size: 64

All Hyperparameters

<details><summary>Click to expand</summary>

• —per_device_train_batch_size: 64
• —num_train_epochs: 15
• —max_steps: -1
• —learning_rate: 5e-05
• —lr_scheduler_type: linear
• —lr_scheduler_kwargs: None
• —warmup_steps: 100
• —optim: adamw_torch
• —optim_args: None
• —weight_decay: 0.0001
• —adam_beta1: 0.9
• —adam_beta2: 0.999
• —adam_epsilon: 1e-08
• —optim_target_modules: None
• —gradient_accumulation_steps: 1
• —average_tokens_across_devices: True
• —max_grad_norm: 1.0
• —label_smoothing_factor: 0.0
• —bf16: False
• —fp16: True
• —bf16_full_eval: False
• —fp16_full_eval: False
• —tf32: None
• —gradient_checkpointing: False
• —gradient_checkpointing_kwargs: None
• —torch_compile: False
• —torch_compile_backend: None
• —torch_compile_mode: None
• —use_liger_kernel: False
• —liger_kernel_config: None
• —use_cache: False
• —neftune_noise_alpha: None
• —torch_empty_cache_steps: None
• —auto_find_batch_size: False
• —log_on_each_node: True
• —logging_nan_inf_filter: True
• —include_num_input_tokens_seen: no
• —log_level: passive
• —log_level_replica: warning
• —disable_tqdm: False
• —project: huggingface
• —trackio_space_id: trackio
• —eval_strategy: no
• —per_device_eval_batch_size: 64
• —prediction_loss_only: True
• —eval_on_start: False
• —eval_do_concat_batches: True
• —eval_use_gather_object: False
• —eval_accumulation_steps: None
• —include_for_metrics: []
• —batch_eval_metrics: False
• —save_only_model: False
• —save_on_each_node: False
• —enable_jit_checkpoint: False
• —push_to_hub: False
• —hub_private_repo: None
• —hub_model_id: None
• —hub_strategy: every_save
• —hub_always_push: False
• —hub_revision: None
• —load_best_model_at_end: False
• —ignore_data_skip: False
• —restore_callback_states_from_checkpoint: False
• —full_determinism: False
• —seed: 42
• —data_seed: None
• —use_cpu: False
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
• —parallelism_config: None
• —dataloader_drop_last: False
• —dataloader_num_workers: 0
• —dataloader_pin_memory: True
• —dataloader_persistent_workers: False
• —dataloader_prefetch_factor: None
• —remove_unused_columns: True
• —label_names: None
• —train_sampling_strategy: random
• —length_column_name: length
• —ddp_find_unused_parameters: None
• —ddp_bucket_cap_mb: None
• —ddp_broadcast_buffers: False
• —ddp_backend: None
• —ddp_timeout: 1800
• —fsdp: []
• —fsdp_config: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —deepspeed: None
• —debug: []
• —skip_memory_metrics: True
• —do_predict: False
• —resume_from_checkpoint: None
• —warmup_ratio: None
• —local_rank: -1
• —prompts: None
• —batch_sampler: batch_sampler
• —multi_dataset_batch_sampler: proportional
• —router_mapping: {}
• —learning_rate_mapping: {}

</details>

Training Logs

<details><summary>Click to expand</summary>

</details>

Framework Versions

• —Python: 3.12.13
• —Sentence Transformers: 5.3.0
• —Transformers: 5.3.0
• —PyTorch: 2.10.0+cu128
• —Accelerate: 1.13.0
• —Datasets: 4.0.0
• —Tokenizers: 0.22.2

Citation

BibTeX

Sentence Transformers and SoftmaxLoss

@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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