Jimmy-Ooi/Tyrisonase_test_model_800_8epoch

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/Tyrisonase_test_model_800_8epoch")
# Run inference
sentences = [
    'COCCOc1ccc(C=C2C(=O)NC(=S)NC2=O)cc1',
    'O=C(NCCc1c[nH]c2ccc(O)cc12)c1ccc(O)cc1O',
    'O=C(c1ccc(C(F)(F)F)cc1)N1CCN(Cc2ccc(F)cc2)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,  0.8655, -0.6846],
#         [ 0.8655,  1.0000, -0.9302],
#         [-0.6846, -0.9302,  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. -->

<!--

Bias, Risks and Limitations

What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. -->

<!--

Recommendations

What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. -->

Training Details

Training Dataset

csv

• —Dataset: csv
• —Size: 120,059 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: 11 tokens</li><li>mean: 39.24 tokens</li><li>max: 106 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 39.14 tokens</li><li>max: 106 tokens</li></ul> | <ul><li>0: ~49.50%</li><li>2: ~50.50%</li></ul> |
• —Samples: | premise | hypothesis | label | |:--------------------------------------------------------------------------|:---------------------------------------------------------------------------|:---------------| | <code>Cn1c2ccccc2c2cc(/C=C/C(=O)c3cccc(NC(=O)c4ccc(Cl)cc4)c3)ccc21</code> | <code>Oc1cc(O)cc(CC(c2ccc(O)cc2O)c2c(O)cc(/C=C/c3ccc(O)cc3O)cc2O)c1</code> | <code>0</code> | | <code>COc1cc(N+[O-])ccc1/C=C/C(=N\O)c1cc2ccccc2cc1O</code> | <code>CN1[C@@H]2CC[C@H]1C/C(=N/Nc1nc(-c3ccc(Cl)c(Cl)c3)cs1)C2</code> | <code>2</code> | | <code>CCOC(OCC)c1ccc(/C=C2\CCC/C(=C\c3ccc(C(OCC)OCC)cc3)C2=O)cc1</code> | <code>Cc1nnc(SCc2cc(=O)c(O)co2)n1NCc1ccc(O)cc1O</code> | <code>2</code> |
• —Loss: <code>SoftmaxLoss</code>

Evaluation Dataset

csv

• —Dataset: csv
• —Size: 21,187 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: 11 tokens</li><li>mean: 38.38 tokens</li><li>max: 106 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 39.13 tokens</li><li>max: 106 tokens</li></ul> | <ul><li>0: ~49.60%</li><li>2: ~50.40%</li></ul> |
• —Samples: | premise | hypothesis | label | |:-----------------------------------------------------|:-------------------------------------------------------------------------|:---------------| | <code>Cc1ccccc1C(=O)N1CCN(Cc2ccc(F)cc2)CC1</code> | <code>COCCOc1ccc(C=C2C(=O)NC(=S)NC2=O)cc1</code> | <code>0</code> | | <code>C/C(=N\NC(N)=S)c1cccc(NC(=O)C(C)(C)C)c1</code> | <code>C=CC(C)(C)c1cc(C2CC(=O)c3c(cc(O)c(CC=C(C)C)c3O)O2)c(OC)cc1O</code> | <code>2</code> | | <code>O=C1CC(c2ccc(O)cc2O)Oc2cc(O)cc(O)c21</code> | <code>Cc1ccc(C(=O)N2CCN(Cc3ccc(F)cc3)CC2)cc1</code> | <code>0</code> |
• —Loss: <code>SoftmaxLoss</code>

Training Hyperparameters

Non-Default Hyperparameters

• —per_device_train_batch_size: 64
• —per_device_eval_batch_size: 64
• —weight_decay: 0.01
• —num_train_epochs: 8
• —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: 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: 8
• —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
• —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: {'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}
• —parallelism_config: None
• —deepspeed: None
• —label_smoothing_factor: 0.0
• —optim: adamwtorchfused
• —optim_args: None
• —adafactor: False
• —group_by_length: False
• —length_column_name: length
• —project: huggingface
• —trackio_space_id: trackio
• —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: no
• —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: True
• —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: 3.12.12
• —Sentence Transformers: 5.1.1
• —Transformers: 4.57.1
• —PyTorch: 2.8.0+cu126
• —Accelerate: 1.10.1
• —Datasets: 4.0.0
• —Tokenizers: 0.22.1

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",
}

<!--

Glossary

Clearly define terms in order to be accessible across audiences. -->

<!--

Model Card Authors

Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. -->

<!--

Model Card Contact

Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->