kwondw/bert-base-uncased-trec-covid-tsdae

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

This is a sentence-transformers model finetuned from google-bert/bert-base-uncased. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

• —Model Type: Sentence Transformer
• —Base model: google-bert/bert-base-uncased <!-- at revision 86b5e0934494bd15c9632b12f734a8a67f723594 -->
• —Maximum Sequence Length: 75 tokens
• —Output Dimensionality: 768 dimensions
• —Similarity Function: Cosine Similarity
• —Supported Modality: Text <!-- - 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({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'})
  (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'cls', '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("kwondw/bert-base-uncased-trec-covid-tsdae")
# Run inference
sentences = [
    'Of, cyclization using CF (3 CO))) ·Et (2) show that the, and enamidones trifluoromethyl residue were.',
    'Of particular interest, the Friedel–Crafts cyclization using (CF(3)CO)(2)O/BF(3)·Et(2)O show that near the expected keto-lactams, enamides and enamidones containing trifluoromethyl residue were isolated.',
    'FINDINGS: Initial clinical presentation included fever, chills, and myalgia in both patients, and for patient 1, diarrhoea.',
]
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, 0.7967, 0.4642],
#         [0.7967, 1.0000, 0.3914],
#         [0.4642, 0.3914, 1.0000]])

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Downstream Usage (Sentence Transformers)

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Evaluation

Metrics

Information Retrieval

• —Dataset: trec-covid
• —Evaluated with <code>InformationRetrievalEvaluator</code>

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Training Details

Training Dataset

Unnamed Dataset

• —Size: 1,204,106 training samples
• —Columns: <code>noisy</code> and <code>text</code>
• —Approximate statistics based on the first 1000 samples: | | noisy | text | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 3 tokens</li><li>mean: 22.09 tokens</li><li>max: 75 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 34.99 tokens</li><li>max: 75 tokens</li></ul> |
• —Samples: | noisy | text | |:-------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>of the mobile apps can potentially help mobile app developers improve or modify their mobile app achieve optimal outcomes</code> | <code>The results of the mobile apps assessment can potentially help mobile app developers improve or modify their existing mobile app designs to achieve optimal outcomes.</code> | | <code>In model such specialty of covid-19 virus as the presence of "super-spreaders" by a on a hierarchic landscape of social spreads infection</code> | <code>In particular, our model describes such a specialty of spread of covid-19 virus as the presence of "super-spreaders" who by performing a kind of random walk on a hierarchic landscape of social clusters spreads infection.</code> | | <code>The SARS-CoV-2 result</code> | <code>The primary outcome was SARS-CoV-2 test result.</code> |
• —Loss: <code>DenoisingAutoEncoderLoss</code> with these parameters:

  {
      "decoder_name_or_path": "google-bert/bert-base-uncased",
      "need_retokenization": false
  }

Evaluation Dataset

Unnamed Dataset

• —Size: 10,000 evaluation samples
• —Columns: <code>noisy</code> and <code>text</code>
• —Approximate statistics based on the first 1000 samples: | | noisy | text | |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 3 tokens</li><li>mean: 21.7 tokens</li><li>max: 75 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 33.89 tokens</li><li>max: 75 tokens</li></ul> |
• —Samples: | noisy | text | |:-----------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>The intraoperative hypertension and hypotension not the PHE non-PHE cohorts.</code> | <code>The frequency of intraoperative hypertension and hypotension did not differ between the PHE and non-PHE cohorts.</code> | | <code>, existing variants suffer problems: () large-scale optimization; (2) hyperparameter problem; high computational cost of the operations</code> | <code>However, the existing BSO variants can suffer from three problems: (1) large-scale optimization problem; (2) hyperparameter optimization problem; (3) high computational cost of the clustering operations.</code> | | <code>neurological impact of coronaviruses implications the novel SARS-CoV-2</code> | <code>Potential neurological impact of coronaviruses: implications for the novel SARS-CoV-2.</code> |
• —Loss: <code>DenoisingAutoEncoderLoss</code> with these parameters:

  {
      "decoder_name_or_path": "google-bert/bert-base-uncased",
      "need_retokenization": false
  }

Training Hyperparameters

Non-Default Hyperparameters

• —per_device_train_batch_size: 32
• —per_device_eval_batch_size: 32
• —learning_rate: 3e-05
• —num_train_epochs: 1
• —warmup_ratio: 0.1
• —fp16: True

All Hyperparameters

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

• —overwrite_output_dir: False
• —do_predict: False
• —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: 3e-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: 1
• —max_steps: -1
• —lr_scheduler_type: linear
• —lr_scheduler_kwargs: None
• —warmup_ratio: 0.1
• —warmup_steps: 0
• —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: True
• —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>

Training Time

• —Training: 3.9 hours
• —Evaluation: 33.6 minutes
• —Total: 4.5 hours

Framework Versions

• —Python: 3.12.13
• —Sentence Transformers: 5.4.1
• —Transformers: 4.57.6
• —PyTorch: 2.10.0+cu128
• —Accelerate: 1.13.0
• —Datasets: 5.0.0
• —Tokenizers: 0.22.2

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",
}

DenoisingAutoEncoderLoss

@inproceedings{wang-2021-TSDAE,
    title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning",
    author = "Wang, Kexin and Reimers, Nils and Gurevych, Iryna",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    pages = "671--688",
    url = "https://arxiv.org/abs/2104.06979",
}

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