omkar334/bert-base-uncased-retromae

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: 128 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("omkar334/bert-base-uncased-retromae")
# Run inference
sentences = [
    'A kid throwing axes at targets in a competition.',
    'The audit steps in this section should be used to assess the potential risks posed by the lack of management or user support.',
    'This photograph is happy',
]
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, 0.5798, 0.5999],
#         [0.5798, 1.0000, 0.5697],
#         [0.5999, 0.5697, 1.0000]])

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Evaluation

Metrics

Semantic Similarity

• —Datasets: sts-dev and sts-test
• —Evaluated with <code>EmbeddingSimilarityEvaluator</code>

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Training Details

Training Dataset

Unnamed Dataset

• —Size: 4,950 training samples
• —Columns: <code>text</code>
• —Approximate statistics based on the first 100 samples: | | text | |:---------|:----------------------------------------------------------------------------------| | type | string | | modality | text | | details | <ul><li>min: 4 tokens</li><li>mean: 17.63 tokens</li><li>max: 53 tokens</li></ul> |
• —Samples: | text | |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>oh i see i'm a former uh a former TI'er i just recently quit and so uh i got myself involved in a sales job and right now uh my list of books to be read have to do with uh the art of selling so</code> | | <code>and uh so i had her baby sitting but she was six months pregnant and it was getting too much for her so i just quit i'd rather quit and take care of my own kids than let somebody else raise them</code> | | <code>The PMG pulled out a new $50 bill and mailed to many boys' mothers.</code> |
• —Loss: <code>RetroMAELoss</code> with these parameters:

  {
      "encoder_mask_ratio": 0.15,
      "decoder_mask_ratio": 0.5,
      "encoder_mlm_loss_weight": 1.0
  }

Evaluation Dataset

Unnamed Dataset

• —Size: 50 evaluation samples
• —Columns: <code>text</code>
• —Approximate statistics based on the first 50 samples: | | text | |:---------|:---------------------------------------------------------------------------------| | type | string | | modality | text | | details | <ul><li>min: 6 tokens</li><li>mean: 16.1 tokens</li><li>max: 43 tokens</li></ul> |
• —Samples: | text | |:-------------------------------------------------------------------------------------------------------------------------------------------| | <code>The audit steps in this section should be used to assess the potential risks posed by the lack of management or user support.</code> | | <code>Rumor has it that the next object of touchy-feely bowdlerization by Disney is Beowulf.</code> | | <code>Here is a prediction.</code> |
• —Loss: <code>RetroMAELoss</code> with these parameters:

  {
      "encoder_mask_ratio": 0.15,
      "decoder_mask_ratio": 0.5,
      "encoder_mlm_loss_weight": 1.0
  }

Training Hyperparameters

Non-Default Hyperparameters

• —per_device_train_batch_size: 32
• —per_device_eval_batch_size: 32
• —learning_rate: 2e-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: 2e-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: 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

Training Time

• —Training: 2.5 minutes

Framework Versions

• —Python: 3.11.14
• —Sentence Transformers: 5.6.0.dev0
• —Transformers: 4.57.6
• —PyTorch: 2.12.0
• —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",
}

RetroMAELoss

@inproceedings{xiao-etal-2022-retromae,
    title = "{R}etro{MAE}: Pre-Training Retrieval-oriented Language Models Via Masked Auto-Encoder",
    author = "Xiao, Shitao and Liu, Zheng and Shao, Yingxia and Cao, Zhao",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.emnlp-main.35/",
}

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