crash_encoder2-sts

SentenceTransformer based on distilbert/distilbert-base-uncased

This is a sentence-transformers model finetuned from distilbert/distilbert-base-uncased. 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: distilbert/distilbert-base-uncased <!-- at revision 12040accade4e8a0f71eabdb258fecc2e7e948be -->
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 tokens
• Similarity Function: Cosine Similarity

<!-- - **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({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel 
  (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("sentence_transformers_model_id")
# Run inference
sentences = [
    'T ENGINE TRANS TOP LAT 90 Deg Front 2025 U717 G-S',
    'T R F ACTIVE VENT SQUIB VOLT 90 Deg Front 2021 P702 VOLTS',
    'T ENGINE TRANS TOP LAT 30 Deg Front Angular Left 2020 P558 G-S',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

<!-- ### 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.* -->

Evaluation

Metrics

Semantic Similarity

• Dataset: sts-dev
• Evaluated with <code>EmbeddingSimilarityEvaluator</code>

Semantic Similarity

• Dataset: sts-dev
• Evaluated with <code>EmbeddingSimilarityEvaluator</code>

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Training Details

Training Dataset

Unnamed Dataset

• Size: 8,081,275 training samples
• Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	<ul><li>min: 23 tokens</li><li>mean: 31.48 tokens</li><li>max: 40 tokens</li></ul>	<ul><li>min: 16 tokens</li><li>mean: 30.06 tokens</li><li>max: 55 tokens</li></ul>	<ul><li>min: 0.0</li><li>mean: 0.44</li><li>max: 1.0</li></ul>

• Samples:

  sentence1	sentence2	score
  <code>T L F DUMMY PELVIS VERT Dynamic Seat Sled Test 2025 U718 G-S</code>	<code>T SCS R2 HY REF 059 R C PLR REF Y SM LAT 90 Deg / Left Side Decel-4g 2020 CX483 G-S</code>	<code>0.21129386503072142</code>
  <code>T L F DUMMY PELVIS VERT Dynamic Seat Sled Test 2025 U718 G-S</code>	<code>T R F DUMMY PELVIS VERT 75 Deg Oblique Right Side 10 in. Pole 2015 P552 G-S</code>	<code>0.4972955033248179</code>
  <code>T L F DUMMY PELVIS VERT Dynamic Seat Sled Test 2025 U718 G-S</code>	<code>T SCS L1 HY REF 053 L B PLR REF Y SM LAT 90 Deg Front Bumper Override 2021 CX727 G-S</code>	<code>0.5701051768787058</code>

• Loss: <code>CoSENTLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  

Evaluation Dataset

Unnamed Dataset

• Size: 1,726,581 evaluation samples
• Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	<ul><li>min: 22 tokens</li><li>mean: 25.0 tokens</li><li>max: 30 tokens</li></ul>	<ul><li>min: 16 tokens</li><li>mean: 31.04 tokens</li><li>max: 53 tokens</li></ul>	<ul><li>min: 0.0</li><li>mean: 0.44</li><li>max: 1.0</li></ul>

• Samples:

  sentence1	sentence2	score
  <code>T R F ADAPTIVE TETHER VENT SQUIB VOLT 30 Deg Front Angular Right 20xx GENERIC VOLTS</code>	<code>T L F DUMMY T12 LONG 27 Deg Crabbed Left Side NHTSA 214 MDB to vehicle 2015 P552 G-S</code>	<code>0.6835618484879796</code>
  <code>T R F ADAPTIVE TETHER VENT SQUIB VOLT 30 Deg Front Angular Right 20xx GENERIC VOLTS</code>	<code>T L F DUMMY R FEMUR LONG 90 Deg Front 2022 U553 G-S</code>	<code>0.666531064739</code>
  <code>T R F ADAPTIVE TETHER VENT SQUIB VOLT 30 Deg Front Angular Right 20xx GENERIC VOLTS</code>	<code>T R F DUMMY NECK UPPER MZ LOAD 90 Deg Front 2019 P375ICA IN-LBS</code>	<code>0.46391834212079874</code>

• Loss: <code>CoSENTLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 32
• per_device_eval_batch_size: 32
• learning_rate: 3e-05
• num_train_epochs: 4
• 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
• 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: 4
• max_steps: -1
• lr_scheduler_type: linear
• 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
• 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: 4
• ddp_backend: None
• tpu_num_cores: None
• tpu_metrics_debug: False
• debug: []
• dataloader_drop_last: True
• dataloader_num_workers: 0
• 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_grad_ckpt': False}
• fsdp_transformer_layer_cls_to_wrap: None
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch
• 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
• 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: False
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• 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
• dispatch_batches: None
• split_batches: False
• include_tokens_per_second: False
• neftune_noise_alpha: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional

</details>

Training Logs

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

Framework Versions

• Python: 3.10.6
• Sentence Transformers: 3.0.0
• Transformers: 4.35.0
• PyTorch: 2.1.0a0+4136153
• Accelerate: 0.30.1
• Datasets: 2.14.1
• Tokenizers: 0.14.1

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",
}

CoSENTLoss

@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}

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