all-mpnet-base-v2-patabs-1epoc-batch32-100

SentenceTransformer based on sentence-transformers/all-mpnet-base-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-mpnet-base-v2 on the json 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: sentence-transformers/all-mpnet-base-v2 <!-- at revision f1b1b820e405bb8644f5e8d9a3b98f9c9e0a3c58 -->
• Maximum Sequence Length: 384 tokens
• Output Dimensionality: 768 tokens
• Similarity Function: Cosine Similarity
• Training Dataset:
  • json

<!-- - **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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel 
  (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})
  (2): Normalize()
)

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("stephenhib/all-mpnet-base-v2-patabs-1epoc-batch32-100")
# Run inference
sentences = [
    'A computer-implemented method determines an orientation parameter value of a prosthetic component. The method includes receiving a first desired separation distance (d1) between a tibial prosthetic component (120) and a femoral prosthetic component (110) at a first flexion position (521) of a knee joint (100) and estimating a first estimated separation distance (g1) between the tibial prosthetic component and the femoral prosthetic component at the first flexion position of the knee joint for at least one potential orientation of the femoral prosthet¬ic component. The method also includes determining a first orientation para¬meter value of the femoral prosthetic component by comparing the first estimated separation distance to the first desired separation distance and out¬putting the first orientation parameter value via a user interface (400).',
    'SYSTEMS AND METHOD FOR PROSTHETIC COMPONENT ORIENTATION',
    'TWO-WAY VALVE FOR CONTROLLING A TEMPERATURE OF A COOLANT FOR AN INTERNAL COMBUSTION ENGINE',
]
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.* --> <!-- ## 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

json

• Dataset: json
• Size: 768,201 training samples
• Columns: <code>positive</code> and <code>anchor</code>
• Approximate statistics based on the first 1000 samples:

  	positive	anchor
  type	string	string
  details	<ul><li>min: 13 tokens</li><li>mean: 163.82 tokens</li><li>max: 384 tokens</li></ul>	<ul><li>min: 3 tokens</li><li>mean: 12.34 tokens</li><li>max: 73 tokens</li></ul>

• Samples:

  positive	anchor
  <code>According to an example aspect of the present invention, there is provided an apparatus and method to control mining vehicles, in particular as electric mining vehicles, taking into account the state of charge the batteries of said mining vehicles.</code>	<code>MINING VEHICLE CONTROL</code>
  <code>The invention is related to a new soft heterophasic random propylene copolymer with improved optical properties, as well as the process by which the heterophasic random propylene copolymer is produced.</code>	<code>SOFT HETEROPHASIC RANDOM PROPYLENE COPOLYMER WITH IMPROVED CLARITY</code>
  <code>The present invention relates to a valve assembly 10 for controlling a volute connecting opening 324 of a multi-channel turbine 500. The valve assembly 10 comprises a housing portion 300, a valve body 100 and an internal lever 200. The housing portion 300 defines a first volute channel 312, a second volute channel 314 and a volute connecting region 320. The housing portion 300 further comprises a cavity 340. The cavity 340 is separated from the volutes 312, 314 and can be accessed from outside the housing portion 300 via a housing opening 342 which extends from outside the housing portion 300 into the cavity 340. The volute connection region 320 is located between the first volute channel 312 and the second volute channel 314 and defines a volute connecting opening 324. The valve body 100 is inserted in the cavity 340 of the housing portion 300 and comprises at least one fin 120. The internal lever 200 is coupled with the valve body 100 and configured to pivotably move the valve body 100 between a first position and a second position. In the first position of the valve body 100, the fin 120 blocks the volute connecting opening 324. Thus, exhaust gases are substantially prevented from overflowing from the first volute channel 312 to the second volute channel 314 and vice versa. In the second position of the valve body 100 the fin 120 clears the volute connecting opening 324. Thus, exhaust gases are enabled to overflow from the first volute channel 312 to the second volute channel 314 and vice versa.</code>	<code>VALVE ASSEMBLY FOR MULTI-CHANNEL TURBINE</code>

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

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

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 4
• per_device_eval_batch_size: 2
• learning_rate: 2e-05
• num_train_epochs: 1
• warmup_ratio: 0.1
• bf16: True
• batch_sampler: no_duplicates

All Hyperparameters

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

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 4
• per_device_eval_batch_size: 2
• 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: {}
• 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
• use_ipex: False
• bf16: True
• fp16: False
• 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: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• fsdp_transformer_layer_cls_to_wrap: None
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': 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
• 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: False
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• 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
• dispatch_batches: None
• split_batches: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: False
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• use_liger_kernel: False
• eval_use_gather_object: False
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional

</details>

Framework Versions

• Python: 3.11.9
• Sentence Transformers: 3.2.0
• Transformers: 4.45.2
• PyTorch: 2.5.0+cu124
• Accelerate: 1.0.1
• Datasets: 3.0.1
• Tokenizers: 0.20.1

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",
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

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