sentence-transformers safetensors mpnet sentence-similarity feature-extraction generated_from_trainer dataset_size:768201 loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:1705.00652 base_model:sentence-transformers/all-mpnet-base-v2 base_model:finetune:sentence-transformers/all-mpnet-base-v2 autotrain_compatible endpoints_compatible region:us
---
base_model: sentence-transformers/all-mpnet-base-v2
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:768201
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: The present disclosure provides systems and methods to optimize
    data backup in a distributed enterprise system by firstly generating a set of
    unique files from all the files available in the enterprise. A backup set comprising
    files to be backed up are then generated from the set of unique files and backup
    is scheduled in the order in which the files to be backed up are identified. Unique
    files are generated based on file sharing patterns and communications among users
    that enable generating a social network graph from which one or more communities
    can be detected and deduplication can be performed on the files hosted by client
    systems in these communities thereby conserving resources.
  sentences:
  - BURNER
  - SYSTEMS AND METHODS FOR OPTIMIZED DATA BACKUP IN A DISTRIBUTED ENTERPRISE SYSTEM
  - Power conversion apparatus
- source_sentence: The present invention relates to a use of polypeptide compounds
    having dual agonist effect on glucagon-like peptide-1 receptor (GLP-1R) and glucagon
    receptor (GCGR). The polypeptide compounds are characterized by high enzymolysis
    stability, high potency and no adverse reaction, and capable of substantially
    improving hepatic fibrosis caused by hepatitis B virus (HBV) and hepatitis C virus
    (HCV) and severity of fibrotic conditions accompanied with liver diseases. The
    dual target agonist polypeptide derivatives are capable of preventing or treating
    hepatic fibrosis diseases associated with viral hepatitis.
  sentences:
  - GLP-1R/GCGR DUAL-TARGET AGONIST PEPTIDE DERIVATIVES FOR TREATMENT OF VIRAL HEPATITIS-RELATED
    HEPATIC FIBROSIS
  - MAGNETIC FILTER CARTRIDGE AND FILTER ASSEMBLY
  - USER TERMINAL AND WIRELESS COMMUNICATION METHOD
- source_sentence: A latch includes a latch housing including a first housing portion
    and a second housing portion separable from the first housing portion. The second
    housing portion includes a keeper. A first arm member is in rotational communication
    with the first housing portion. The first arm member is configured to rotate about
    a first axis between a first position and a second position. A second arm member
    is in rotational communication with the first arm member. A latch load pin is
    in rotational communication with the first arm member about a second axis. The
    latch load pin is configured to mate with the keeper with the first arm member
    in the first position. The second arm member in the first position is configured
    to be fixed relative to the first arm member as the first arm member rotates from
    the first position toward the second position.
  sentences:
  - UNLOCKING METHODS AND RELATED PRODUCTS
  - LATCH AND METHOD FOR OPERATING SAID LATCH
  - PANEL-SHAPED MOLDED ARTICLE AND PRODUCTION METHOD FOR PANEL-SHAPED MOLDED ARTICLE
- source_sentence: The present invention aims to provide a production method of low-fat
    and low-protein yogurt with smooth taste, suppressed syneresis and superior shape
    retainability, comprising adding protein glutaminase and starch to raw milk.
  sentences:
  - YOGURT PRODUCTION METHOD
  - Aircraft electric motor system
  - Floor panel, flooring system and method for laying flooring system
- source_sentence: 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).
  sentences:
  - Mobile device and antenna structure
  - TWO-WAY VALVE FOR CONTROLLING A TEMPERATURE OF A COOLANT FOR AN INTERNAL COMBUSTION
    ENGINE
  - SYSTEMS AND METHOD FOR PROSTHETIC COMPONENT ORIENTATION
---

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

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/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](https://huggingface.co/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](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### 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:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
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]
```

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## 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>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "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
```bibtex
@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
```bibtex
@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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