README.md

---

language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:75
- loss:CosineSimilarityLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
datasets: []
widget:
- source_sentence: This store featured in the SavaCentre TV adverts in 1983.
  sentences:
  - I love the Scream movies and all horror movies and this one ranks way up there.
  - Development of synchronous toothed-belts was halted by the Gilmer company prior
    to 1940.
  - This store was not featured in the SavaCentre TV promotions in 1983.
- source_sentence: In 2014, Nextgen earns KLAS Top Performance Honors for Ambulatory
    RCM Services.
  sentences:
  - These strategies employ reporter transposon s and in vitro expression technology
    (IVET).
  - In 2014, Nextgen fails to achieve KLAS Top Performance Honors for Ambulatory RCM
    Services.
  - The film's sole bright spot was Jonah Hill (who will look almost unrecognizable
    to fans of the recent Superbad due to the amount of weight he lost in the interim).
- source_sentence: E105 has never been implicated in atopic asthma.
  sentences:
  - E105 has been implicated in non-atopic asthma.
  - The species is named in honor of the divorce of Sara Anderson and Malcolm Slaney.
  - Each annex to a filed document is not required to have page numbering.
- source_sentence: Additionally, a church at San Lazaro in Orange Walk District escaped
    all damage.
  sentences:
  - Kuwait has a reputation for being the central music influence of the GCC countries.
  - Early settlers may have introduced it 4,000 years ago.
  - Additionally, a church at San Lazaro in Orange Walk District suffered severe damage.
- source_sentence: The content in Australia is lower than in other reports.
  sentences:
  - Other reports also show a content lower than 0.1% in Australia.
  - Commercial DNP is unable to be utilized as an antiseptic or as a non-selective
    bioaccumulating pesticide.
  - Installation of Halon systems is mandated by the European Union.
pipeline_tag: sentence-similarity
---


# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co./sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-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-MiniLM-L6-v2](https://huggingface.co./sentence-transformers/all-MiniLM-L6-v2) <!-- at revision 8b3219a92973c328a8e22fadcfa821b5dc75636a -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **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': 256, 'do_lower_case': False}) with Transformer model: BertModel 

  (1): Pooling({'word_embedding_dimension': 384, '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("LeoChiuu/all-MiniLM-L6-v2-negations")

# Run inference

sentences = [

    'The content in Australia is lower than in other reports.',

    'Other reports also show a content lower than 0.1% in Australia.',

    'Installation of Halon systems is mandated by the European Union.',

]

embeddings = model.encode(sentences)

print(embeddings.shape)

# [3, 384]



# 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

#### Unnamed Dataset


* Size: 75 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                        | sentence_1                                                                        | label                                           |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                            | string                                                                            | int                                             |
  | details | <ul><li>min: 9 tokens</li><li>mean: 16.36 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 16.55 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>0: ~61.33%</li><li>1: ~38.67%</li></ul> |
* Samples:
  | sentence_0                                                                                   | sentence_1                                                                                | label          |
  |:---------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------|:---------------|
  | <code>It wasn't an inexpensive piece, but I would still have expected better quality.</code> | <code>It was an inexpensive piece, but I would still have expected better quality.</code> | <code>0</code> |
  | <code>My name is noncrucial.</code>                                                          | <code>My name is important.</code>                                                        | <code>0</code> |
  | <code>Hawthorne mostly wrote against his own religious belief.</code>                        | <code>Hawthorne wrote against his beliefs.</code>                                         | <code>1</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
  ```json

  {

      "loss_fct": "torch.nn.modules.loss.MSELoss"

  }

  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 10
- `multi_dataset_batch_sampler`: round_robin



#### All Hyperparameters

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



- `overwrite_output_dir`: False

- `do_predict`: False
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 10
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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
- `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`: 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, '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_sampler`: batch_sampler

- `multi_dataset_batch_sampler`: round_robin



</details>



### Framework Versions

- Python: 3.11.9

- Sentence Transformers: 3.0.1

- Transformers: 4.40.2

- PyTorch: 2.3.0+cpu

- Accelerate: 0.32.1

- Datasets: 2.19.1

- Tokenizers: 0.19.1



## 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",

}

```



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