README.md

---

tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:12800
- loss:MultipleNegativesRankingLoss
base_model: microsoft/MiniLM-L12-H384-uncased
widget:
- source_sentence: A man in an orange shirt and a man in a white shirt are at a vegetable
    stand, and the man in the orange shirt gives a woman wearing red some vegetables.
  sentences:
  - Two men at a food stand.
  - Two teams play each other in a soccer game.
  - Two men are outside working on a building.
- source_sentence: A young girl wearing jeans and a white shirt is standing on grass
    holding dandelions in her hands.
  sentences:
  - A lady retrieving clothes from the dryer.
  - The girl is wearing jeans.
  - The boy is using a computer
- source_sentence: Young individuals in uniforms are gathering under the flags of
    several different countries.
  sentences:
  - A toddler is wearing maroon.
  - A man fishes.
  - A youth group is gathering near multi-national banners.
- source_sentence: A young person wearing a pink one-piece swimsuit and goggles about
    to complete a leap into a swimming pool.
  sentences:
  - People walk by artwork.
  - A crowd in Japan gathers in grassy area.
  - A young person has on clothes.
- source_sentence: A man wearing a Gas mask attempts a flying kick toward another
    man wearing a mask and red shirt in a martial arts stance.
  sentences:
  - A man carries a basket on his head.
  - Two men fighting in the martial arts.
  - The men are working together on the same window
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---


# SentenceTransformer based on microsoft/MiniLM-L12-H384-uncased

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased). 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:** [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) <!-- at revision 44acabbec0ef496f6dbc93adadea57f376b7c0ec -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 dimensions
- **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': 512, 'do_lower_case': False, 'architecture': '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})

)

```

## 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("Borsa356/bert_mnr_5")

# Run inference

sentences = [

    'A man wearing a Gas mask attempts a flying kick toward another man wearing a mask and red shirt in a martial arts stance.',

    'Two men fighting in the martial arts.',

    'The men are working together on the same window',

]

embeddings = model.encode(sentences)

print(embeddings.shape)

# [3, 384]



# Get the similarity scores for the embeddings

similarities = model.similarity(embeddings, embeddings)

print(similarities)

# tensor([[1.0000, 0.6838, 0.1621],

#         [0.6838, 1.0000, 0.2773],

#         [0.1621, 0.2773, 1.0000]])

```

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 12,800 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                        | sentence_1                                                                       |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                           |
  | details | <ul><li>min: 5 tokens</li><li>mean: 16.84 tokens</li><li>max: 86 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.72 tokens</li><li>max: 25 tokens</li></ul> |
* Samples:
  | sentence_0                                                                                                               | sentence_1                                                         |
  |:-------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------|
  | <code>A man is working a hotdog stand.</code>                                                                            | <code>A person is running a small location that sells food.</code> |
  | <code>The man, only visible as a black silhouette, took a picture along a brightly colored wall full of graffiti.</code> | <code>The wall is decorated with bright colors.</code>             |
  | <code>A woman wearing a sweater and jeans is playing on a tire swing with two little children in an outdoor park.</code> | <code>A woman and two children are in the park.</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

- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `batch_sampler`: no_duplicates

- `multi_dataset_batch_sampler`: round_robin



#### All Hyperparameters

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



- `overwrite_output_dir`: False

- `do_predict`: False
- `eval_strategy`: no
- `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`: 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`: 3
- `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
- `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`: 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, '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`: 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`: False
- `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`: False
- `prompts`: None
- `batch_sampler`: no_duplicates

- `multi_dataset_batch_sampler`: round_robin

- `router_mapping`: {}
- `learning_rate_mapping`: {}

</details>

### Training Logs
| Epoch | Step | Training Loss |
|:-----:|:----:|:-------------:|
| 1.25  | 500  | 0.6989        |
| 2.5   | 1000 | 0.3136        |


### Framework Versions
- Python: 3.13.5
- Sentence Transformers: 5.0.0
- Transformers: 4.53.0
- PyTorch: 2.7.1+cpu
- Accelerate: 1.8.1
- Datasets: 3.6.0
- Tokenizers: 0.21.2

## 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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