---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:50
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: Two men on bicycles competing in a race.
  sentences:
  - People are riding bikes.
  - A woman is doing a cartwheel.
  - A few people are catching fish.
- source_sentence: A man selling donuts to a customer during a world exhibition event
    held in the city of Angeles
  sentences:
  - An Indian woman is doing her laundry in a lake.
  - A man selling donuts to a customer.
  - A woman drinks her coffee in a small cafe.
- source_sentence: Kids are on a amusement ride.
  sentences:
  - A car is broke down on the side of the road.
  - A man is wearing a blue shirt
  - Kids ride an amusement ride.
- source_sentence: Two young children in blue jerseys, one with the number 9 and one
    with the number 2 are standing on wooden steps in a bathroom and washing their
    hands in a sink.
  sentences:
  - A woman is doing a cartwheel.
  - Two kids in jackets walk to school.
  - Two kids in numbered jerseys wash their hands.
- source_sentence: Two women having drinks and smoking cigarettes at the bar.
  sentences:
  - boys play football
  - Three women are at a bar.
  - Two women are at a bar.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: all-MiniLM-L6-v2 finetuned on AllNLI
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoMSMARCO
      type: NanoMSMARCO
    metrics:
    - type: cosine_accuracy@1
      value: 0.36
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.52
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.58
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.36
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.1733333333333333
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.11599999999999999
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.36
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.52
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.58
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5537649594026555
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.47934920634920636
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.4904052935766491
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoNFCorpus
      type: NanoNFCorpus
    metrics:
    - type: cosine_accuracy@1
      value: 0.4
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.6
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.62
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.72
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.4
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.35999999999999993
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.324
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.276
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.03458800957047009
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.06249836236458624
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.08046568973676278
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.13259147712553063
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.32368179953782333
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4918571428571428
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.1389531634193327
      name: Cosine Map@100
  - task:
      type: nano-beir
      name: Nano BEIR
    dataset:
      name: NanoBEIR mean
      type: NanoBEIR_mean
    metrics:
    - type: cosine_accuracy@1
      value: 0.38
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.56
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.76
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.38
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2666666666666666
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.22
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.17800000000000002
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.19729400478523504
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.29124918118229315
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3302328448683814
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.46629573856276535
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4387233794702394
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4856031746031746
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.3146792284979909
      name: Cosine Map@100
---

# all-MiniLM-L6-v2 finetuned on AllNLI

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) on the all-nli dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for retrieval.

## 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 1110a243fdf4706b3f48f1d95db1a4f5529b4d41 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
- **Supported Modality:** Text
- **Training Dataset:**
    - all-nli
- **Language:** en
- **License:** apache-2.0

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'})
  (1): Pooling({'embedding_dimension': 384, 'pooling_mode': 'mean', '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("Hyperakan/all-MiniLM-L6-v2-smoke")
# Run inference
queries = [
    'Two women having drinks and smoking cigarettes at the bar.',
]
documents = [
    'Two women are at a bar.',
    'Three women are at a bar.',
    'boys play football',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 384] [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[ 0.7893,  0.6667, -0.1284]])
```
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## Evaluation

### Metrics

#### Information Retrieval

* Datasets: `NanoMSMARCO` and `NanoNFCorpus`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.InformationRetrievalEvaluator)

| Metric              | NanoMSMARCO | NanoNFCorpus |
|:--------------------|:------------|:-------------|
| cosine_accuracy@1   | 0.36        | 0.4          |
| cosine_accuracy@3   | 0.52        | 0.6          |
| cosine_accuracy@5   | 0.58        | 0.62         |
| cosine_accuracy@10  | 0.8         | 0.72         |
| cosine_precision@1  | 0.36        | 0.4          |
| cosine_precision@3  | 0.1733      | 0.36         |
| cosine_precision@5  | 0.116       | 0.324        |
| cosine_precision@10 | 0.08        | 0.276        |
| cosine_recall@1     | 0.36        | 0.0346       |
| cosine_recall@3     | 0.52        | 0.0625       |
| cosine_recall@5     | 0.58        | 0.0805       |
| cosine_recall@10    | 0.8         | 0.1326       |
| **cosine_ndcg@10**  | **0.5538**  | **0.3237**   |
| cosine_mrr@10       | 0.4793      | 0.4919       |
| cosine_map@100      | 0.4904      | 0.139        |

#### Nano BEIR

* Dataset: `NanoBEIR_mean`
* Evaluated with [<code>NanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.NanoBEIREvaluator) with these parameters:
  ```json
  {
      "dataset_names": [
          "msmarco",
          "nfcorpus"
      ],
      "dataset_id": "sentence-transformers/NanoBEIR-en"
  }
  ```

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.38       |
| cosine_accuracy@3   | 0.56       |
| cosine_accuracy@5   | 0.6        |
| cosine_accuracy@10  | 0.76       |
| cosine_precision@1  | 0.38       |
| cosine_precision@3  | 0.2667     |
| cosine_precision@5  | 0.22       |
| cosine_precision@10 | 0.178      |
| cosine_recall@1     | 0.1973     |
| cosine_recall@3     | 0.2912     |
| cosine_recall@5     | 0.3302     |
| cosine_recall@10    | 0.4663     |
| **cosine_ndcg@10**  | **0.4387** |
| cosine_mrr@10       | 0.4856     |
| cosine_map@100      | 0.3147     |

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## Training Details

### Training Dataset

#### all-nli

* Dataset: all-nli
* Size: 50 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 50 samples:
  |          | anchor                                                                           | positive                                                                         | negative                                                                          |
  |:---------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type     | string                                                                           | string                                                                           | string                                                                            |
  | modality | text                                                                             | text                                                                             | text                                                                              |
  | details  | <ul><li>min: 8 tokens</li><li>mean: 21.7 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.4 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.34 tokens</li><li>max: 30 tokens</li></ul> |
* Samples:
  | anchor                                                                     | positive                                         | negative                                                   |
  |:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------|
  | <code>A person on a horse jumps over a broken down airplane.</code>        | <code>A person is outdoors, on a horse.</code>   | <code>A person is at a diner, ordering an omelette.</code> |
  | <code>Children smiling and waving at camera</code>                         | <code>There are children present</code>          | <code>The kids are frowning</code>                         |
  | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</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",
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }
  ```

### Evaluation Dataset

#### all-nli

* Dataset: all-nli
* Size: 20 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 20 samples:
  |          | anchor                                                                           | positive                                                                         | negative                                                                          |
  |:---------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type     | string                                                                           | string                                                                           | string                                                                            |
  | modality | text                                                                             | text                                                                             | text                                                                              |
  | details  | <ul><li>min: 9 tokens</li><li>mean: 19.3 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 9.55 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.05 tokens</li><li>max: 15 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                                                                         | positive                                                    | negative                                                |
  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------|
  | <code>Two women are embracing while holding to go packages.</code>                                                                                                             | <code>Two woman are holding packages.</code>                | <code>The men are fighting outside a deli.</code>       |
  | <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>Two kids in numbered jerseys wash their hands.</code> | <code>Two kids in jackets walk to school.</code>        |
  | <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code>                                                                    | <code>A man selling donuts to a customer.</code>            | <code>A woman drinks her coffee in a small cafe.</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",
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }
  ```

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

- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `weight_decay`: 0.01
- `num_train_epochs`: 1
- `max_steps`: 1
- `warmup_ratio`: 0.1
- `seed`: 12
- `bf16`: True
- `load_best_model_at_end`: True
- `batch_sampler`: no_duplicates

#### 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
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.01
- `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`: None
- `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`: 12
- `data_seed`: None
- `jit_mode_eval`: 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`: True
- `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}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `project`: huggingface
- `trackio_space_id`: trackio
- `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`: no
- `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`: True
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}

</details>

### Training Logs
| Epoch    | Step  | Training Loss | Validation Loss | NanoMSMARCO_cosine_ndcg@10 | NanoNFCorpus_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 |
|:--------:|:-----:|:-------------:|:---------------:|:--------------------------:|:---------------------------:|:----------------------------:|
| -1       | -1    | -             | -               | 0.5538                     | 0.3237                      | 0.4387                       |
| **0.25** | **1** | **0.5675**    | **0.1029**      | **0.554**                  | **0.3235**                  | **0.4387**                   |
| -1       | -1    | -             | -               | 0.5538                     | 0.3237                      | 0.4387                       |

* The bold row denotes the saved checkpoint.

### Training Time
- **Training**: 12.6 seconds
- **Evaluation**: 8.9 seconds
- **Total**: 21.5 seconds

### Framework Versions
- Python: 3.12.13
- Sentence Transformers: 5.6.0
- Transformers: 4.57.6
- PyTorch: 2.5.1+cu121
- Accelerate: 1.14.0
- Datasets: 5.0.0
- Tokenizers: 0.22.2

## 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{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}
```

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