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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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