README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:19598
- loss:CoSENTLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: soi tỷ lệ Southampton vs Nottingham (21h00, 24/8), vòng 2 Ngoại
    hạng Anh
  sentences:
  - nhận định Oakleigh Cannons vs Macarthur
  - dự đoán Mallorca vs Athletic Bilbao
  - soi kèo Southampton vs Nottingham Forest
- source_sentence: Melbourne Victory vs Macarthur 12h00 ngày 3/11 (VĐQG Australia
    2024/25).
  sentences:
  - tỷ lệ Tijuana vs Leon
  - 'Melbourne Victory vs Brisbane Roar  '
  - Hải Phòng vs SHB Đà Nẵng
- source_sentence: Banfield vs Estudiantes 4h00 ngày 8/10 (VĐQG Argentina 2024).
  sentences:
  - arsenal vs psg
  - Shandong Luneng vs Qingdaoyangcheng
  - 'Boca Juniors vs River Plate  '
- source_sentence: 'St Pauli vs Bayern Munich (21h30 ngày 9/11): Khó có bất ngờ.'
  sentences:
  - st pauli vs bayern munich
  - Seattle Sounders vs Houston Dynamo
  - kyrgyzstan vs triều tiên
- source_sentence: 'Juventus vs Napoli (23h00 ngày 21/9): Không dễ cho chủ nhà.'
  sentences:
  - cruz azul vs juarez
  - Real Madrid vs Barcelona
  - El Salvador vs Montserrat
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
model-index:
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
  results:
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: sport query title dev
      type: sport_query_title_dev
    metrics:
    - type: cosine_accuracy
      value: 0.9943877551020408
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.6410836577415466
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.9943269726663229
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.6107593178749084
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.9958677685950413
      name: Cosine Precision
    - type: cosine_recall
      value: 0.9927909371781668
      name: Cosine Recall
    - type: cosine_ap
      value: 0.9995956398472251
      name: Cosine Ap
---

# 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) on the csv dataset. 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 fa97f6e7cb1a59073dff9e6b13e2715cf7475ac9 -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - csv
<!-- - **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("Tam1032/MiniLM6-v2-sport")
# Run inference
sentences = [
    'Juventus vs Napoli (23h00 ngày 21/9): Không dễ cho chủ nhà.',
    'Real Madrid vs Barcelona',
    'El Salvador vs Montserrat',
]
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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## Evaluation

### Metrics

#### Binary Classification

* Dataset: `sport_query_title_dev`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

| Metric                    | Value      |
|:--------------------------|:-----------|
| cosine_accuracy           | 0.9944     |
| cosine_accuracy_threshold | 0.6411     |
| cosine_f1                 | 0.9943     |
| cosine_f1_threshold       | 0.6108     |
| cosine_precision          | 0.9959     |
| cosine_recall             | 0.9928     |
| **cosine_ap**             | **0.9996** |

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## Training Details

### Training Dataset

#### csv

* Dataset: csv
* Size: 19,598 training samples
* Columns: <code>hypothesis</code>, <code>premise</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | hypothesis                                                                         | premise                                                                          | label                                           |
  |:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                             | string                                                                           | int                                             |
  | details | <ul><li>min: 12 tokens</li><li>mean: 27.44 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 9.63 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>0: ~50.20%</li><li>1: ~49.80%</li></ul> |
* Samples:
  | hypothesis                                                                             | premise                                                 | label          |
  |:---------------------------------------------------------------------------------------|:--------------------------------------------------------|:---------------|
  | <code>bóng đá Las Palmas vs Girona, 23h30 ngày 26/10: Trừng phạt chủ nhà.</code>       | <code>Las Palmas vs Girona</code>                       | <code>1</code> |
  | <code>Seattle Sounders vs Houston Dynamo 9h30 ngày 29/9 (Nhà nghề Mỹ 2024).</code>     | <code>dự đoán Seattle Sounders vs Houston Dynamo</code> | <code>1</code> |
  | <code>bóng đá Tây Ban Nha vs Đan Mạch, 01h45 ngày 13/10: Khuất phục ‘lính chì’.</code> | <code>bóng đá Tây Ban Nha vs Đan Mạch</code>            | <code>1</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```

### Evaluation Dataset

#### csv

* Dataset: csv
* Size: 19,598 evaluation samples
* Columns: <code>hypothesis</code>, <code>premise</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | hypothesis                                                                         | premise                                                                          | label                                           |
  |:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                             | string                                                                           | int                                             |
  | details | <ul><li>min: 12 tokens</li><li>mean: 27.15 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.55 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>0: ~51.40%</li><li>1: ~48.60%</li></ul> |
* Samples:
  | hypothesis                                                                | premise                                | label          |
  |:--------------------------------------------------------------------------|:---------------------------------------|:---------------|
  | <code>Hải Phòng vs CAHN (19h15 ngày 15/9): Điểm tựa sân nhà.</code>       | <code>kết quả Hải Phòng vs CAHN</code> | <code>1</code> |
  | <code>Kuwait vs Jordan 1h15 ngày 20/11 (Vòng loại World Cup 2026).</code> | <code>Kuwait vs Iraq</code>            | <code>0</code> |
  | <code>bóng đá Parma vs Empoli 18h30 ngày 27/10 (Serie A 2024/25).</code>  | <code>nhận định Parma vs Empoli</code> | <code>1</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```

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

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `warmup_ratio`: 0.1
- `fp16`: True

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `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`: 5e-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`: 3
- `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`: False
- `fp16`: True
- `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
- `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
- `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
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | Validation Loss | sport_query_title_dev_cosine_ap |
|:------:|:----:|:-------------:|:---------------:|:-------------------------------:|
| 1.0    | 1103 | -             | 0.1376          | 0.9991                          |
| 1.4506 | 1600 | 0.3994        | -               | -                               |
| 2.0    | 2206 | -             | 0.0693          | 0.9994                          |
| 2.9012 | 3200 | 0.0442        | -               | -                               |
| 3.0    | 3309 | -             | 0.0534          | 0.9996                          |


### Framework Versions
- Python: 3.11.7
- Sentence Transformers: 3.3.1
- Transformers: 4.47.0
- PyTorch: 2.5.1+cu124
- Accelerate: 1.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0

## 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",
}
```

#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}
```

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