README.md

---
base_model: huudan123/model_stage2
datasets: []
language: []
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:102645
- loss:CosineSimilarityLoss
widget:
- source_sentence: Tổng thống Bulgaria cố gắng phá vỡ bế tắc bầu cử
  sentences:
  - Maldives tổ chức bầu cử tổng thống mới
  - Cháy rừng Oklahoma phá hủy nhà cửa - trong ảnh
  - Một đang đi dọc theo một hồ bơi.
- source_sentence: Mel Smith qua đời ở tuổi 60 và Vương quốc Anh thương tiếc một bộ
    phim hài yêu thích
  sentences:
  - 'GL, Terral Hi Corn: Vậy, bạn thực sự tin vào từng lời của Terral đã viết?'
  - Margaret Thatcher, cựu Thủ tướng Anh, qua đời ở tuổi 87
  - Mỹ giúp cung cấp vũ khí cho phiến quân Syria
- source_sentence: Một chui ra phía sau xe tải.
  sentences:
  - Nhân kỷ niệm 50 năm ngày mất của JFK, Dallas tổ chức lễ tưởng niệm đầu tiên
  - Cổ phiếu Allegiant tăng 4 USD, tương đương 17,2%, lên 27,43 USD trong phiên giao
    dịch sáng thứ Năm trên thị trường chứng khoán Nasdaq.
  - Một cô gái trẻ đội mũ bảo hiểm xe đạp với một chiếc xe đạp ở phía sau.
- source_sentence: AL gia hạn lên án bạo lực ở Syria
  sentences:
  - Tòa án Ai Cập ra lệnh thả Mubarak
  - Obama lên án bạo lực Ai Cập, hủy bỏ các cuộc tập trận quân sự
  - Trái phiếu kỳ hạn 30 năm US30YT = RR giảm 14/32 với lợi suất 4,26% từ 4,23%.
- source_sentence: Thật nực cười khi tôi thấy các hãng hàng không đôi khi yêu cầu
    tắt những thứ này.
  sentences:
  - Tôi rất tiếc khi nghe điều này Kelly.
  - Hàng loạt các cuộc tấn công Iraq giết chết ít nhất sáu người
  - 'Các cuộc tấn công mạng được coi là mối đe dọa ngày càng tăng đối với mạng máy
    tính quân sự và dân sự. '
model-index:
- name: SentenceTransformer based on huudan123/model_stage2
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts evaluator
      type: sts-evaluator
    metrics:
    - type: pearson_cosine
      value: 0.05287418847635471
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.33628129091743275
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.15493487298707004
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.3373742409125596
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.15533169047001907
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.33611237346676887
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.05498172874565448
      name: Pearson Dot
    - type: spearman_dot
      value: 0.05788159269305955
      name: Spearman Dot
    - type: pearson_max
      value: 0.15533169047001907
      name: Pearson Max
    - type: spearman_max
      value: 0.3373742409125596
      name: Spearman Max
---

# SentenceTransformer based on huudan123/model_stage2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [huudan123/model_stage2](https://huggingface.co/huudan123/model_stage2). It maps sentences & paragraphs to a 768-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:** [huudan123/model_stage2](https://huggingface.co/huudan123/model_stage2) <!-- at revision 78216f64916cdd3714bc707046c014a6f562e89b -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 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': 512, 'do_lower_case': False}) with Transformer model: RobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, '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("huudan123/final_model_main")
# Run inference
sentences = [
    'Thật nực cười khi tôi thấy các hãng hàng không đôi khi yêu cầu tắt những thứ này.',
    'Tôi rất tiếc khi nghe điều này Kelly.',
    'Hàng loạt các cuộc tấn công Iraq giết chết ít nhất sáu người',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

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### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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

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## Evaluation

### Metrics

#### Semantic Similarity
* Dataset: `sts-evaluator`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric             | Value      |
|:-------------------|:-----------|
| pearson_cosine     | 0.0529     |
| spearman_cosine    | 0.3363     |
| pearson_manhattan  | 0.1549     |
| spearman_manhattan | 0.3374     |
| pearson_euclidean  | 0.1553     |
| spearman_euclidean | 0.3361     |
| pearson_dot        | 0.055      |
| spearman_dot       | 0.0579     |
| pearson_max        | 0.1553     |
| **spearman_max**   | **0.3374** |

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## Training Details

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

- `overwrite_output_dir`: True
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `learning_rate`: 2e-05
- `num_train_epochs`: 30
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
- `gradient_checkpointing`: True

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

- `overwrite_output_dir`: True
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-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`: 30
- `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`: 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}
- `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`: True
- `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_eval_metrics`: False
- `eval_on_start`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch    | Step     | Training Loss | loss       | sts-evaluator_spearman_max |
|:--------:|:--------:|:-------------:|:----------:|:--------------------------:|
| 0        | 0        | -             | -          | 0.6240                     |
| 0.6234   | 500      | 0.0186        | -          | -                          |
| 1.0      | 802      | -             | 0.0215     | 0.7037                     |
| 1.2469   | 1000     | 0.0071        | -          | -                          |
| 1.8703   | 1500     | 0.0063        | -          | -                          |
| 2.0      | 1604     | -             | 0.0216     | 0.7184                     |
| 2.4938   | 2000     | 0.0057        | -          | -                          |
| 3.0      | 2406     | -             | 0.0200     | 0.7298                     |
| 3.1172   | 2500     | 0.0055        | -          | -                          |
| 3.7406   | 3000     | 0.0052        | -          | -                          |
| 4.0      | 3208     | -             | 0.0175     | 0.7733                     |
| 4.3641   | 3500     | 0.005         | -          | -                          |
| 4.9875   | 4000     | 0.005         | -          | -                          |
| 5.0      | 4010     | -             | 0.0144     | 0.7820                     |
| 5.6110   | 4500     | 0.0046        | -          | -                          |
| 6.0      | 4812     | -             | 0.0135     | 0.7839                     |
| 6.2344   | 5000     | 0.0045        | -          | -                          |
| 6.8579   | 5500     | 0.0043        | -          | -                          |
| 7.0      | 5614     | -             | 0.0132     | 0.7867                     |
| 7.4813   | 6000     | 0.0041        | -          | -                          |
| 8.0      | 6416     | -             | 0.0113     | 0.7894                     |
| 8.1047   | 6500     | 0.004         | -          | -                          |
| 8.7282   | 7000     | 0.0037        | -          | -                          |
| 9.0      | 7218     | -             | 0.0105     | 0.7845                     |
| 9.3516   | 7500     | 0.0036        | -          | -                          |
| 9.9751   | 8000     | 0.0037        | -          | -                          |
| **10.0** | **8020** | **-**         | **0.0096** | **0.7963**                 |
| 10.5985  | 8500     | 0.0074        | -          | -                          |
| 11.0     | 8822     | -             | 0.2441     | 0.3470                     |
| 11.2219  | 9000     | 0.0065        | -          | -                          |
| 11.8454  | 9500     | 0.0063        | -          | -                          |
| 12.0     | 9624     | -             | 0.2443     | 0.2869                     |
| 12.4688  | 10000    | 0.0062        | -          | -                          |
| 13.0     | 10426    | -             | 0.2446     | 0.2917                     |
| 13.0923  | 10500    | 0.0061        | -          | -                          |
| 13.7157  | 11000    | 0.006         | -          | -                          |
| 14.0     | 11228    | -             | 0.2446     | 0.3374                     |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.3.1+cu121
- Accelerate: 0.33.0
- Datasets: 2.20.0
- 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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