README.md

---
base_model: huudan123/model_stage3_2_score
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:96895
- loss:CosineSimilarityLoss
widget:
- source_sentence: 'Gorgich và Pashtoon bị xử tử trong tù. '
  sentences:
  - Chỉ trích Mubarak của Ai Cập
  - NKorea xử tử chú của Kim Jong Un
  - 'Phiến quân thân Nga bắn rơi máy bay Malaysia: Ukraine'
- source_sentence: LHQ quan ngại về khả năng vũ khí hóa học của Syria
  sentences:
  - Nhân viên bệnh viện Texas xét nghiệm dương tính với Ebola
  - Mỹ và Nga đạt thỏa thuận về vũ khí hóa học của Syria
  - Một nảy trên tấm bạt lò xo.
- source_sentence: Chủ tịch Trung Quốc đến Argentina thăm cấp nhà nước
  sentences:
  - Ông Hollande đến thăm cấp nhà nước Mỹ
  - Bão tuyết tấn công vùng Đông Bắc nước Mỹ, năm người chết, 700.000 người mất điện
  - Một con chim lớn đang bay trong không trung.
- source_sentence: Syria triển khai thêm quân bất chấp thỏa thuận hòa bình
  sentences:
  - Một cậu bé đang chơi với một.
  - Cả phiến quân và lực lượng chính phủ đều bị cáo buộc cướp bóc các ngôi làng ở
    vùng nông thôn Liberia bất chấp thỏa thuận hòa bình.
  - Một người đàn ông đang thổi sáo.
- source_sentence: Một người đàn ông đang lắp ráp các bộ phận loa.
  sentences:
  - Một người đàn ông đang đi bộ trên vỉa hè.
  - Không phải là một câu trả lời thực sự cho câu hỏi của bạn, nhưng có lẽ nó sẽ giúp.
  - Một người đàn ông phun nước từ vòi cho một người đàn ông khác.
model-index:
- name: SentenceTransformer based on huudan123/model_stage3_2_score
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts evaluator
      type: sts-evaluator
    metrics:
    - type: pearson_cosine
      value: 0.3815738041383691
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.382323329830821
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.4198450446326336
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.4097830682280972
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.4195429740858371
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.40938489178823334
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.2588766748845407
      name: Pearson Dot
    - type: spearman_dot
      value: 0.26733997459061914
      name: Spearman Dot
    - type: pearson_max
      value: 0.4198450446326336
      name: Pearson Max
    - type: spearman_max
      value: 0.4097830682280972
      name: Spearman Max
---

# SentenceTransformer based on huudan123/model_stage3_2_score

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [huudan123/model_stage3_2_score](https://huggingface.co/huudan123/model_stage3_2_score). 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_stage3_2_score](https://huggingface.co/huudan123/model_stage3_2_score) <!-- at revision 670784ce0e1295612bf239e706cfa9751b66ab24 -->
- **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/model_stage4")
# Run inference
sentences = [
    'Một người đàn ông đang lắp ráp các bộ phận loa.',
    'Một người đàn ông đang đi bộ trên vỉa hè.',
    'Một người đàn ông phun nước từ vòi cho một người đàn ông khác.',
]
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     | 0.3816     |
| spearman_cosine    | 0.3823     |
| pearson_manhattan  | 0.4198     |
| spearman_manhattan | 0.4098     |
| pearson_euclidean  | 0.4195     |
| spearman_euclidean | 0.4094     |
| pearson_dot        | 0.2589     |
| spearman_dot       | 0.2673     |
| pearson_max        | 0.4198     |
| **spearman_max**   | **0.4098** |

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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`: 1e-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`: 1e-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       | -             | -         | 0.8441                     |
| 0.6605  | 500     | 0.023         | -         | -                          |
| **1.0** | **757** | **-**         | **0.013** | **0.7165**                 |
| 1.3210  | 1000    | 0.0058        | -         | -                          |
| 1.9815  | 1500    | 0.0026        | -         | -                          |
| 2.0     | 1514    | -             | 0.0319    | 0.5737                     |
| 2.6420  | 2000    | 0.0016        | -         | -                          |
| 3.0     | 2271    | -             | 0.0662    | 0.5100                     |
| 3.3025  | 2500    | 0.0013        | -         | -                          |
| 3.9630  | 3000    | 0.0011        | -         | -                          |
| 4.0     | 3028    | -             | 0.0962    | 0.4147                     |
| 4.6235  | 3500    | 0.001         | -         | -                          |
| 5.0     | 3785    | -             | 0.0976    | 0.4098                     |

* 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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