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:5749
- loss:CosineSimilarityLoss
widget:
- source_sentence: trắng và nâu đang chạy nhanh qua đám cỏ.
  sentences:
  - Một chiếc máy bay trên bầu trời.
  - trắng lớn đang chạy trên cỏ.
  - Hai con đại bàng đang đậu trên cành cây.
- source_sentence: Chúng tôi đang di chuyển \"... liên quan đến khung nghỉ vũ trụ
    comoving ... với tốc độ khoảng 371 km/s về phía chòm sao Sư Tử\".
  sentences:
  - Một bức ảnh đen trắng của một người đàn ông đứng cạnh xe buýt.
  - Một vận động viên quần vợt ở giữa trận đấu.
  - Không có 'tĩnh' không liên quan đến một số đối tượng khác.
- source_sentence: Một người đàn ông đang trượt băng xuống cầu thang.
  sentences:
  - Tôi đồng ý với những người khác rằng theo dõi thời gian của bạn là cơ bản cho
    giải pháp.
  - Người đàn ông đang trượt tuyết xuống một ngọn đồi tuyết.
  - Một đứa bé đang cười.
- source_sentence: Theo trang web này, cường độ khả kiến cực đại sẽ vào khoảng 10,5
    vào khoảng ngày 2/2.
  sentences:
  - Trẻ em nhìn một con cừu.
  - Dữ liệu AAVSO dường như chỉ ra rằng nó có thể đã đạt đỉnh, vào khoảng 10,5 (trực
    quan).
  - Chim đen đứng trên bê tông.
- source_sentence: Tôi có thể nghĩ ra ba yếu tố chính là những phỏng đoán khá logic.
  sentences:
  - Những ở một mình trong rừng.
  - Cô gái đang đứng trước cánh cửa mở của xe buýt.
  - Đã có khá nhiều nghiên cứu trong bóng đá / bóng đá thảo luận về lợi thế sân nhà.
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: 0.8444675896278073
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8433102414270872
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8322074189093971
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8372438919154898
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8330146892118017
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.838262655985479
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8324128204608153
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8309364918730088
      name: Spearman Dot
    - type: pearson_max
      value: 0.8444675896278073
      name: Pearson Max
    - type: spearman_max
      value: 0.8433102414270872
      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/model_stage3")
# Run inference
sentences = [
    'Tôi có thể nghĩ ra ba yếu tố chính là những phỏng đoán khá logic.',
    'Đã có khá nhiều nghiên cứu trong bóng đá / bóng đá thảo luận về lợi thế sân nhà.',
    'Cô gái đang đứng trước cánh cửa mở của xe buýt.',
]
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     | 0.8445     |
| spearman_cosine    | 0.8433     |
| pearson_manhattan  | 0.8322     |
| spearman_manhattan | 0.8372     |
| pearson_euclidean  | 0.833      |
| spearman_euclidean | 0.8383     |
| pearson_dot        | 0.8324     |
| spearman_dot       | 0.8309     |
| pearson_max        | 0.8445     |
| **spearman_max**   | **0.8433** |

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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`: 15
- `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`: 15
- `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                     |
| 1.0      | 45      | -             | 0.0395     | 0.7906                     |
| 2.0      | 90      | -             | 0.0315     | 0.8277                     |
| 3.0      | 135     | -             | 0.0297     | 0.8385                     |
| 4.0      | 180     | -             | 0.0296     | 0.8392                     |
| 5.0      | 225     | -             | 0.0286     | 0.8426                     |
| 6.0      | 270     | -             | 0.0295     | 0.8412                     |
| 7.0      | 315     | -             | 0.0290     | 0.8418                     |
| 8.0      | 360     | -             | 0.0289     | 0.8426                     |
| 9.0      | 405     | -             | 0.0286     | 0.8437                     |
| 10.0     | 450     | -             | 0.0288     | 0.8433                     |
| 11.0     | 495     | -             | 0.0288     | 0.8429                     |
| 11.1111  | 500     | 0.0204        | -          | -                          |
| 12.0     | 540     | -             | 0.0289     | 0.8433                     |
| **13.0** | **585** | **-**         | **0.0286** | **0.8439**                 |
| 14.0     | 630     | -             | 0.0286     | 0.8433                     |
| 15.0     | 675     | -             | 0.0287     | 0.8433                     |

* 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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