README.md

---
base_model: huudan123/model_stage1
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:183796
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: nếu thời_gian đến mà họ phải có một cuộc đấu_tranh johny shanon
    có_thể là một người ngạc_nhiên
  sentences:
  - johny nghĩ anh ta là người giỏi nhất trong thị_trấn
  - nếu một cuộc đấu_tranh đã xảy ra johny có_thể ngạc_nhiên đấy
  - tất_cả bằng_chứng về văn_hóa từ xã_hội của umbria đã bị mất
- source_sentence: chèn jay leno đùa ở đây
  sentences:
  - mathews đã chỉ ra rằng sẽ không cần phải tuyển_dụng luật_sư địa_phương
  - đây là nơi mà một trò_đùa jay leno sẽ đi
  - jay leno không phải là một diễn_viên hài
- source_sentence: đúng_vậy tất_cả là lỗi của họ
  sentences:
  - bạn bị giới_hạn bởi số_lượng bộ_nhớ bạn đã có
  - phải tất_cả đều là lỗi của họ
  - rõ_ràng là tất_cả những lỗi của công_nhân
- source_sentence: 6 mặc_dù mỗi cơ_quan phát_triển và triển_khai các thỏa_thuận hiệu_quả
    phản_ánh các ưu_tiên tổ_chức cụ_thể cấu_trúc và nền văn_hóa các thỏa_thuận hiệu_quả
    đã gặp các đặc_điểm sau
  sentences:
  - các thỏa_thuận hiệu_quả đã được phát_hành từ mỗi đại_lý
  - kế_hoạch hiệu_quả loại_trừ bất_cứ điều gì để làm với các cấu_trúc
  - không có gì bên trong sảnh trên đồi cả
- source_sentence: hay na uy hay gì đó
  sentences:
  - na uy hay cái gì đó khác
  - điều đó hoàn_toàn không đúng
  - na uy hoặc từ một trong những quốc_gia scandinavia
model-index:
- name: SentenceTransformer based on huudan123/model_stage1
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts evaluator
      type: sts-evaluator
    metrics:
    - type: pearson_cosine
      value: 0.6279986884327646
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.6257861952118347
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.6286844662908612
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6309663003206769
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.6277475064516767
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.6297451268540156
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.588316765453479
      name: Pearson Dot
    - type: spearman_dot
      value: 0.5802157556789215
      name: Spearman Dot
    - type: pearson_max
      value: 0.6286844662908612
      name: Pearson Max
    - type: spearman_max
      value: 0.6309663003206769
      name: Spearman Max
---

# SentenceTransformer based on huudan123/model_stage1

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [huudan123/model_stage1](https://huggingface.co/huudan123/model_stage1). 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_stage1](https://huggingface.co/huudan123/model_stage1) <!-- at revision b7466e583ac080b4f544522adb1647a976398ea1 -->
- **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_stage2")
# Run inference
sentences = [
    'hay na uy hay gì đó',
    'na uy hoặc từ một trong những quốc_gia scandinavia',
    'na uy hay cái gì đó 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)

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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     | 0.628     |
| spearman_cosine    | 0.6258    |
| pearson_manhattan  | 0.6287    |
| spearman_manhattan | 0.631     |
| pearson_euclidean  | 0.6277    |
| spearman_euclidean | 0.6297    |
| pearson_dot        | 0.5883    |
| spearman_dot       | 0.5802    |
| pearson_max        | 0.6287    |
| **spearman_max**   | **0.631** |

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## Training Details

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

- `overwrite_output_dir`: True
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 256
- `per_device_eval_batch_size`: 256
- `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`: 256
- `per_device_eval_batch_size`: 256
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_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`: 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        | -             | -          | 0.6283                     |
| 0.6964  | 500      | 4.3237        | -          | -                          |
| 1.0     | 718      | -             | 2.3703     | 0.6500                     |
| 1.3928  | 1000     | 2.2259        | -          | -                          |
| **2.0** | **1436** | **-**         | **2.2597** | **0.624**                  |
| 2.0891  | 1500     | 2.0143        | -          | -                          |
| 2.7855  | 2000     | 1.7433        | -          | -                          |
| 3.0     | 2154     | -             | 2.3027     | 0.6405                     |
| 3.4819  | 2500     | 1.5279        | -          | -                          |
| 4.0     | 2872     | -             | 2.3583     | 0.6094                     |
| 4.1783  | 3000     | 1.3796        | -          | -                          |
| 4.8747  | 3500     | 1.2096        | -          | -                          |
| 5.0     | 3590     | -             | 2.4877     | 0.6069                     |
| 5.5710  | 4000     | 1.036         | -          | -                          |
| 6.0     | 4308     | -             | 2.5685     | 0.6310                     |

* 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",
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

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