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---
base_model: huudan123/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:254546
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: em_gái grany người da trắng cô ấy muốn đi học
sentences:
- bà thường kể câu_chuyện về chị_gái bà người chồng quyết_định chuyển đến thành_phố
augusta chuyển sang màu trắng
- thêm thời_gian thông_thường thêm phát_triển kế_hoạch hành_động
- em_gái grany người da trắng
- source_sentence: hãy họ biết họ cố_gắng cản_trở_việc chèo thuyền chúng_tôi chúng_tôi
treo đầu_tiên doxy chiến_đấu nó
sentences:
- tôi biết mình hướng tới mục_đích báo_cáo một địa_chỉ ở washington
- chúng_ta cố_gắng chiến_đấu nó một_khi chúng_ta bắt_đầu_ra khơi
- cách nào biết liệu con thuyền đi thẳng
- source_sentence: louisa may alcot nathaniel hawthorne sống phố pinckney trong phố
beacon oliver wendel holmes gọi con đường đầy nắng nhà sử_học wiliam prescot
sentences:
- hawthorne sống phố pinckney trong 7 năm
- hawthorne sống phố pinckney
- dùng tất_cả hiệu_quả trong phòng_chống thói_quen xấu nó hiệu_quả trong điều_trị
nói_chung
- source_sentence: hình 6 hiển_thị chi_phí đơn_vị trung_bình tạo hàm chi_phí usps
sentences:
- chi_phí trung_bình usps thể_hiện trong hình 6
- chi_phí trung_bình usps thể_hiện trong hình 6 thấy tất_cả lợi_nhuận
- cấp đại_úy blod một khoản hoa_hồng một sai_lầm sai_lầm đấy tôi
- source_sentence: bạn tiếp_tục nhập thông_tin cơ_sở dữ_liệu
sentences:
- mặc_dù dứa hương_vị tuyệt_vời chi_phí vận_chuyển quá cao đưa chúng thị_trường
- bạn tiếp_tục bạn nhập mọi thứ
- bạn mọi thứ bạn bắt_đầu_từ
model-index:
- name: SentenceTransformer based on huudan123/stage1
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev
type: sts-dev
metrics:
- type: pearson_cosine
value: 0.7132925999347621
name: Pearson Cosine
- type: spearman_cosine
value: 0.7139908860784119
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6924068767142901
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6987187512790664
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6927853521211202
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6988256048265301
name: Spearman Euclidean
- type: pearson_dot
value: 0.6562289766339777
name: Pearson Dot
- type: spearman_dot
value: 0.6552808237632588
name: Spearman Dot
- type: pearson_max
value: 0.7132925999347621
name: Pearson Max
- type: spearman_max
value: 0.7139908860784119
name: Spearman Max
---
# SentenceTransformer based on huudan123/stage1
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [huudan123/stage1](https://huggingface.co/huudan123/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/stage1](https://huggingface.co/huudan123/stage1) <!-- at revision 2af9d99bbe23c419d648a4eef0dd24d5b788921d -->
- **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/stage2")
# Run inference
sentences = [
'bạn tiếp_tục nhập thông_tin cơ_sở dữ_liệu',
'bạn mọi thứ bạn bắt_đầu_từ',
'bạn tiếp_tục bạn nhập mọi thứ',
]
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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</details>
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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### Out-of-Scope Use
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## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:----------|
| pearson_cosine | 0.7133 |
| **spearman_cosine** | **0.714** |
| pearson_manhattan | 0.6924 |
| spearman_manhattan | 0.6987 |
| pearson_euclidean | 0.6928 |
| spearman_euclidean | 0.6988 |
| pearson_dot | 0.6562 |
| spearman_dot | 0.6553 |
| pearson_max | 0.7133 |
| spearman_max | 0.714 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 254,546 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 14.78 tokens</li><li>max: 110 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 14.78 tokens</li><li>max: 110 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 10.19 tokens</li><li>max: 29 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------|:-------------------------------------------------------|
| <code>conceptualy kem skiming hai kích_thước cơ_bản sản_phẩm địa_lý</code> | <code>sản_phẩm địa_lý làm kem skiming làm_việc</code> | <code>kem skiming hai tập_trung sản_phẩm địa_lý</code> |
| <code>sản_phẩm địa_lý làm kem skiming làm_việc</code> | <code>conceptualy kem skiming hai kích_thước cơ_bản sản_phẩm địa_lý</code> | <code>kem skiming hai tập_trung sản_phẩm địa_lý</code> |
| <code>bạn biết trong mùa giải tôi đoán ở mức_độ bạn bạn mất chúng đến mức_độ tiếp_theo họ quyết_định nhớ đội_ngũ cha_mẹ chiến_binh quyết_định gọi nhớ một người ba a một người đàn_ông đi đến thay_thế anh ta một người đàn_ông nào đi thay_thế anh ta</code> | <code>recals thực_hiện thứ sáu</code> | <code>anh mất mọi thứ ở mức_độ người dân nhớ</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 1,660 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 13.54 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 13.54 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 8.78 tokens</li><li>max: 22 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:-------------------------------------------------------------------------------|:---------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| <code>anh ấy nói mẹ con về nhà</code> | <code>xuống xe_buýt trường anh ấy gọi mẹ</code> | <code>anh nói mẹ anh về nhà</code> |
| <code>xuống xe_buýt trường anh ấy gọi mẹ</code> | <code>anh ấy nói mẹ con về nhà</code> | <code>anh nói mẹ anh về nhà</code> |
| <code>tôi biết mình hướng tới mục_đích báo_cáo một địa_chỉ ở washington</code> | <code>tôi bao_giờ đến washington tôi chỉ_định ở tôi lạc cố_gắng tìm</code> | <code>tôi hoàn_toàn chắc_chắn tôi làm tôi đi đến washington tôi giao báo_cáo</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### 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`: 20
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.05
- `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`: 20
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.05
- `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-dev_spearman_cosine |
|:-------:|:-------:|:-------------:|:----------:|:-----------------------:|
| 0 | 0 | - | - | 0.5307 |
| 0.0503 | 50 | 9.1742 | - | - |
| 0.1005 | 100 | 5.9716 | - | - |
| 0.1508 | 150 | 4.6737 | - | - |
| 0.2010 | 200 | 3.2819 | - | - |
| 0.2513 | 250 | 2.8832 | - | - |
| 0.3015 | 300 | 2.7327 | - | - |
| 0.3518 | 350 | 2.6305 | - | - |
| 0.4020 | 400 | 2.6239 | - | - |
| 0.4523 | 450 | 2.5527 | - | - |
| 0.5025 | 500 | 2.5271 | - | - |
| 0.5528 | 550 | 2.4904 | - | - |
| 0.6030 | 600 | 2.4987 | - | - |
| 0.6533 | 650 | 2.4009 | - | - |
| 0.7035 | 700 | 2.3944 | - | - |
| 0.7538 | 750 | 2.5054 | - | - |
| 0.8040 | 800 | 2.3989 | - | - |
| 0.8543 | 850 | 2.4019 | - | - |
| 0.9045 | 900 | 2.3638 | - | - |
| 0.9548 | 950 | 2.3478 | - | - |
| **1.0** | **995** | **-** | **3.0169** | **0.7322** |
| 1.0050 | 1000 | 2.4424 | - | - |
| 1.0553 | 1050 | 2.2478 | - | - |
| 1.1055 | 1100 | 2.2448 | - | - |
| 1.1558 | 1150 | 2.205 | - | - |
| 1.2060 | 1200 | 2.1811 | - | - |
| 1.2563 | 1250 | 2.1794 | - | - |
| 1.3065 | 1300 | 2.1495 | - | - |
| 1.3568 | 1350 | 2.1548 | - | - |
| 1.4070 | 1400 | 2.1299 | - | - |
| 1.4573 | 1450 | 2.1335 | - | - |
| 1.5075 | 1500 | 2.1388 | - | - |
| 1.5578 | 1550 | 2.0999 | - | - |
| 1.6080 | 1600 | 2.0859 | - | - |
| 1.6583 | 1650 | 2.0959 | - | - |
| 1.7085 | 1700 | 2.0334 | - | - |
| 1.7588 | 1750 | 2.0647 | - | - |
| 1.8090 | 1800 | 2.0261 | - | - |
| 1.8593 | 1850 | 2.0133 | - | - |
| 1.9095 | 1900 | 2.0517 | - | - |
| 1.9598 | 1950 | 2.0152 | - | - |
| 2.0 | 1990 | - | 3.1210 | 0.7187 |
| 2.0101 | 2000 | 1.924 | - | - |
| 2.0603 | 2050 | 1.7472 | - | - |
| 2.1106 | 2100 | 1.7485 | - | - |
| 2.1608 | 2150 | 1.7536 | - | - |
| 2.2111 | 2200 | 1.751 | - | - |
| 2.2613 | 2250 | 1.7172 | - | - |
| 2.3116 | 2300 | 1.7269 | - | - |
| 2.3618 | 2350 | 1.7352 | - | - |
| 2.4121 | 2400 | 1.7019 | - | - |
| 2.4623 | 2450 | 1.7278 | - | - |
| 2.5126 | 2500 | 1.7046 | - | - |
| 2.5628 | 2550 | 1.6962 | - | - |
| 2.6131 | 2600 | 1.6881 | - | - |
| 2.6633 | 2650 | 1.6806 | - | - |
| 2.7136 | 2700 | 1.6614 | - | - |
| 2.7638 | 2750 | 1.6918 | - | - |
| 2.8141 | 2800 | 1.6794 | - | - |
| 2.8643 | 2850 | 1.6708 | - | - |
| 2.9146 | 2900 | 1.6531 | - | - |
| 2.9648 | 2950 | 1.6236 | - | - |
| 3.0 | 2985 | - | 3.2556 | 0.7140 |
* 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: 0.32.1
- 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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