File size: 15,449 Bytes
7ac9b41 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 |
---
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]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## 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** |
<!--
## 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.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## 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",
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |