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EN
Mt5 Chinese Small
M
Mt5 Chinese Small
Developed by yihsuan
An abstract generation model fine-tuned from mT5-small, supporting Chinese text summarization tasks
Downloads 36
Release Time : 4/26/2022
Model Overview
This model is a fine-tuned version of google/mt5-small on Chinese datasets, specifically designed for generating Chinese text summaries.
Model Features
Chinese Abstract Generation
Optimized summarization capability specifically for Chinese text
Based on mT5 Architecture
Utilizes the multilingual T5 model architecture with strong cross-language transfer capabilities
Lightweight Model
Small model size, suitable for deployment in resource-constrained environments
Model Capabilities
Chinese text summarization
Multi-sentence compression
Key information extraction
Use Cases
News Summarization
News Article Summarization
Automatically generates key summaries of news articles
ROUGE-L score 18.6
Scientific Literature
Research Paper Summarization
Generates brief overviews of research papers
đ best_model_test_0423_small
This model is a fine - tuned text summarization model based on google/mt5 - small. It can effectively summarize text and has achieved good results on the evaluation set, providing a reliable solution for text summarization tasks.
đ Quick Start
This model is a fine - tuned version of [google/mt5 - small](https://huggingface.co/google/mt5 - small) on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.6341
- Rouge1: 18.7681
- Rouge2: 6.3762
- Rougel: 18.6081
- Rougelsum: 18.6173
- Gen Len: 22.1086
⨠Features
- Summarization: It can perform text summarization tasks.
- Metrics: Evaluated using ROUGE metrics.
đ Documentation
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9, 0.999) and epsilon = 1e - 08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 5.8165 | 0.05 | 1000 | 3.6541 | 11.6734 | 3.9865 | 11.5734 | 11.5375 | 18.0056 |
| 4.306 | 0.1 | 2000 | 3.4291 | 12.0417 | 3.8419 | 11.9231 | 11.9223 | 16.8948 |
| 4.1091 | 0.16 | 3000 | 3.3643 | 13.661 | 4.5171 | 13.5123 | 13.5076 | 19.4016 |
| 3.9637 | 0.21 | 4000 | 3.2574 | 13.8443 | 4.1761 | 13.689 | 13.6927 | 18.4288 |
| 3.8205 | 0.26 | 5000 | 3.2434 | 13.5371 | 4.3639 | 13.3551 | 13.3552 | 21.5776 |
| 3.7262 | 0.31 | 6000 | 3.1690 | 14.3668 | 4.8048 | 14.2191 | 14.1906 | 21.5548 |
| 3.6887 | 0.36 | 7000 | 3.0657 | 14.3265 | 4.436 | 14.212 | 14.205 | 20.89 |
| 3.6337 | 0.42 | 8000 | 3.0318 | 14.6809 | 4.8345 | 14.5378 | 14.5331 | 20.3651 |
| 3.5443 | 0.47 | 9000 | 3.0554 | 15.3372 | 4.9163 | 15.1794 | 15.1781 | 21.7742 |
| 3.5203 | 0.52 | 10000 | 2.9793 | 14.9278 | 4.9656 | 14.7491 | 14.743 | 20.8113 |
| 3.4936 | 0.57 | 11000 | 3.0079 | 15.7705 | 5.1453 | 15.5582 | 15.5756 | 23.4274 |
| 3.4592 | 0.62 | 12000 | 2.9721 | 15.0201 | 5.1612 | 14.8508 | 14.8198 | 22.7007 |
| 3.377 | 0.67 | 13000 | 3.0112 | 15.9595 | 5.1133 | 15.78 | 15.7774 | 23.4427 |
| 3.4158 | 0.73 | 14000 | 2.9239 | 14.7984 | 5.051 | 14.6943 | 14.6581 | 21.6009 |
| 3.378 | 0.78 | 15000 | 2.8897 | 16.5128 | 5.1923 | 16.3523 | 16.3265 | 22.0828 |
| 3.3231 | 0.83 | 16000 | 2.9347 | 16.9997 | 5.5524 | 16.8534 | 16.8737 | 22.5807 |
| 3.3268 | 0.88 | 17000 | 2.9116 | 16.0261 | 5.4226 | 15.9234 | 15.914 | 23.6988 |
| 3.3127 | 0.93 | 18000 | 2.8610 | 16.6255 | 5.3554 | 16.4729 | 16.4569 | 22.9481 |
| 3.2664 | 0.99 | 19000 | 2.8606 | 17.7703 | 5.9475 | 17.6229 | 17.6259 | 23.4423 |
| 3.1718 | 1.04 | 20000 | 2.8764 | 17.301 | 5.6262 | 17.122 | 17.1104 | 23.0093 |
| 3.0987 | 1.09 | 21000 | 2.8282 | 16.4718 | 5.2077 | 16.3394 | 16.3401 | 20.9697 |
| 3.1486 | 1.14 | 22000 | 2.8235 | 18.5594 | 5.9469 | 18.3882 | 18.3799 | 22.7291 |
| 3.1435 | 1.19 | 23000 | 2.8261 | 18.111 | 6.0309 | 17.9593 | 17.9613 | 22.9612 |
| 3.1049 | 1.25 | 24000 | 2.8068 | 17.124 | 5.5675 | 16.9714 | 16.9876 | 22.5558 |
| 3.1357 | 1.3 | 25000 | 2.8014 | 17.3916 | 5.8671 | 17.2148 | 17.2502 | 23.0075 |
| 3.0904 | 1.35 | 26000 | 2.7790 | 17.419 | 5.6689 | 17.3125 | 17.3058 | 22.1492 |
| 3.0877 | 1.4 | 27000 | 2.7462 | 17.0605 | 5.4735 | 16.9414 | 16.9378 | 21.7522 |
| 3.0694 | 1.45 | 28000 | 2.7563 | 17.752 | 5.8889 | 17.5967 | 17.619 | 23.2005 |
| 3.0498 | 1.51 | 29000 | 2.7521 | 17.9056 | 5.7754 | 17.7624 | 17.7836 | 21.9369 |
| 3.0566 | 1.56 | 30000 | 2.7468 | 18.6531 | 6.0538 | 18.5397 | 18.5038 | 22.2358 |
| 3.0489 | 1.61 | 31000 | 2.7450 | 18.4869 | 5.9297 | 18.3139 | 18.3169 | 22.0108 |
| 3.0247 | 1.66 | 32000 | 2.7449 | 18.5192 | 5.9966 | 18.3721 | 18.3569 | 22.2071 |
| 2.9877 | 1.71 | 33000 | 2.7160 | 18.1655 | 5.9294 | 18.0304 | 18.0836 | 21.4595 |
| 3.0383 | 1.76 | 34000 | 2.7202 | 18.4959 | 6.2413 | 18.3363 | 18.3431 | 22.9732 |
| 3.041 | 1.82 | 35000 | 2.6948 | 17.5306 | 5.8119 | 17.4011 | 17.4149 | 21.9435 |
| 2.9285 | 1.87 | 36000 | 2.6957 | 18.6418 | 6.1394 | 18.514 | 18.4823 | 22.5174 |
| 3.0556 | 1.92 | 37000 | 2.7000 | 18.7387 | 6.0585 | 18.5761 | 18.574 | 22.9315 |
| 3.0033 | 1.97 | 38000 | 2.6974 | 17.9387 | 6.1387 | 17.8271 | 17.8111 | 22.4726 |
| 2.9207 | 2.02 | 39000 | 2.6998 | 18.6073 | 6.1906 | 18.3891 | 18.4103 | 23.0274 |
| 2.8922 | 2.08 | 40000 | 2.6798 | 18.4017 | 6.2244 | 18.2321 | 18.2296 | 22.0697 |
| 2.8938 | 2.13 | 41000 | 2.6666 | 18.8016 | 6.2066 | 18.6411 | 18.6353 | 21.7017 |
| 2.9124 | 2.18 | 42000 | 2.6606 | 18.7544 | 6.3533 | 18.5923 | 18.5739 | 21.4303 |
| 2.8597 | 2.23 | 43000 | 2.6947 | 18.8672 | 6.4526 | 18.7416 | 18.7482 | 22.3352 |
| 2.8435 | 2.28 | 44000 | 2.6738 | 18.9405 | 6.356 | 18.7791 | 18.7729 | 21.9081 |
| 2.8672 | 2.34 | 45000 | 2.6734 | 18.7509 | 6.3991 | 18.6175 | 18.5828 | 21.8869 |
| 2.899 | 2.39 | 46000 | 2.6575 | 18.5529 | 6.3489 | 18.4139 | 18.401 | 21.7694 |
| 2.8616 | 2.44 | 47000 | 2.6485 | 18.7563 | 6.268 | 18.6368 | 18.6253 | 21.5685 |
| 2.8937 | 2.49 | 48000 | 2.6486 | 18.6525 | 6.3426 | 18.5184 | 18.5129 | 22.3337 |
| 2.8446 | 2.54 | 49000 | 2.6572 | 18.6529 | 6.2655 | 18.4915 | 18.4764 | 22.3331 |
| 2.8676 | 2.59 | 50000 | 2.6608 | 19.0913 | 6.494 | 18.929 | 18.9233 | 22.132 |
| 2.8794 | 2.65 | 51000 | 2.6583 | 18.7648 | 6.459 | 18.6276 | 18.6125 | 22.2414 |
| 2.8836 | 2.7 | 52000 | 2.6512 | 18.7243 | 6.3865 | 18.5848 | 18.5763 | 22.2551 |
| 2.8174 | 2.75 | 53000 | 2.6409 | 18.9393 | 6.3914 | 18.7733 | 18.7715 | 22.1243 |
| 2.8494 | 2.8 | 54000 | 2.6396 | 18.6126 | 6.4389 | 18.4673 | 18.4516 | 21.7638 |
| 2.9025 | 2.85 | 55000 | 2.6341 | 18.7681 | 6.3762 | 18.6081 | 18.6173 | 22.1086 |
| 2.8754 | 2.91 | 56000 | 2.6388 | 19.0828 | 6.5203 | 18.9334 | 18.9285 | 22.3497 |
| 2.8489 | 2.96 | 57000 | 2.6375 | 18.9219 | 6.4922 | 18.763 | 18.7437 | 21.9321 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.10.1+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
đ License
The model is licensed under the Apache - 2.0 license.
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