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---
library_name: transformers
license: apache-2.0
base_model: hfl/chinese-roberta-wwm-ext-large
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: robert_bilstm_mega_res-ner-msra-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# robert_bilstm_mega_res-ner-msra-ner
This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext-large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0668
- Precision: 0.9473
- Recall: 0.9473
- F1: 0.9473
- Accuracy: 0.9928
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0421 | 1.0 | 1449 | 0.0274 | 0.9225 | 0.9341 | 0.9282 | 0.9923 |
| 0.0077 | 2.0 | 2898 | 0.0355 | 0.9255 | 0.9389 | 0.9321 | 0.9910 |
| 0.0066 | 3.0 | 4347 | 0.0376 | 0.9397 | 0.9384 | 0.9391 | 0.9921 |
| 0.0031 | 4.0 | 5796 | 0.0421 | 0.9385 | 0.9401 | 0.9393 | 0.9926 |
| 0.0071 | 5.0 | 7245 | 0.0446 | 0.9365 | 0.9446 | 0.9406 | 0.9923 |
| 0.0007 | 6.0 | 8694 | 0.0431 | 0.9457 | 0.9398 | 0.9428 | 0.9930 |
| 0.0003 | 7.0 | 10143 | 0.0494 | 0.9412 | 0.9408 | 0.9410 | 0.9926 |
| 0.0013 | 8.0 | 11592 | 0.0584 | 0.9379 | 0.9338 | 0.9358 | 0.9917 |
| 0.0003 | 9.0 | 13041 | 0.0557 | 0.9373 | 0.9422 | 0.9398 | 0.9923 |
| 0.0011 | 10.0 | 14490 | 0.0525 | 0.9395 | 0.9463 | 0.9429 | 0.9926 |
| 0.0 | 11.0 | 15939 | 0.0569 | 0.9379 | 0.9449 | 0.9414 | 0.9924 |
| 0.0001 | 12.0 | 17388 | 0.0586 | 0.9358 | 0.9434 | 0.9396 | 0.9922 |
| 0.0 | 13.0 | 18837 | 0.0601 | 0.9439 | 0.9437 | 0.9438 | 0.9926 |
| 0.0013 | 14.0 | 20286 | 0.0606 | 0.9395 | 0.9454 | 0.9424 | 0.9924 |
| 0.0 | 15.0 | 21735 | 0.0591 | 0.9451 | 0.9495 | 0.9473 | 0.9926 |
| 0.0 | 16.0 | 23184 | 0.0608 | 0.9399 | 0.9490 | 0.9444 | 0.9926 |
| 0.0 | 17.0 | 24633 | 0.0620 | 0.9440 | 0.9454 | 0.9447 | 0.9927 |
| 0.0 | 18.0 | 26082 | 0.0636 | 0.9493 | 0.9454 | 0.9473 | 0.9926 |
| 0.0 | 19.0 | 27531 | 0.0681 | 0.9460 | 0.9451 | 0.9456 | 0.9926 |
| 0.0 | 20.0 | 28980 | 0.0630 | 0.9430 | 0.9430 | 0.9430 | 0.9925 |
| 0.0 | 21.0 | 30429 | 0.0620 | 0.9445 | 0.9463 | 0.9454 | 0.9928 |
| 0.0 | 22.0 | 31878 | 0.0671 | 0.9456 | 0.9446 | 0.9451 | 0.9926 |
| 0.0 | 23.0 | 33327 | 0.0682 | 0.9479 | 0.9451 | 0.9465 | 0.9926 |
| 0.0 | 24.0 | 34776 | 0.0671 | 0.9475 | 0.9466 | 0.9470 | 0.9927 |
| 0.0 | 25.0 | 36225 | 0.0668 | 0.9473 | 0.9473 | 0.9473 | 0.9928 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.4.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
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