safetensors generated_from_trainer base_model:hfl/chinese-roberta-wwm-ext-large license:apache-2.0 region:us
---
license: apache-2.0
base_model: hfl/chinese-roberta-wwm-ext-large
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: RoBERTa-ext-large-lora-chinese-finetuned-ner
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# RoBERTa-ext-large-lora-chinese-finetuned-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: 0.3762
- Precision: 0.6284
- Recall: 0.7311
- F1: 0.6759
- Accuracy: 0.9107

## 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: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5112        | 1.0   | 252  | 0.3440          | 0.5005    | 0.6105 | 0.5501 | 0.8940   |
| 0.283         | 2.0   | 504  | 0.3198          | 0.5363    | 0.6715 | 0.5963 | 0.9017   |
| 0.2373        | 3.0   | 756  | 0.3104          | 0.5506    | 0.7216 | 0.6246 | 0.9054   |
| 0.1995        | 4.0   | 1008 | 0.3210          | 0.5804    | 0.7236 | 0.6441 | 0.9092   |
| 0.1678        | 5.0   | 1260 | 0.3300          | 0.5828    | 0.7140 | 0.6418 | 0.9077   |
| 0.1435        | 6.0   | 1512 | 0.3274          | 0.5912    | 0.7173 | 0.6482 | 0.9104   |
| 0.1206        | 7.0   | 1764 | 0.3566          | 0.5964    | 0.7351 | 0.6585 | 0.9079   |
| 0.105         | 8.0   | 2016 | 0.3579          | 0.6065    | 0.7281 | 0.6618 | 0.9112   |
| 0.0925        | 9.0   | 2268 | 0.3645          | 0.6148    | 0.7382 | 0.6709 | 0.9103   |
| 0.0835        | 10.0  | 2520 | 0.3762          | 0.6284    | 0.7311 | 0.6759 | 0.9107   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
