safetensors generated_from_trainer base_model:gyr66/RoBERTa-ext-large-chinese-finetuned-ner region:us
---
base_model: gyr66/RoBERTa-ext-large-chinese-finetuned-ner
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: RoBERTa-ext-large-lora-updated-chinese-finetuned-ner
  results: []
---

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# RoBERTa-ext-large-lora-updated-chinese-finetuned-ner

This model is a fine-tuned version of [gyr66/RoBERTa-ext-large-chinese-finetuned-ner](https://huggingface.co/gyr66/RoBERTa-ext-large-chinese-finetuned-ner) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9586
- Precision: 0.7016
- Recall: 0.7518
- F1: 0.7258
- Accuracy: 0.9154

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0034        | 1.0   | 252  | 1.0787          | 0.6753    | 0.7523 | 0.7117 | 0.9121   |
| 0.0032        | 2.0   | 504  | 1.0376          | 0.6830    | 0.7490 | 0.7145 | 0.9141   |
| 0.0018        | 3.0   | 756  | 1.0547          | 0.6731    | 0.7573 | 0.7127 | 0.9126   |
| 0.0032        | 4.0   | 1008 | 1.0262          | 0.6829    | 0.7384 | 0.7096 | 0.9126   |
| 0.0027        | 5.0   | 1260 | 0.9613          | 0.6898    | 0.7445 | 0.7161 | 0.9118   |
| 0.0027        | 6.0   | 1512 | 0.9481          | 0.6780    | 0.7550 | 0.7145 | 0.9120   |
| 0.0019        | 7.0   | 1764 | 0.9328          | 0.6917    | 0.7513 | 0.7203 | 0.9150   |
| 0.0008        | 8.0   | 2016 | 0.9570          | 0.6976    | 0.7520 | 0.7238 | 0.9143   |
| 0.0005        | 9.0   | 2268 | 0.9586          | 0.7016    | 0.7518 | 0.7258 | 0.9154   |
| 0.0003        | 10.0  | 2520 | 0.9565          | 0.6945    | 0.7520 | 0.7221 | 0.9151   |


### Framework versions

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