---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Ernie-3.0-base-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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# Ernie-3.0-base-chinese-finetuned-ner

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4856
- Precision: 0.6511
- Recall: 0.7535
- F1: 0.6986
- Accuracy: 0.9053

## 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: 2e-05
- train_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0667        | 1.0   | 126  | 0.4589          | 0.6387    | 0.7553 | 0.6921 | 0.9012   |
| 0.0594        | 2.0   | 252  | 0.4656          | 0.6444    | 0.7515 | 0.6939 | 0.9057   |
| 0.053         | 3.0   | 378  | 0.4524          | 0.6444    | 0.7477 | 0.6922 | 0.9064   |
| 0.0473        | 4.0   | 504  | 0.4955          | 0.6298    | 0.7568 | 0.6875 | 0.9012   |
| 0.0461        | 5.0   | 630  | 0.4892          | 0.6512    | 0.7505 | 0.6973 | 0.9077   |
| 0.0438        | 6.0   | 756  | 0.5021          | 0.6450    | 0.7528 | 0.6947 | 0.9054   |
| 0.0428        | 7.0   | 882  | 0.5048          | 0.6471    | 0.7576 | 0.6980 | 0.9050   |
| 0.0583        | 8.0   | 1008 | 0.4990          | 0.6401    | 0.7533 | 0.6921 | 0.9038   |
| 0.0582        | 9.0   | 1134 | 0.4833          | 0.6457    | 0.7513 | 0.6945 | 0.9064   |
| 0.0635        | 10.0  | 1260 | 0.4856          | 0.6511    | 0.7535 | 0.6986 | 0.9053   |


### Framework versions

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