---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: HsscBERT_abs_and_full
  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. -->

# HsscBERT_abs_and_full

This model is a fine-tuned version of [/home/hscrc/pretrained_models/bert-base-chinese](https://huggingface.co//home/hscrc/pretrained_models/bert-base-chinese) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6037
- Accuracy: 0.8504

## 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: 32
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 9
- total_train_batch_size: 288
- total_eval_batch_size: 144
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0

### Training results

| Training Loss | Epoch | Step  | Accuracy | Validation Loss |
|:-------------:|:-----:|:-----:|:--------:|:---------------:|
| 0.8163        | 0.19  | 5000  | 0.8326   | 0.6971          |
| 0.7942        | 0.38  | 10000 | 0.8364   | 0.6761          |
| 0.7817        | 0.57  | 15000 | 0.8384   | 0.6651          |
| 0.7751        | 0.75  | 20000 | 0.8402   | 0.6563          |
| 0.7654        | 0.94  | 25000 | 0.8415   | 0.6490          |
| 0.7546        | 1.13  | 30000 | 0.8427   | 0.6441          |
| 0.7527        | 1.32  | 35000 | 0.8434   | 0.6398          |
| 0.7484        | 1.51  | 40000 | 0.8444   | 0.6345          |
| 0.7443        | 1.7   | 45000 | 0.8450   | 0.6318          |
| 0.74          | 1.88  | 50000 | 0.8456   | 0.6292          |
| 0.738         | 2.07  | 55000 | 0.8460   | 0.6268          |
| 0.734         | 2.26  | 60000 | 0.8464   | 0.6246          |
| 0.7335        | 2.45  | 65000 | 0.8467   | 0.6229          |
| 0.7299        | 2.64  | 70000 | 0.8470   | 0.6212          |
| 0.7291        | 2.83  | 75000 | 0.8473   | 0.6201          |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.10.0+cu113
- Datasets 2.9.0
- Tokenizers 0.13.2