---
library_name: transformers
base_model: google-bert/bert-base-chinese
tags:
- generated_from_trainer
model-index:
- name: bert_crf-ner-weibo
  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. -->

# bert_crf-ner-weibo

This model is a fine-tuned version of [google-bert/bert-base-chinese](https://huggingface.co/google-bert/bert-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2287
- eval_precision: 0.6344
- eval_recall: 0.7584
- eval_f1: 0.6909
- eval_accuracy: 0.9678
- eval_runtime: 0.5124
- eval_samples_per_second: 524.958
- eval_steps_per_second: 9.758
- epoch: 115.0
- step: 2530

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use 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: 200
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.46.1
- Pytorch 1.13.1+cu117
- Datasets 3.1.0
- Tokenizers 0.20.2