pytorch bert fill-mask transformers generated_from_trainer license:apache-2.0 autotrain_compatible
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- null
model_index:
- name: bert-base-chinese-complaint-128
  results:
  - task:
      name: Masked Language Modeling
      type: fill-mask
---

<!-- 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-base-chinese-complaint-128

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3004

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.3735        | 1.0   | 1250  | 2.4628          |
| 2.2412        | 2.0   | 2500  | 2.0378          |
| 1.9251        | 3.0   | 3750  | 1.8368          |
| 1.7407        | 4.0   | 5000  | 1.6972          |
| 1.6137        | 5.0   | 6250  | 1.5937          |
| 1.5365        | 6.0   | 7500  | 1.5315          |
| 1.4662        | 7.0   | 8750  | 1.4921          |
| 1.3985        | 8.0   | 10000 | 1.4517          |
| 1.3509        | 9.0   | 11250 | 1.4308          |
| 1.3047        | 10.0  | 12500 | 1.3906          |
| 1.2745        | 11.0  | 13750 | 1.3467          |
| 1.2377        | 12.0  | 15000 | 1.3306          |
| 1.2139        | 13.0  | 16250 | 1.3205          |
| 1.2027        | 14.0  | 17500 | 1.3098          |
| 1.1722        | 15.0  | 18750 | 1.2845          |
| 1.1697        | 16.0  | 20000 | 1.3004          |


### Framework versions

- Transformers 4.8.2
- Pytorch 1.7.1
- Datasets 1.16.1
- Tokenizers 0.10.3
