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# Danmaku-MLM

This model is a fine-tuned version of [uer/chinese_roberta_L-8_H-512](https://huggingface.co/uer/chinese_roberta_L-8_H-512) on an [Danmaku Corpus](https://github.com/Chen-X666/bullet-screenCorpus)(2000k raw data) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1645
- Accuracy: 0.7780

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

Traning Data: https://github.com/Chen-X666/bullet-screenCorpus

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 1.13.1
- Datasets 2.6.1
- Tokenizers 0.11.0
{"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "pipeline_tag": "fill-mask", "model-index": [{"name": "danmaku-mlm", "results": []}]}
transformers pytorch safetensors bert fill-mask generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 




# Danmaku-MLM

This model is a fine-tuned version of uer/chinese_roberta_L-8_H-512 on an Danmaku Corpus(2000k raw data) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1645
- Accuracy: 0.7780

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

Traning Data: URL

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 1.13.1
- Datasets 2.6.1
- Tokenizers 0.11.0
# Danmaku-MLM

This model is a fine-tuned version of uer/chinese_roberta_L-8_H-512 on an Danmaku Corpus(2000k raw data) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1645
- Accuracy: 0.7780 ## Model description

More information needed ## Intended uses & limitations

More information needed ## Training and evaluation data

Traning Data: URL ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP ### Training results ### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 1.13.1
- Datasets 2.6.1
- Tokenizers 0.11.0
TAGS
#transformers #pytorch #safetensors #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 # Danmaku-MLM

This model is a fine-tuned version of uer/chinese_roberta_L-8_H-512 on an Danmaku Corpus(2000k raw data) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1645
- Accuracy: 0.7780 ## Model description

More information needed ## Intended uses & limitations

More information needed ## Training and evaluation data

Traning Data: URL ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP ### Training results ### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 1.13.1
- Datasets 2.6.1
- Tokenizers 0.11.0
TAGS
#transformers #pytorch #safetensors #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
# Danmaku-MLM

This model is a fine-tuned version of uer/chinese_roberta_L-8_H-512 on an Danmaku Corpus(2000k raw data) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1645
- Accuracy: 0.7780## Model description

More information needed## Intended uses & limitations

More information needed## Training and evaluation data

Traning Data: URL## Training procedure### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP### Training results### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 1.13.1
- Datasets 2.6.1
- Tokenizers 0.11.0