README.md

---
library_name: transformers
license: apache-2.0
base_model: hfl/chinese-roberta-wwm-ext
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: music_ent_classification
  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. -->

# music_ent_classification

This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1230
- Accuracy: 0.9662

## 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: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 128
- total_eval_batch_size: 32
- 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: 5.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4408        | 1.0   | 54   | 0.1763          | 0.9459   |
| 0.1407        | 2.0   | 108  | 0.1221          | 0.9628   |
| 0.0762        | 3.0   | 162  | 0.1123          | 0.9640   |
| 0.0563        | 4.0   | 216  | 0.1226          | 0.9718   |
| 0.0423        | 5.0   | 270  | 0.1230          | 0.9662   |


### Framework versions

- Transformers 4.57.5
- Pytorch 2.6.0+cu124
- Datasets 2.19.0
- Tokenizers 0.22.2


### 训练
```
export WANDB_MODE=disabled # 禁用交互式登录
export CUDA_VISIBLE_DEVICES=0,1,2,3 # 确保识别 4 张 V100
# 变量定义
model="hfl/chinese-roberta-wwm-ext"
transformers_root="transformers"

output_dir="./models/music_ent_classification"
mkdir ${output_dir} -p

# 使用 torchrun 启动
torchrun --nproc_per_node=4 \
    ${transformers_root}/examples/pytorch/text-classification/run_classification.py \
    --model_name_or_path ${model} \
    --train_file "./data/*.train.json" \
    --validation_file "./data/*.test.json" \
    --trust_remote_code True \
    --do_train \
    --do_eval \
    --shuffle_train_dataset \
    --metric_name accuracy \
    --text_column_name sentence1 \
    --label_column_name label \
    --max_seq_length 256 \
    --per_device_train_batch_size 32 \
    --learning_rate 2e-5 \
    --num_train_epochs 5 \
    --logging_steps 50 \
    --save_strategy epoch \
    --eval_strategy epoch \
    --fp16 True \
    --output_dir ${output_dir} \
    --overwrite_output_dir 
```

### 推理

```
# 启动分布式推理
torchrun --nproc_per_node=$(echo $CUDA_DEVICES | tr ',' '\n' | wc -l) \
    transformers/examples/pytorch/text-classification/run_classification.py \
    --model_name_or_path "${MODEL_PATH}" \
    --train_file "${TRAIN_DATA}" \
    --validation_file "${TRAIN_DATA}" \
    --test_file "${INPUT_FILE}" \
    --text_column_name "sentence1" \
    --label_column_name "label" \
    --do_predict \
    --max_seq_length 128 \
    --per_device_eval_batch_size 256 \
    --output_dir "${OUTPUT_DIR}" \
    --fp16 True \
    --trust_remote_code True \
    --overwrite_output_dir

if [ $? -eq 0 ]; then
    echo "✅ [Infer] 推理完成。"
else
    echo "❌ [Infer] 推理失败。"
    exit 1
fi
```