---
library_name: transformers
base_model: google-bert/bert-base-chinese
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: for_multiple_choice
  results: []
license: apache-2.0
datasets:
- roberthsu2003/for_Multiple_Choice
language:
- zh
---

<!-- 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. -->

# for_multiple_choice

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:
- Loss: 1.3109
- Accuracy: 0.5962

## 模型的使用

from transformers import AutoTokenizer, AutoModelForMultipleChoice
from typing import Any
import torch

tokenizer = AutoTokenizer.from_pretrained('roberthsu2003/for_multiple_choice')
model = AutoModelForMultipleChoice.from_pretrained('roberthsu2003/for_multiple_choice')

from typing import Any
import torch

class MultipleChoicePipeline:
    def __init__(self, model, tokenizer) -> None:
        self.model = model
        self.tokenizer = tokenizer
        self.device = model.device

    def preprocess(self, context, question, choices):
        cs, qcs = [], []
        for choice in choices:
            cs.append(context)
            qcs.append(question + " " + choice)
        return tokenizer(cs, qcs, truncation="only_first", max_length=256, return_tensors="pt")

    def predict(self, inputs):
        inputs = {k: v.unsqueeze(0).to(self.device) for k, v in inputs.items()}
        return self.model(**inputs).logits

    def postprocess(self, logits, choices):
        predition = torch.argmax(logits, dim=-1).cpu().item()
        return choices[predition]

    def __call__(self, context, question, choices) -> Any:
        inputs = self.preprocess(context,question,choices)
        logits = self.predict(inputs)
        result = self.postprocess(logits, choices)
        return result

if __name__ == "__main__":
  pipe = MultipleChoicePipeline(model, tokenizer)
  result1 = pipe("男：你今天晚上有時間嗎?我們一起去看電影吧? 女：你喜歡恐怖片和愛情片，但是我喜歡喜劇片","女的最喜歡哪種電影?",["恐怖片","愛情片","喜劇片","科幻片"])
  print(result1)

```

## 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: Use OptimizerNames.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: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9816        | 1.0   | 366  | 0.9955          | 0.5814   |
| 0.7299        | 2.0   | 732  | 1.0239          | 0.5918   |
| 0.3452        | 3.0   | 1098 | 1.3109          | 0.5962   |


### Framework versions

- Transformers 4.50.2
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1