---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---

# mbert_trim_ende
<!-- Provide a quick summary of what the model is/does. -->

该模型基于bert-base-multilingual-cased，使用[TextPruner](https://github.com/airaria/TextPruner)对词表进行裁剪，保留iwslt14德英数据集，用于测试bert-fused的翻译效果。
并且在iwslt14德英数据集上进行掩码语言模型微调，数据的拼接方式是： de, en, de[sep]en, en[sep]de。


## Model Details

lang：德英
vocab_size: 119547 -> 21443
model_size: 682M -> 392M
iwslt14 de_en BLEU: ?---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: mbert_trim_ende
  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. -->

# mbert_trim_ende

This model is a fine-tuned version of [miugod/mbert_trim_ende](https://huggingface.co/miugod/mbert_trim_ende) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8260
- Accuracy: 0.8200

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- Transformers 4.29.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.3