transformers pytorch bert text-classification generated_from_trainer dataset:xnli license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- xnli
license: apache-2.0
metrics:
- accuracy
tags:
- text-classification
- generated_from_trainer
model-index:
- name: xnli_m_bert_only_de
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: xnli
      type: xnli
      config: de
      split: train
      args: de
    metrics:
    - type: accuracy
      value: 0.7562248995983936
      name: Accuracy
---

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# xnli_m_bert_only_de

This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the xnli dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1207
- Accuracy: 0.7562

## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1041        | 1.0   | 3068 | 1.1207          | 0.7562   |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.13.1
