transformers tensorboard safetensors xlm-roberta text-classification generated_from_trainer dataset:xnli base_model:FacebookAI/xlm-roberta-base base_model:finetune:FacebookAI/xlm-roberta-base license:mit model-index autotrain_compatible endpoints_compatible region:us
---
base_model: xlm-roberta-base
datasets:
- xnli
license: mit
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: checkpoints_xnli
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: xnli
      type: xnli
      config: en
      split: train
      args: en
    metrics:
    - type: accuracy
      value: 0.8472664306994984
      name: Accuracy
---

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# checkpoints_xnli

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xnli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4345
- Accuracy: 0.8473

## 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: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4929        | 1.0   | 11045 | 0.4551          | 0.8217   |
| 0.408         | 2.0   | 22090 | 0.4308          | 0.8358   |
| 0.3093        | 3.0   | 33135 | 0.4345          | 0.8473   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
