pytorch roberta text-classification dataset:multi_nli transformers generated_from_trainer license:mit model-index
---
license: mit
tags:
- generated_from_trainer
datasets:
- multi_nli
metrics:
- accuracy
model-index:
- name: roberta-base-mnli
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: multi_nli
      type: multi_nli
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8719307182883341
---

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# roberta-base-mnli

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the multi_nli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4661
- Accuracy: 0.8719

## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4172        | 1.0   | 24544 | 0.4175          | 0.8508   |
| 0.3324        | 2.0   | 49088 | 0.4146          | 0.8609   |
| 0.2191        | 3.0   | 73632 | 0.4661          | 0.8719   |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.11.0+cu102
- Datasets 2.0.0
- Tokenizers 0.11.6
