pytorch tensorboard roberta text-classification dataset:glue transformers generated_from_trainer license:mit
---
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model_index:
- name: roberta-base-finetuned-cola
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: glue
      type: glue
      args: cola
    metric:
      name: Matthews Correlation
      type: matthews_correlation
      value: 0.557882735147727
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# roberta-base-finetuned-cola

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4716
- Matthews Correlation: 0.5579

## Model description

More information needed

## Intended uses & limitations
```python
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("jxuhf/roberta-base-finetuned-cola")
```
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.4981        | 1.0   | 535  | 0.5162          | 0.5081               |
| 0.314         | 2.0   | 1070 | 0.4716          | 0.5579               |


### Framework versions

- Transformers 4.9.0
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
