---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- nyu-mll/glue
metrics:
- accuracy
- f1
base_model: bert-base-uncased
model-index:
- name: sequence_classification
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: glue
      type: glue
      args: mrpc
    metrics:
    - type: accuracy
      value: 0.8529411764705882
      name: Accuracy
    - type: f1
      value: 0.8943661971830987
      name: F1
---

<!-- 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. -->

# sequence_classification

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7738
- Accuracy: 0.8529
- F1: 0.8944

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 459  | 0.3519          | 0.8627   | 0.9    |
| 0.4872        | 2.0   | 918  | 0.6387          | 0.8333   | 0.8893 |
| 0.2488        | 3.0   | 1377 | 0.7738          | 0.8529   | 0.8944 |


### Framework versions

- Transformers 4.13.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3