---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: PolicyBERTa-7d
  results: []
---

<!-- 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. -->

# PolicyBERTa-7d

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8549
- Accuracy: 0.7059
- F1-micro: 0.7059
- F1-macro: 0.6683
- F1-weighted: 0.7033
- Precision: 0.7059
- Recall: 0.7059

## 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: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-micro | F1-macro | F1-weighted | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:-----------:|:---------:|:------:|
| 0.9154        | 1.0   | 1812 | 0.8984          | 0.6785   | 0.6785   | 0.6383   | 0.6772      | 0.6785    | 0.6785 |
| 0.8374        | 2.0   | 3624 | 0.8569          | 0.6957   | 0.6957   | 0.6529   | 0.6914      | 0.6957    | 0.6957 |
| 0.7053        | 3.0   | 5436 | 0.8582          | 0.7019   | 0.7019   | 0.6594   | 0.6967      | 0.7019    | 0.7019 |
| 0.7178        | 4.0   | 7248 | 0.8488          | 0.7030   | 0.7030   | 0.6662   | 0.7011      | 0.7030    | 0.7030 |
| 0.6688        | 5.0   | 9060 | 0.8549          | 0.7059   | 0.7059   | 0.6683   | 0.7033      | 0.7059    | 0.7059 |


### Framework versions

- Transformers 4.16.2
- Pytorch 1.9.0+cu102
- Datasets 1.8.0
- Tokenizers 0.10.3