negative neutral positive
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: bert-base-uncased-finetuned-GP-Sentiment
  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. -->

# bert-base-uncased-finetuned-GP-Sentiment

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7815
- F1: 0.6808
- Accuracy: 0.7390

## 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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| No log        | 1.0   | 313  | 0.7492          | 0.6448 | 0.6845   |
| 0.7419        | 2.0   | 626  | 0.7281          | 0.6800 | 0.7350   |
| 0.7419        | 3.0   | 939  | 0.7815          | 0.6808 | 0.7390   |
| 0.5309        | 4.0   | 1252 | 0.8782          | 0.6799 | 0.7422   |
| 0.336         | 5.0   | 1565 | 1.1222          | 0.6792 | 0.7390   |
| 0.336         | 6.0   | 1878 | 1.1544          | 0.6671 | 0.7174   |
| 0.219         | 7.0   | 2191 | 1.3721          | 0.6627 | 0.7246   |
| 0.1541        | 8.0   | 2504 | 1.4864          | 0.6652 | 0.7326   |
| 0.1541        | 9.0   | 2817 | 1.6475          | 0.6660 | 0.7446   |
| 0.1094        | 10.0  | 3130 | 1.6749          | 0.6700 | 0.7446   |


### Framework versions

- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
