---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: w266_model3_BERT_CNN
  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. -->

# w266_model3_BERT_CNN

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7935
- Accuracy: {'accuracy': 0.67}
- F1: {'f1': 0.6539863523155215}
- Precision: {'precision': 0.6655888523241464}
- Recall: {'recall': 0.67}

## 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: 5.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy            | F1                         | Precision                         | Recall            |
|:-------------:|:-----:|:----:|:---------------:|:-------------------:|:--------------------------:|:---------------------------------:|:-----------------:|
| 0.7881        | 1.0   | 1923 | 0.8177          | {'accuracy': 0.638} | {'f1': 0.6219209356584174} | {'precision': 0.6325213408748697} | {'recall': 0.638} |
| 0.649         | 2.0   | 3846 | 0.8257          | {'accuracy': 0.669} | {'f1': 0.6701535233107099} | {'precision': 0.672307962349643}  | {'recall': 0.669} |
| 0.4771        | 3.0   | 5769 | 0.8922          | {'accuracy': 0.676} | {'f1': 0.6778795418743319} | {'precision': 0.6805694646691987} | {'recall': 0.676} |
| 0.3403        | 4.0   | 7692 | 1.4285          | {'accuracy': 0.669} | {'f1': 0.666176554548987}  | {'precision': 0.6653390405441227} | {'recall': 0.669} |
| 0.2088        | 5.0   | 9615 | 1.7417          | {'accuracy': 0.67}  | {'f1': 0.6716636513157895} | {'precision': 0.6752339933799478} | {'recall': 0.67}  |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3