---
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: 0.8379
- Accuracy: {'accuracy': 0.638}
- F1: {'f1': 0.6347028754401022}
- Precision: {'precision': 0.6330789574164506}
- Recall: {'recall': 0.638}

## 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            |
|:-------------:|:-----:|:----:|:---------------:|:-------------------:|:--------------------------:|:---------------------------------:|:-----------------:|
| No log        | 1.0   | 125  | 0.9131          | {'accuracy': 0.575} | {'f1': 0.5238639982308713} | {'precision': 0.5763958598091251} | {'recall': 0.575} |
| No log        | 2.0   | 250  | 0.8958          | {'accuracy': 0.594} | {'f1': 0.5872686350677672} | {'precision': 0.5839805403784284} | {'recall': 0.594} |
| No log        | 3.0   | 375  | 1.0090          | {'accuracy': 0.579} | {'f1': 0.5805384614353013} | {'precision': 0.5937972757207018} | {'recall': 0.579} |
| 0.6603        | 4.0   | 500  | 1.2606          | {'accuracy': 0.576} | {'f1': 0.5817117729696419} | {'precision': 0.5920616341004294} | {'recall': 0.576} |
| 0.6603        | 5.0   | 625  | 1.4795          | {'accuracy': 0.577} | {'f1': 0.5838498885025925} | {'precision': 0.6094074383728622} | {'recall': 0.577} |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.2
- Tokenizers 0.13.3