---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: w266_model2_BERT_LSTM_1
  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_model2_BERT_LSTM_1

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: 2.6673
- Accuracy: {'accuracy': 0.586}
- F1: {'f1': 0.5941271393567649}
- Precision: {'precision': 0.6305594263991693}
- Recall: {'recall': 0.586}

## 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  | 2.7886          | {'accuracy': 0.563} | {'f1': 0.5737642190234387} | {'precision': 0.6070380044002861} | {'recall': 0.563}              |
| No log        | 2.0   | 250  | 3.2762          | {'accuracy': 0.567} | {'f1': 0.5732065475023022} | {'precision': 0.6124992011023714} | {'recall': 0.567}              |
| No log        | 3.0   | 375  | 3.1370          | {'accuracy': 0.57}  | {'f1': 0.5799666523302439} | {'precision': 0.6122839339063632} | {'recall': 0.57}               |
| 0.0465        | 4.0   | 500  | 3.3590          | {'accuracy': 0.569} | {'f1': 0.5796357806282344} | {'precision': 0.6093440842818532} | {'recall': 0.5689999999999998} |
| 0.0465        | 5.0   | 625  | 3.4285          | {'accuracy': 0.57}  | {'f1': 0.580483223593091}  | {'precision': 0.618976915416096}  | {'recall': 0.57}               |


### Framework versions

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