---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: Bert_Stacked_model_100
  results: []
datasets:
- pkavumba/balanced-copa
- 12ml/e-CARE
pipeline_tag: question-answering
---

<!-- 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_Stacked_model_100

This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1094
- F1: 0.5669

## 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.249         | 1.0   | 1576 | 1.1862          | 0.5172 |
| 1.1963        | 2.0   | 3152 | 1.1461          | 0.5407 |
| 1.1495        | 3.0   | 4728 | 1.1241          | 0.5570 |
| 1.1192        | 4.0   | 6304 | 1.1172          | 0.5634 |
| 1.1025        | 5.0   | 7880 | 1.1094          | 0.5669 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1