---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: roberta-base-finetuned-classifier-roberta1
  results: []
---

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# roberta-base-finetuned-classifier-roberta1

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the [lectures](https://huggingface.co/datasets/AlexanderBenady/generated_lectures) dataset.
It achieves the following results on the test set:
- Loss: 0.5266
- Precision: 0.9244
- Recall: 0.9200
- F1-score: 0.9198
- Accuracy: 0.92

## Model description

The model was trained on a lectures dataset of 1000 rows of data. Hyperparameter tuning was also done to achieve these results.

## Intended uses & limitations

More information needed

## Training and evaluation data
The dataset was split into 80% training data, 10% validation data and 10% test data. We ensured that each split would have a proportional number of lectures per field. 

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1-score | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:--------:|:--------:|
| No log        | 1.0   | 25   | 0.4560          | 0.9362    | 0.93   | 0.9308   | 0.93     |
| No log        | 2.0   | 50   | 0.3287          | 0.9519    | 0.95   | 0.9505   | 0.95     |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2