pytorch tensorboard bert text-classification transformers generated_from_trainer model-index
---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-pysentimiento-war-tweets
  results: []
---

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# finetuning-pysentimiento-war-tweets

This model is a fine-tuned version of [finiteautomata/beto-sentiment-analysis](https://huggingface.co/finiteautomata/beto-sentiment-analysis) on a dataset of 1500 tweets from Peruvian accounts. It achieves the following results on the evaluation set:
- Loss: 1.7689
- Accuracy: 0.7378
- F1: 0.7456

## Model description

This model in a fine-tuned version of [finiteautomata/beto-sentiment-analysis](https://huggingface.co/finiteautomata/beto-sentiment-analysis) using five labels: **pro_russia**, **against_ukraine**, **neutral**, **against_russia**, **pro_ukraine**.

## Intended uses & limitations

This model shall be used to classify text (more specifically, Spanish tweets) as expressing a position concerning the Russo-Ukrainian war.

## Training and evaluation data

We used an 80/20 training/test split on the aforementioned dataset.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
