---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: kin-sentiC
  results: []
---

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# kin-sentiC

This model is a fine-tuned version of [RogerB/afro-xlmr-large-finetuned-kintweetsD](https://huggingface.co/RogerB/afro-xlmr-large-finetuned-kintweetsD) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8401
- F1: 0.7066

## Model description

The model was trained and evaluated on a Kinyarwanda sentiment analysis dataset of tweets created by [Muhammad et al](https://huggingface.co/datasets/shmuhammad/AfriSenti-twitter-sentiment/viewer/kin).
It classifies Kinyarwanda sentences into three categories: positive (0), neutral (1), and negative (2).

## Intended uses & limitations

The model is specifically designed for classifying Kinyarwanda sentences, with a focus on Kinyarwanda tweets.

## Training and evaluation data

The training data used for training the model were a combination of the [train set from Muhammad et al](https://huggingface.co/datasets/shmuhammad/AfriSenti-twitter-sentiment/viewer/kin/train) and the [val set from Muhammad et al](https://huggingface.co/datasets/shmuhammad/AfriSenti-twitter-sentiment/viewer/kin/) , which served as the validation data during the training process.
For evaluating the model's performance, the test data used were sourced from the [test set from Muhammad et al](https://huggingface.co/datasets/shmuhammad/AfriSenti-twitter-sentiment/viewer/kin/test) 

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 100000
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.913         | 1.0   | 1013 | 0.6933          | 0.7054 |
| 0.737         | 2.0   | 2026 | 0.5614          | 0.7854 |
| 0.646         | 3.0   | 3039 | 0.5357          | 0.8039 |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3