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projecte-aina
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roberta-base-ca-v2-cased-wikicat-ca

Text Classification
Transformers
PyTorch
Catalan
roberta
catalan
text classification
WikiCAT_ca
CaText
Catalan Textual Corpus
Eval Results
Inference Endpoints
Model card Files Community
1
roberta-base-ca-v2-cased-wikicat-ca
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---

language:

- ca

license: apache-2.0

tags:

- "catalan"

- "text classification"

- "WikiCAT_ca"

- "CaText"

- "Catalan Textual Corpus"

datasets:

- "projecte-aina/WikiCAT_ca"

metrics:

- f1

model-index:
- name: roberta-base-ca-v2-cased-wikicat-ca
  results:
  - task: 
      type: text-classification 
    dataset:
      type: projecte-aina/WikiCAT_ca
      name: WikiCAT_ca
    metrics:
      - name: F1
        type: f1
        value: 77.823
        
widget:

- text: "La ressonància magnètica és una prova diagnòstica clau per a moltes malalties."

---

# Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for Text Classification.

## Table of Contents
- [Model Description](#model-description)
- [Intended Uses and Limitations](#intended-uses-and-limitations)
- [How to Use](#how-to-use)
- [Training](#training)
  - [Training Data](#training-data)
  - [Training Procedure](#training-procedure)
- [Evaluation](#evaluation)
   - [Variable and Metrics](#variable-and-metrics)
   - [Evaluation Results](#evaluation-results)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Funding](#funding)
- [Contributions](#contributions)
- [Disclaimer](#disclaimer)

## Model description

The **roberta-base-ca-v2-cased-wikicat-ca** is a Text Classification model for the Catalan language fine-tuned from the [roberta-base-ca-v2](https://huggingface.co/projecte-aina/roberta-base-ca-v2) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers (check the roberta-base-ca-v2 model card for more details).

## Intended Uses and Limitations

**roberta-base-ca-v2-cased-wikicat-ca** model can be used to classify texts. The model is limited by its training dataset and may not generalize well for all use cases.

## How to Use

Here is how to use this model:

```python
from transformers import pipeline
from pprint import pprint

nlp = pipeline("text-classification", model="roberta-base-ca-v2-cased-wikicat-ca")
example = "La ressonància magnètica és una prova diagnòstica clau per a moltes malalties."

tc_results = nlp(example)
pprint(tc_results)
```

## Training

### Training data
We used the TC dataset in Catalan called [WikiCAT_ca](https://huggingface.co/datasets/projecte-aina/WikiCAT_ca) for training and evaluation.

### Training Procedure
The model was trained with a batch size of 4 and three learning rates (1e-5, 3e-5, 5e-5) for 10 epochs. We then selected the best learning rate (3e-5) and checkpoint (epoch 3, step 1857) using the downstream task metric in the corresponding development set.

## Evaluation

### Variable and Metrics

This model was finetuned maximizing F1 (weighted) score.

### Evaluation results
We evaluated the _roberta-base-ca-v2-cased-wikicat-ca_ on the WikiCAT_ca dev set:

| Model        | WikiCAT_ca (F1)| 
| ------------|:-------------|
| roberta-base-ca-v2-cased-wikicat-ca | 77.823 |

For more details, check the fine-tuning and evaluation scripts in the official [GitHub repository](https://github.com/projecte-aina/club).

## Licensing Information

[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)

## Citation Information 


### Funding
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).

## Contributions

[N/A]

### Disclaimer

<details>
<summary>Click to expand</summary>

The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.

When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.

In no event shall the owner and creator of the models (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.