readme: ---
  
  language:
  
  - ca
  
  tags:
  
  - "catalan"
  
  - "text classification"
  
  - "tecla"
  
  - "CaText"
  
  - "Catalan Textual Corpus"
  
  datasets:
  
  - "projecte-aina/tecla"
  
  metrics:
  - accuracy
  
  model-index:
  - name: roberta-base-ca-cased-tc
    results:
    - task:
        type: text-classification
      dataset:
        name: TeCla
        type: projecte-aina/tecla
      metrics:
        - name: Accuracy
          type: accuracy
          value: 0.740388810634613
  widget:
  
  - text: "Els Pets presenten el seu nou treball al Palau Sant Jordi." 
  
  - text: "Els barcelonins incrementen un 23% l’ús del cotxe des de l’inici de la pandèmia."
  
  - text: "Retards a quatre línies de Rodalies per una avaria entre Sants i plaça de Catalunya."
  
  - text: "Majors de 60 anys i sanitaris començaran a rebre la tercera dosi de la vacuna covid els propers dies."
  
  - text: "Els cinemes Verdi estrenen Verdi Classics, un nou canal de televisió."
  
  ---
  
  # Catalan BERTa (roberta-base-ca) finetuned for Text Classification.
  
  ## Table of Contents
  <details>
  <summary>Click to expand</summary>
  
  - [Model description](#model-description)
  - [Intended uses and limitations](#intended-use)
  - [How to use](#how-to-use)
  - [Limitations and bias](#limitations-and-bias)
  - [Training](#training)
    - [Training data](#training-data)
    - [Training procedure](#training-procedure)
  - [Evaluation](#evaluation)
     - [Variable and metrics](#variable-and-metrics)
     - [Evaluation results](#evaluation-results)
  - [Additional information](#additional-information)
    - [Author](#author)
    - [Contact information](#contact-information)
    - [Copyright](#copyright)
    - [Licensing information](#licensing-information)
    - [Funding](#funding)
    - [Citing information](#citing-information)
    - [Disclaimer](#disclaimer)
  </details>
  
  ## Model description
  The **roberta-base-ca-cased-tc** is a Text Classification (TC) model for the Catalan language fine-tuned from the roberta-base-ca 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.
  
  ## Intended uses and limitations
  
  **roberta-base-ca-cased-tc** 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="projecte-aina/roberta-base-ca-cased-tc")
  example = "Retards a quatre línies de Rodalies per una avaria entre Sants i plaça de Catalunya."
  
  tc_results = nlp(example)
  pprint(tc_results)
  ```
  
  ## Limitations and bias
  At the time of submission, no measures have been taken to estimate the bias embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated.
  
  
  ## Training
  
  ### Training data
  We used the TC dataset in Catalan called [TeCla](https://huggingface.co/datasets/projecte-aina/tecla) for training and evaluation.
  
  ### Training procedure
  The model was trained with a batch size of 16 and a learning rate of 5e-5 for 5 epochs. We then selected the best checkpoint using the downstream task metric in the corresponding development set and then evaluated it on the test set.
  
  ## Evaluation
  
  ### Variable and metrics
  
  This model was finetuned maximizing accuracy.
  
  ## Evaluation results
  We evaluated the _roberta-base-ca-cased-tc_ on the TeCla test set against standard multilingual and monolingual baselines:
  
  | Model        | TeCla (accuracy) | 
  | ------------|:-------------|
  | roberta-base-ca-cased-tc | **74.04** |
  | mBERT       |  70.56 | 
  | XLM-RoBERTa | 71.68 | 
  | WikiBERT-ca |  73.22 | 
  
  For more details, check the fine-tuning and evaluation scripts in the official [GitHub repository](https://github.com/projecte-aina/club).
  
  ## Additional information
  
  ### Author
  Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es)
  
  ### Contact information
  For further information, send an email to aina@bsc.es
  
  ### Copyright
  Copyright (c) 2022 Text Mining Unit at Barcelona Supercomputing Center 
  
  ### Licensing information
  [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
  
  ### 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).
  
  ### Citation Information  
  If you use any of these resources (datasets or models) in your work, please cite our latest paper:
  ```bibtex
  @inproceedings{armengol-estape-etal-2021-multilingual,
      title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for 