rimusa/CEFR_essay_classification-swedish-cased

Model Card for Model ID

This model was developed as part of the Computational SLA working group at Språkbanken Text. It takes essays written in Swedish by second language learners and assigns them one of the CEFR levels. Of note is that it only uses the first five levels of the scale (A1 to C1), ignoring level C2 due to both lack of training data and it measuring things differently than the other levels do.

Most of the information contained in this Model Card comes from the paper that introduced the present model. Feel free to check it out for more in-depth information.

Model Details

Model Description

[ADD DETAILS HERE]

• Developed by: Språkbanken Text, as part of the Computational SLA group
• Shared by: Ricardo Muñoz Sánchez (rimusa)
• Model type: BERT for text classification
• Language(s): Swedish
• License: [ADD DETAILS HERE]
• Finetuned from model: KB/bert-base-swedish-cased

Model Sources [optional]

• Repository: coming soon
• Paper: Jingle BERT, Jingle BERT, Frozen All the Way: Freezing Layers to Identify CEFR Levels of Second Language Learners Using BERT (link)

Uses

Direct Use

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

We used essays from the SweLL-Pilot

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

• Training regime: [More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: [More Information Needed]
• Hours used: [More Information Needed]
• Cloud Provider: [More Information Needed]
• Compute Region: [More Information Needed]
• Carbon Emitted: [More Information Needed]

Citation [optional]

BibTeX:

@inproceedings{sanchez-etal-2024-jingle,
    title = "Jingle {BERT}, Jingle {BERT}, Frozen All the Way: Freezing Layers to Identify {CEFR} Levels of Second Language Learners Using {BERT}",
    author = "Mu{\~n}oz S{\'a}nchez, Ricardo  and
      Alfter, David  and
      Dobnik, Simon  and
      Szawerna, Maria Irena  and
      Volodina, Elena",
    editor = {Gaillat, Thomas  and
      Mallart, Cyriel  and
      Moreau, Fabienne  and
      Li, Jen-Yu  and
      Drouet, Griselda  and
      Alfter, David  and
      Volodina, Elena  and
      J{\"o}nsson, Arne},
    booktitle = "Proceedings of the 13th Workshop on Natural Language Processing for Computer Assisted Language Learning",
    month = oct,
    year = "2024",
    address = "Rennes, France",
    publisher = "LiU Electronic Press",
    url = "https://aclanthology.org/2024.nlp4call-1.11/",
    pages = "137--152"
}

APA:

Ricardo Muñoz Sánchez, David Alfter, Simon Dobnik, Maria Irena Szawerna, and Elena Volodina. 2024. Jingle BERT, Jingle BERT, Frozen All the Way: Freezing Layers to Identify CEFR Levels of Second Language Learners Using BERT. In Proceedings of the 13th Workshop on Natural Language Processing for Computer Assisted Language Learning, pages 137–152, Rennes, France. LiU Electronic Press.

Model Card Authors

Ricardo Muñoz Sánchez (rimusa)

Model Card Contact

For more information about the model or the present Model Card, you can reach out to:

• Ricardo Muñoz Sánchez ([mailto:[email protected]])
• Elena Volodina ([mailto:[email protected]])