distilbert-base-task-multi-label-classification

Model Card for distilbert-base-task-multi-label-classification Model

Model Details

Model Description

This model is based on the distillation of the BERT base model, which is a widely used language model. The distillation process involves training a smaller model to mimic the behavior and predictions of the larger BERT model. The purpose of this model is to perform fine-tuning on the distilbert-base-pwc-task-multi-label-classification checkpoint for multi-label classification tasks.

Fine-tuning approach can be applied to other models such as RoBERTa, DeBERTa, DistilBERT, CANINE, and more. The notebook provides a practical guide for utilizing these models in various classification scenarios.

• Developed by: Lina Saba
• Model type: bert for multi-label classification
• Language(s) (NLP): Python
• Finetuned from model: distilbert-base-pwc-task-multi-label-classification

Model Sources [optional]

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• Repository: https://colab.research.google.com/drive/1Z314gK2qixK_0ujgQ3nvqvar1iV3QnoF?usp=sharing
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Uses

This model aims to fine-tune BERT to predict one or more labels for a given piece of text. The related notebook illustrates how to fine-tune a distilbert-base-pwc-task-multi-label-classification model, Knowing that it's the same way to fine-tune a RoBERTa, DeBERTa, DistilBERT, CANINE, ... checkpoint.

Direct Use

Predict the labels of a piece of text from this list = { 0: 'aspersion', 1: 'hyperbole', 2: 'lying', 3: 'namecalling', 4: 'noncooperation', 5: 'offtopic', 6: 'other_incivility', 7: 'pejorative', 8: 'sarcasm', 9: 'vulgarity' }

Downstream Use [optional]

This model is fine-tuned on a dataset; a collection of more than 6000 comments on Arizona Daily Star news articles from 2011 that have been manually annotated for various forms of incivility including aspersion, namecalling, sarcasm, and vulgarity.

Bias, Risks, and Limitations

Technical limitations : - Can't print more than one identified label using pipeline. - Half of the test results aren't exactly the same as what expected

Training Details

Training Data

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Training Procedure

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Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

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

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Technical Specifications [optional]

Model Architecture and Objective

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Citation [optional]

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Glossary [optional]

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