Model Card for Model ID

The model is designed to analyze and classify sections of articles, in English, based on their content and context, 5 classes : background, objectives, methods, results, and conclusions.

The model was trained using a dataset containing 6000 labeled text samples, distributed among the classes.

The model is based on the Roberta-base model.

Model Description

The model can be used directly to classify text into one of the five classes. It takes in a string of text as input and outputs a probability distribution over the five classes. The class with the highest probability is selected as the predicted class.

Developed by: Centrale Supélec Student
Model type: 80M
Language(s) (NLP): English
License: MIT

Training Procedure

The model was trained utilizing the Hugging Face Transformers library. The training approach employed transfer learning, where the original layers of the Roberta-base model were frozen, and only the classification layers were fine-tuned on the labeled dataset. This selective fine-tuning allowed the model to leverage the pre-existing knowledge of the Roberta-base model while adapting to the specific task at hand. To optimize memory usage and accelerate training, mixed precision fp16 was used. Further details regarding the training procedure can be found in the Technical Specifications section.

Out-of-Scope Use

It should not be used for any illegal activities.

Bias, Risks, and Limitations

The model may exhibit biases based on the training data used. It may not perform well on text that is written in languages other than English. The model may produce false positives or false negatives, which could lead to incorrect categorization of text.

Recommendations

Users should be aware of the limitations and biases of the model and use it accordingly. They should also be prepared to handle false positives and false negatives. It is recommended to fine-tune the model for specific downstream tasks and to evaluate its performance on relevant datasets.

Load model directly

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("eliasalbouzidi/roberta-section-article-classifier")

model = AutoModelForSequenceClassification.from_pretrained("eliasalbouzidi/roberta-section-article-classifier")

Use a pipeline as a high-level helper

from transformers import pipeline

pipe = pipeline("text-classification", model="eliasalbouzidi/roberta-section-article-classifier")

Contact

Please reach out to eliasalbouzidi@gmail.com if you have any questions or feedback.