This model is a fine-tuned version of a pre-trained model for text classification. It is specifically designed to classify sentences from research papers into annotation categories.
This model is part of the 🤗 Transformers library and has been fine-tuned to enable efficient annotation of academic texts. It takes a single sentence as input and predicts one of the five predefined categories to streamline the research annotation process.
This model can be used as a standalone text classifier to annotate sentences from research papers based on their semantic content.
The model can be fine-tuned further for similar tasks, such as classifying academic content in specific domains.
The model is not suitable for multi-paragraph classification or non-academic text.
The model has been trained on specific datasets derived from research papers, so it may not generalize well to other domains or languages.
Users should evaluate the model’s performance on their specific data and consider fine-tuning for domain-specific tasks.
1from transformers import pipeline
2
3classifier = pipeline("text-classification", model="AshutoshAdhikari/rsclf-scibert-improved")
4result = classifier("This paper introduces a novel technique for...")
5print(result)
