rsclf-scibert-improved

Model Card for Research Paper Annotation Classifier

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.

Model Details

Annotation Categories

• Methodology (0): Describes methods or techniques used.
• None (1): Content irrelevant for annotation.
• Novelty (2): Highlights novel contributions.
• Past Work (3): References or compares past research.
• Result (4): Discusses experimental results or findings.

Model Description

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.

• Developed by: Ashutosh Adhikari
• Model type: Fine-tuned text classification model
• Language(s) (NLP): English
• License: Apache 2.0
• Finetuned from model: allenai/scibert_scivocab_uncased

Model Sources

• Repository: N/A
• Paper: N/A
• Demo: N/A

Uses

Direct Use

This model can be used as a standalone text classifier to annotate sentences from research papers based on their semantic content.

Downstream Use

The model can be fine-tuned further for similar tasks, such as classifying academic content in specific domains.

Out-of-Scope Use

The model is not suitable for multi-paragraph classification or non-academic text.

Bias, Risks, and Limitations

The model has been trained on specific datasets derived from research papers, so it may not generalize well to other domains or languages.

Recommendations

Users should evaluate the model’s performance on their specific data and consider fine-tuning for domain-specific tasks.

How to Get Started with the Model

from transformers import pipeline

classifier = pipeline("text-classification", model="AshutoshAdhikari/rsclf-scibert-improved")
result = classifier("This paper introduces a novel technique for...")
print(result)