README.md

---
library_name: transformers
tags:
- text-classification
- quantization
- fine-tuning
base_model:
- allenai/scibert_scivocab_uncased
---

# 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

```python
from transformers import pipeline

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