AshutoshAdhikari
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rsclf-scibert-improved

@@ -1,205 +1,65 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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 ## Model Details
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 ---
 library_name: transformers
+tags: [text-classification, quantization, fine-tuning]
 ---
+# 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:** `distilbert-base-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="your-model-name")
+result = classifier("This paper introduces a novel technique for...")
+print(result)