---
tags:
- text-classification
- roberta
- scientific-abstracts
- multi-class
- research-field-classification
datasets:
- ScientificArticleAbstract_Classification
license: apache-2.0
model-index:
- name: ScientificTextClassification_ResearchField
  results:
  - task:
      name: Text Classification
      type: text-classification
    metrics:
    - type: accuracy
      value: 0.941
      name: Accuracy (Top-1)
    - type: macro_f1
      value: 0.935
      name: Macro F1 Score
---

# ScientificTextClassification_ResearchField

## 📚 Overview

This is a **RoBERTa-base** model fine-tuned for the complex task of multi-class classification of scientific article abstracts. The model predicts the **primary research field** (e.g., Physics, Biology, Computer Science) based solely on the abstract text, serving as a powerful tool for automated journal indexing and literature review organization.

## 🧠 Model Architecture

The choice of RoBERTa ensures enhanced robustness and better handling of long-range dependencies common in technical and scientific prose.

* **Base Model:** `roberta-base` (an optimized BERT approach without the next-sentence prediction objective).
* **Classification Head:** Outputs 8 distinct categories (`num_labels: 8`).
* **Input Data:** Detailed scientific abstracts from diverse journals.
* **Output:** A probability distribution over the 8 classes: Physics, Chemistry, Medicine, Computer Science, Biology, Geoscience, Materials Science, and Engineering.
* **Training Dataset:** **ScientificArticleAbstract_Classification**, providing abstracts linked to their high-level research disciplines.

## 🎯 Intended Use

The model offers utility in several scientific and information retrieval contexts:

1.  **Automated Library and Repository Indexing:** Rapidly and accurately tagging new publications with their correct discipline.
2.  **Literature Review Automation:** Filtering large databases of articles to focus on specific fields.
3.  **Grant Proposal Routing:** Assisting research institutions in routing incoming proposals to the appropriate review panel or expert based on the summary.
4.  **Trend Analysis:** Tracking the volume and convergence of research across different fields.

## ⚠️ Limitations

1.  **Interdisciplinary Papers:** The model performs single-label classification. It may struggle with highly interdisciplinary abstracts that bridge two or more distinct fields (e.g., computational chemistry or bio-engineering).
2.  **Vocabulary Drift:** Scientific terminology evolves quickly. New sub-disciplines or extremely novel concepts may not be classified correctly until the model is retrained.
3.  **Class Imbalance:** If the underlying distribution of the eight fields in the real world shifts significantly from the training set, performance may vary.

### MODEL 3: **EcommerceAspectSentiment_BART**

This model is a BART-large sequence-to-sequence model fine-tuned for abstractive multi-aspect sentiment summarization based on Dataset 3 (EcommerceCustomerReview\_MultiAspectRating).

#### config.json

```json
{
  "_name_or_path": "facebook/bart-large",
  "architectures": [
    "BartForConditionalGeneration"
  ],
  "model_type": "bart",
  "vocab_size": 50265,
  "d_model": 1024,
  "encoder_layers": 12,
  "decoder_layers": 12,
  "encoder_attention_heads": 16,
  "decoder_attention_heads": 16,
  "encoder_ffn_dim": 4096,
  "decoder_ffn_dim": 4096,
  "dropout": 0.1,
  "activation_function": "gelu",
  "init_std": 0.02,
  "num_labels": 3,
  "max_position_embeddings": 1024,
  "eos_token_id": 2,
  "bos_token_id": 0,
  "pad_token_id": 1,
  "is_encoder_decoder": true,
  "scale_embedding": false,
  "forced_eos_token_id": 2,
  "transformers_version": "4.35.2"
}