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---
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"
} |