transformers safetensors bert text-classification science scholarly en license:mit autotrain_compatible endpoints_compatible region:us
---
language:
- en
library_name: transformers
license: mit
metrics:
- f1
- accuracy
- precision
- recall
pipeline_tag: text-classification
tags:
- science
- scholarly
- bert
---

This is a fine-tuned BERT-based language model to classify NLP-related research papers as "Survey" or "Non-Survey" papers. The model is fine-tuned on a dataset of 787 survey and 11,805 non-survey papers from the ACL Anthology and the arXiv cs.CL category. Prior to fine-tuning, the model is initialized with weights from [malteos/scincl](https://huggingface.co/malteos/scincl).

## This model was trained during our work on the Natural Language Processing Knowledge Graph (NLP-KG)
 
🖥️ NLP-KG web application: [https://nlpkg.sebis.cit.tum.de](https://nlpkg.sebis.cit.tum.de)

📄 Paper: [https://aclanthology.org/2024.acl-demos.13](https://aclanthology.org/2024.acl-demos.13)  

📽️ Demo video: [https://youtu.be/HOUjFxDySOg](https://youtu.be/HOUjFxDySOg)

💻 GitHub: [https://github.com/NLP-Knowledge-Graph/NLP-KG-WebApp](https://github.com/NLP-Knowledge-Graph/NLP-KG-WebApp)


## How to use the fine-tuned model

```python
from transformers import pipeline, AutoTokenizer

# load tokenizer and init classification pipeline
tokenizer = AutoTokenizer.from_pretrained('TimSchopf/nlp_survey_classifier')
classifier = pipeline("text-classification", model="TimSchopf/nlp_survey_classifier", truncation=True, max_length=512, padding=True)

# prepare data
papers = [{'title': 'Attention Is All You Need', 'abstract': 'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.'},
          {'title': 'SimCSE: Simple Contrastive Learning of Sentence Embeddings', 'abstract': 'This paper presents SimCSE, a simple contrastive learning framework that greatly advances state-of-the-art sentence embeddings. We first describe an unsupervised approach, which takes an input sentence and predicts itself in a contrastive objective, with only standard dropout used as noise. This simple method works surprisingly well, performing on par with previous supervised counterparts. We find that dropout acts as minimal data augmentation, and removing it leads to a representation collapse. Then, we propose a supervised approach, which incorporates annotated pairs from natural language inference datasets into our contrastive learning framework by using "entailment" pairs as positives and "contradiction" pairs as hard negatives. We evaluate SimCSE on standard semantic textual similarity (STS) tasks, and our unsupervised and supervised models using BERT base achieve an average of 76.3% and 81.6% Spearmans correlation respectively, a 4.2% and 2.2% improvement compared to the previous best results. We also show -- both theoretically and empirically -- that the contrastive learning objective regularizes pre-trained embeddings anisotropic space to be more uniform, and it better aligns positive pairs when supervised signals are available.'},
          {'title': 'A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI', 'abstract': 'Recently, artificial intelligence and machine learning in general have demonstrated remarkable performances in many tasks, from image processing to natural language processing, especially with the advent of deep learning. Along with research progress, they have encroached upon many different fields and disciplines. Some of them require high level of accountability and thus transparency, for example the medical sector. Explanations for machine decisions and predictions are thus needed to justify their reliability. This requires greater interpretability, which often means we need to understand the mechanism underlying the algorithms. Unfortunately, the blackbox nature of the deep learning is still unresolved, and many machine decisions are still poorly understood. We provide a review on interpretabilities suggested by different research works and categorize them. The different categories show different dimensions in interpretability research, from approaches that provide "obviously" interpretable information to the studies of complex patterns. By applying the same categorization to interpretability in medical research, it is hoped that (1) clinicians and practitioners can subsequently approach these methods with caution, (2) insights into interpretability will be born with more considerations for medical practices, and (3) initiatives to push forward data-based, mathematically- and technically-grounded medical education are encouraged.'}]
            
# concatenate title and abstract with [SEP] token
title_abs = [d['title'] + tokenizer.sep_token + (d.get('abstract') or '') for d in papers]

# classify papers
classifier(title_abs)
```

## Evaluation Results

The model was evaluated on 20% test data.

* **F1:** 84.35
* **Precision:** 82.38
* **Recall:** 86.53
* **Accuracy:** 98.04


## Citation information
When citing our work in academic papers and theses, please use this BibTeX entry:
``` 
@inproceedings{schopf-matthes-2024-nlp,
    title = "{NLP}-{KG}: A System for Exploratory Search of Scientific Literature in Natural Language Processing",
    author = "Schopf, Tim  and
      Matthes, Florian",
    editor = "Cao, Yixin  and
      Feng, Yang  and
      Xiong, Deyi",
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.acl-demos.13",
    pages = "127--135",
    abstract = "Scientific literature searches are often exploratory, whereby users are not yet familiar with a particular field or concept but are interested in learning more about it. However, existing systems for scientific literature search are typically tailored to keyword-based lookup searches, limiting the possibilities for exploration. We propose NLP-KG, a feature-rich system designed to support the exploration of research literature in unfamiliar natural language processing (NLP) fields. In addition to a semantic search, NLP-KG allows users to easily find survey papers that provide a quick introduction to a field of interest. Further, a Fields of Study hierarchy graph enables users to familiarize themselves with a field and its related areas. Finally, a chat interface allows users to ask questions about unfamiliar concepts or specific articles in NLP and obtain answers grounded in knowledge retrieved from scientific publications. Our system provides users with comprehensive exploration possibilities, supporting them in investigating the relationships between different fields, understanding unfamiliar concepts in NLP, and finding relevant research literature. Demo, video, and code are available at: https://github.com/NLP-Knowledge-Graph/NLP-KG-WebApp.",
}
``` 
