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stormsidali2001/
IMRAD-introduction-move-zero-sub-moves-classifier

Name: IMRAD-introduction-move-zero-sub-moves-classifier
Availability: InStock

tf-keras

text-classification

stormsidali2001/IMRAD-introduction-sentences-moves-sub-moves-dataset

mit

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IMRaD Introduction Move 0 Sub-move Classifier

This model is a fine-tuned BERT model specialized in classifying sentences from the "Establishing a Research Territory" (Move 0) section of scientific research paper introductions into their corresponding sub-moves:

Show that the research area is important, problematic, or relevant in some way: Highlighting the significance, issues, or relevance of the research topic.
Introduce and review previous research in the field: Presenting a brief overview of existing work and studies related to the topic.

Parent Classifier:

This model is designed to be used in conjunction with the main IMRaD Introduction Move Classifier: https://huggingface.co/stormsidali2001/IMRAD_introduction_moves_classifier.

The parent classifier identifies the overall IMRaD move for each sentence. If a sentence is classified as "Establishing a Research Territory" (Move 0), this sub-move classifier can be used to further analyze the specific purpose of that sentence within Move 0.

Intended Uses & Limitations

Intended Uses:

Scientific Writing Assistance: Help researchers and students understand and refine the structure of their "Establishing a Research Territory" section.
Literature Review Analysis: Quickly identify how authors establish the context and background in research paper introductions.
Educational Tool: Illustrate the different sub-moves used to establish a research territory in scientific writing.

Limitations:

Domain Specificity: The model was trained on scientific research papers and may not be as accurate on other types of text.
Accuracy: While the model has good performance, it is not perfect. Predictions should be carefully reviewed.
Sentence-Level Classification: The model classifies individual sentences and does not provide an analysis of the entire "Establishing a Research Territory" section as a whole.

Training and Evaluation Data

This model was trained and evaluated on a subset of the "IMRAD Introduction Sentences Moves & Sub-moves Dataset" available on Hugging Face: https://huggingface.co/datasets/stormsidali2001/IMRAD-introduction-sentences-moves-sub-moves-dataset

The dataset includes sentences specifically from Move 0 of introductions, labeled with their respective sub-moves.

Training Details:

Base Model: google/bert-base-cased
Implementation: TensorFlow/Keras
Evaluation Metrics: F1 score and accuracy

How to Use

python

from transformers import pipeline

# Load the parent classifier
move_classifier = pipeline("text-classification", model="stormsidali2001/IMRAD_introduction_moves_classifier")

# Load the sub-move classifier for Move 0
submove_classifier_0 = pipeline("text-classification", model="stormsidali2001/IMRAD-introduction-move-zero-sub-moves-classifier")

sentence = "Electronic cigarettes were introduced into the US market in 2007."

# First, classify the move 
move_result = move_classifier(sentence)
move = move_result[0]['label']

if move == "Establishing a Research Territory":
    # If Move 0, classify the sub-move
    submove_result = submove_classifier_0(sentence)
    print(submove_result)