README.md

metadata

library_name: tf-keras
pipeline_tag: text-classification
widget:
  - text: >-
      Climate change is a pressing global issue with far-reaching consequences
      for ecosystems and human societies.
    output:
      - label: >-
          Show that the research area is important, problematic, or relevant in
          some way
        score: 0.95
      - label: Introduce and review previous research in the field
        score: 0.05
  - text: >-
      Numerous studies have investigated the impact of rising temperatures on
      marine biodiversity.
    output:
      - label: >-
          Show that the research area is important, problematic, or relevant in
          some way
        score: 0.1
      - label: Introduce and review previous research in the field
        score: 0.9
  - text: >-
      Despite its importance, the specific role of ocean currents in mitigating
      climate change remains poorly understood.
    output:
      - label: >-
          Show that the research area is important, problematic, or relevant in
          some way
        score: 0.55
      - label: Introduce and review previous research in the field
        score: 0.45
license: mit
datasets:
  - stormsidali2001/IMRAD-introduction-sentences-moves-sub-moves-dataset
language:
  - en
metrics:
  - f1
  - accuracy
base_model: google/bert-base-cased

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

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)