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---
library_name: tf-keras
pipeline_tag: text-classification
widget:
- text: Electronic cigarettes (also known as vapes, vaporizers, or vape pens) were introduced into the US market in 2007.
output:
- label: Establishing a Research Territory
score: 0.9
- label: Establishing a Niche
score: 0.05
- label: Occupying the Niche
score: 0.05
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 Classifier
This model is a fine-tuned BERT model designed to classify sentences from the introductions of scientific research papers into one of three IMRaD moves:
* **Establishing a Research Territory:** Setting the context and background information for the research.
* **Establishing a Niche:** Identifying a gap or problem in existing research.
* **Occupying the Niche:** Proposing a solution or approach to address the identified gap.
## Intended Uses & Limitations
**Intended Uses:**
* **Scientific Writing Assistance:** Help researchers and students analyze and improve the structure of their introductions by identifying the IMRaD moves present in each sentence.
* **Literature Review Analysis:** Assist in quickly understanding the rhetorical structure of introductions in a set of research papers.
* **Educational Tool:** Illustrate IMRaD concepts and their practical application in scientific writing.
**Limitations:**
* **Domain Specificity:** The model was trained on a dataset of scientific research papers and might not perform as well on other types of text.
* **Accuracy:** While the model achieves good accuracy, it's not perfect. Predictions should be reviewed carefully, especially in complex or ambiguous sentences.
* **Sentence-Level Classification:** The model classifies individual sentences. It does not provide an overall analysis of the entire introduction.
## Training and Evaluation Data
The model was trained and evaluated on the "IMRAD Introduction Sentences Moves & Sub-moves Dataset" available on Hugging Face: [https://huggingface.co/datasets/stormsidali2001/IMRAD-introduction-sentences-moves-sub-moves-dataset](https://huggingface.co/datasets/stormsidali2001/IMRAD-introduction-sentences-moves-sub-moves-dataset)
The dataset consists of sentences extracted from scientific research paper introductions, manually labeled with their corresponding IMRaD moves.
**Training Details:**
* The `bert-base-cased` model from Google was used as the base model.
* Fine-tuning was performed using a TensorFlow/Keras implementation.
* Evaluation metrics include F1 score and accuracy.
## How to Use
You can use this model with the `pipeline` function from the `transformers` library:
```python
from transformers import pipeline
classifier = pipeline("text-classification", model="your-username/your-model-name")
sentence = "Electronic cigarettes were introduced into the US market in 2007."
result = classifier(sentence)
print(result) |