File size: 4,567 Bytes
17d0414 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 |
---
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](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](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) |