Update README.md

README.md

@@ -1,3 +1,93 @@
----
-license: mit
----
+---
+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)