Update README.md

README.md

@@ -8,7 +8,7 @@ widget:
     score: 0.9
   - label: MOVE_1
     score: 0.05
-  - label: MOVE_1
+  - label: MOVE_2
     score: 0.05
 license: mit
 datasets:
@@ -18,29 +18,53 @@ language:
 metrics:
 - f1
 - accuracy
-base_model: google-bert/bert-base-cased
+base_model: google/bert-base-cased
 ---
 
-## Model description
+## 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:
+
+* **MOVE_0 (Establishing a Research Territory):** Setting the context and background information for the research.
+* **MOVE_1 (Establishing a Niche):** Identifying a gap or problem in existing research.
+* **MOVE_2 (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.
 
-More information needed
+## Training and Evaluation Data
 
-## Intended uses & limitations
+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)
 
-More information needed
+The dataset consists of sentences extracted from scientific research paper introductions, manually labeled with their corresponding IMRaD moves.
 
-## Training and evaluation data
+**Training Details:**
 
-More information needed
+* 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.
 
-## Metadata
+## How to Use
 
+You can use this model with the `pipeline` function from the `transformers` library:
 
- ## Model Plot
+```python
+from transformers import pipeline
 
-<details>
-<summary>View Model Plot</summary>
+classifier = pipeline("text-classification", model="your-username/your-model-name")
 
-![Model Image](./model.png)
+sentence = "Electronic cigarettes were introduced into the US market in 2007."
+result = classifier(sentence)
 
-</details>
+print(result)