done

README.md

@@ -10,4 +10,56 @@ language:
 metrics:
 - accuracy
 library_name: transformers
----
+---
+# Model Card: BERT-based CEFR Classifier
+
+## Overview
+
+This repository contains a model trained to predict Common European Framework of Reference (CEFR) levels for a given text using a BERT-based model architecture. The model was fine-tuned on the CEFR dataset, and the `bert-base-...` pre-trained model was used as the base.
+
+## Model Details
+
+- Model architecture: BERT (base model: `bert-base-...`)
+- Task: CEFR level prediction for text classification
+- Training dataset: CEFR dataset
+- Fine-tuning: Epochs, Loss, Accuracy, etc.
+
+## Performance
+
+The model's performance during training is summarized below:
+
+| Epoch | Training Loss | Validation Loss | Accuracy |
+|-------|---------------|-----------------|----------|
+| 1     | 1.491800      | 1.319211        | 0.420690 |
+| 2     | 1.238600      | 0.864768        | 0.700447 |
+| 3     | 0.813200      | 0.538081        | 0.815057 |
+
+Additional metrics:
+
+- Training Loss: 1.1851
+- Training Runtime: 7465.51 seconds
+- Training Samples per Second: 7.633
+- Total Floating Point Operations: 1.499392196785152e+16
+
+## Usage
+
+1. Install the required libraries by running `pip install transformers`.
+2. Load the trained model and use it for CEFR level prediction.
+
+
+from transformers import pipeline
+
+# Load the model
+model_name = "AbdulSami/bert-base-cased-cefr"
+classifier = pipeline("text-classification", model=model_name)
+
+# Text for prediction
+text = "This is a sample text for CEFR classification."
+
+# Predict CEFR level
+predictions = classifier(text)
+
+# Print the predictions
+print(predictions)
+
+