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, etc.

Performance

The model's performance during training is summarized below:

Epoch	Training Loss	Validation Loss
1	0.412300	0.396337
2	0.369600	0.388866
3	0.298200	0.419018
4	0.214500	0.481886
5	0.148300	0.557343

--Additional metrics:

--Training Loss: 0.2900624789151278 --Training Runtime: 5168.3962 seconds --Training Samples per Second: 10.642 --Total Floating Point Operations: 1.447162776576e+16

Usage

Install the required libraries by running pip install transformers.
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)