LordCoffee/bert-base-cased-cefr

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, 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

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)