---
pipeline_tag: token-classification
tags:
- code
license: apache-2.0
datasets:
- Alex123321/english_cefr_dataset
language:
- en
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, 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)