metadata
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, 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
- 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)