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--- |
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pipeline_tag: token-classification |
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tags: |
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- code |
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license: apache-2.0 |
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datasets: |
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- Alex123321/english_cefr_dataset |
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language: |
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- en |
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metrics: |
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- accuracy |
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library_name: transformers |
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--- |
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# Model Card: BERT-based CEFR Classifier |
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## Overview |
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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. |
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## Model Details |
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- Model architecture: BERT (base model: `bert-base-...`) |
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- Task: CEFR level prediction for text classification |
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- Training dataset: CEFR dataset |
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- Fine-tuning: Epochs, Loss, Accuracy, etc. |
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## Performance |
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The model's performance during training is summarized below: |
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| Epoch | Training Loss | Validation Loss | Accuracy | |
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|-------|---------------|-----------------|----------| |
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| 1 | 1.491800 | 1.319211 | 0.420690 | |
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| 2 | 1.238600 | 0.864768 | 0.700447 | |
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| 3 | 0.813200 | 0.538081 | 0.815057 | |
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Additional metrics: |
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- Training Loss: 1.1851 |
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- Training Runtime: 7465.51 seconds |
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- Training Samples per Second: 7.633 |
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- Total Floating Point Operations: 1.499392196785152e+16 |
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## Usage |
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1. Install the required libraries by running `pip install transformers`. |
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2. Load the trained model and use it for CEFR level prediction. |
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from transformers import pipeline |
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# Load the model |
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model_name = "AbdulSami/bert-base-cased-cefr" |
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classifier = pipeline("text-classification", model=model_name) |
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# Text for prediction |
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text = "This is a sample text for CEFR classification." |
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# Predict CEFR level |
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predictions = classifier(text) |
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# Print the predictions |
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print(predictions) |
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