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--- |
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license: apache-2.0 |
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datasets: |
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- AyoubChLin/CNN_News_Articles_2011-2022 |
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language: |
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- en |
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metrics: |
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- accuracy |
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pipeline_tag: zero-shot-classification |
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--- |
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# DistilBERT for Zero Shot Classification |
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This repository contains a DistilBERT model trained for zero-shot classification on CNN articles. The model has been evaluated on CNN articles and achieved an accuracy of 0.956 and an F1 score of 0.955. |
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## Model Details |
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- Architecture: DistilBERT |
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- Training Data: CNN articles |
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- Accuracy: 0.956 |
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- F1 Score: 0.955 |
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## Usage |
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To use this model for zero-shot classification, you can follow the steps below: |
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1. Load the trained model: |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("AyoubChLin/DistilBERT_ZeroShot") |
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model = AutoModelForSequenceClassification.from_pretrained("AyoubChLin/DistilBERT_ZeroShot") |
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``` |
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4. Classify text using zero-shot classification: |
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```python |
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from transformers import pipeline |
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# Create a zero-shot classification pipeline |
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classifier = pipeline("zero-shot-classification", model=model, tokenizer=tokenizer) |
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# Classify a sentence |
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sentence = "The latest scientific breakthroughs in medicine" |
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candidate_labels = ["politics", "sports", "technology", "business"] |
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result = classifier(sentence, candidate_labels) |
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print(result) |
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``` |
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The output will be a dictionary containing the classified label and the corresponding classification score. |
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## About the Author |
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This work was created by Ayoub Cherguelaine. |
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If you have any questions or suggestions regarding this repository or the trained model, feel free to reach out to Ayoub Cherguelaine. |