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--- |
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license: apache-2.0 |
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language: |
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- it |
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metrics: |
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- accuracy |
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base_model: |
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- DeepMount00/ModernBERT-base-ita |
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pipeline_tag: text-classification |
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--- |
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# Text Quality Classifier (Binary) |
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This model aim to classify the general quality and educational content of a given text. The available labels are 'LABEL_0' that means **bad quality** and 'LABEL_1' that means **good quality**. |
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It can be used to efficiently filter by quality huge quantity of raw text. Useful for creating pretraining italian datasets. |
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The model tend to classify as "good quality" wikipedia-like texts, containing educational, well structured and explained text. |
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## How to get access |
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This is a private model, but if you want to get access explain us how you're going to use this model at <a href="mailto:[email protected]">[email protected]</a> |
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## Eval |
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Durante la fase di valutazione, il modello ha ottenuto le seguenti metriche: |
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* **Eval Loss:** 0.3422 |
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* **Accuracy:** 0.8607 |
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* **F1-Score:** 0.8597 |
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## How to use |
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```python |
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from transformers import pipeline |
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MODEL = "ReDiX/text-quality-classifier-ita" |
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pipe = pipeline("text-classification", model=MODEL, tokenizer=MODEL) |
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example_text = "Questo è un testo di esempio in italiano per la classificazione." |
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result = pipe(example_text) |
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print(f"TEXT: '{example_text}'") |
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print(f"RESULT: {result}") |
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``` |
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# Eval |
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