Instructions to use nlpodyssey/bert-italian-uncased-iptc-headlines with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nlpodyssey/bert-italian-uncased-iptc-headlines with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nlpodyssey/bert-italian-uncased-iptc-headlines")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nlpodyssey/bert-italian-uncased-iptc-headlines") model = AutoModelForSequenceClassification.from_pretrained("nlpodyssey/bert-italian-uncased-iptc-headlines", device_map="auto") - Notebooks
- Google Colab
- Kaggle
bert-italian-uncased-iptc-headlines
This a bert-base-italian-uncased model fine-tuned for news headlines classification in Italian.
It predicts the top-level category of the IPTC subject taxonomy:
| Class | English label |
|---|---|
| 01000000 | Arts, Culture & Entertainment |
| 02000000 | Crime, Law & Justice |
| 03000000 | Disasters & Accidents |
| 04000000 | Economy, Business & Finance |
| 05000000 | Education |
| 06000000 | Environmental Issues |
| 07000000 | Health |
| 08000000 | Human Interest |
| 09000000 | Labour |
| 10000000 | Lifestyle & Leisure |
| 11000000 | Politics |
| 12000000 | Religion & Belief |
| 13000000 | Science & Technology |
| 14000000 | Social Issues |
| 15000000 | Sport |
| 16000000 | Unrest, Conflicts & War |
| 17000000 | Weather |
Authors
The NLP Odyssey Authors (Matteo Grella, Marco Nicola)
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