Instructions to use TimSchopf/nlp_taxonomy_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TimSchopf/nlp_taxonomy_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TimSchopf/nlp_taxonomy_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TimSchopf/nlp_taxonomy_classifier") model = AutoModelForSequenceClassification.from_pretrained("TimSchopf/nlp_taxonomy_classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f01002968074bee05a93586291ba5ce10d591c6a88bf4d59479c57e19bf5ec34
- Size of remote file:
- 440 MB
- SHA256:
- 7d79fb2400aea6e6cbfd6813c29fe02128f585c4e59ca73d7547f9bb13459a04
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