Instructions to use mantasb/squeezebert-dappradar-multilabel_desc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mantasb/squeezebert-dappradar-multilabel_desc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mantasb/squeezebert-dappradar-multilabel_desc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mantasb/squeezebert-dappradar-multilabel_desc") model = AutoModelForSequenceClassification.from_pretrained("mantasb/squeezebert-dappradar-multilabel_desc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 12ad400fb182a2d2d4f11b0e47454a98d19cfa96bf7a2a475083209a3e56386c
- Size of remote file:
- 3.52 kB
- SHA256:
- 8071ac1557ee00310a18e900f479baeb4d71a176a9c507988b5771968d738a77
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