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:
- 232e8d4b998b26e2002b407b64fcf24b6f6915c8aea811b71d40cb02b28e1c7d
- Size of remote file:
- 205 MB
- SHA256:
- e072da5a0d9483cfb6c6b08a01f73802c9016121b40342eace6bbf171d41895f
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