Instructions to use mpapucci/BertForItaCaseholdClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mpapucci/BertForItaCaseholdClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mpapucci/BertForItaCaseholdClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mpapucci/BertForItaCaseholdClassification") model = AutoModelForSequenceClassification.from_pretrained("mpapucci/BertForItaCaseholdClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from mpapucci/BertForItaCaseholdClassification: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/mpapucci/BertForItaCaseholdClassification/resolve/main/model.safetensors
- Command line
-
hf download hf://mpapucci/BertForItaCaseholdClassification/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/mpapucci/BertForItaCaseholdClassification/resolve/main/model.safetensors
440 MB
- Xet hash:
- edaf39ac9bba0457efe8f2912cb3cc04ab69e2c59055e043d458bff5752f3c08
- Size of remote file:
- 440 MB
- SHA256:
- 0cd363b95e36ca9ac4d3f41a0ac11e58dc9f164c6f93875e92df472e47813a67
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