Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use marcolatella/prova_Classi2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use marcolatella/prova_Classi2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="marcolatella/prova_Classi2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("marcolatella/prova_Classi2") model = AutoModelForSequenceClassification.from_pretrained("marcolatella/prova_Classi2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e0ee05abb35edd37e2f36728c01a4442ba9b204be43cf379f846b444e9960a24
- Size of remote file:
- 268 MB
- SHA256:
- 841e1203899e36e2997422b54b3e777eefbb4c10067654c87506bbb16eee3e62
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