Instructions to use Neperl/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Neperl/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Neperl/results")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Neperl/results") model = AutoModelForSequenceClassification.from_pretrained("Neperl/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e49fbcb999593c8160fedc98d00cef510d2a7933b9320787742260247c1fab7b
- Size of remote file:
- 5.2 kB
- SHA256:
- 86b4528cf870412ebc252d1e6a7dee12d73959eb0c92013ff1c3ce6b9b1a1120
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