Instructions to use Maaz66/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Maaz66/model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Maaz66/model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Maaz66/model") model = AutoModelForSequenceClassification.from_pretrained("Maaz66/model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c7d3bc3e4014d78f5c65d237723bb5dc3ef8becb837783d3c9a8546b2cc219e1
- Size of remote file:
- 268 MB
- SHA256:
- 6c367986a0e4e51bcd88d4257acf07ef9e1f03613e365dbb6d33bc0f640c9a0f
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