Instructions to use pin/senda with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pin/senda with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pin/senda")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pin/senda") model = AutoModelForSequenceClassification.from_pretrained("pin/senda") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5e9d6ddf18d4bb5de6d249076f8a479ea291c1a216ebbd82395d6d25b6dfaccd
- Size of remote file:
- 442 MB
- SHA256:
- 91d60466a8afae147292031007e0a96349e60f1d4dadbb6ce6fbcc49499c99bf
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