Instructions to use RonTuretzky/pcm_perceiver with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RonTuretzky/pcm_perceiver with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RonTuretzky/pcm_perceiver")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RonTuretzky/pcm_perceiver") model = AutoModelForSequenceClassification.from_pretrained("RonTuretzky/pcm_perceiver", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 55993940b17279a34f62d8f5d242cb11e84b0c07cb615410fc6607ed132da37d
- Size of remote file:
- 825 MB
- SHA256:
- fdd14c1e5684c5e87b39893ec17a4407f54f962b862c3484f0f4dd357dde0469
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