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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pin/senda") model = AutoModelForSequenceClassification.from_pretrained("pin/senda", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from pin/senda: direct link, hf CLI and curl.
- Browser
- Download file 442 MB
-
https://huggingface.co/pin/senda/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://pin/senda/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/pin/senda/resolve/main/flax_model.msgpack
442 MB
- Xet hash:
- 5e9d6ddf18d4bb5de6d249076f8a479ea291c1a216ebbd82395d6d25b6dfaccd
- Size of remote file:
- 442 MB
- SHA256:
- 91d60466a8afae147292031007e0a96349e60f1d4dadbb6ce6fbcc49499c99bf
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