Instructions to use mrfriedpotato/effect_t with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrfriedpotato/effect_t with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mrfriedpotato/effect_t")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mrfriedpotato/effect_t") model = AutoModelForSequenceClassification.from_pretrained("mrfriedpotato/effect_t", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mrfriedpotato/effect_t: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/mrfriedpotato/effect_t/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mrfriedpotato/effect_t/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mrfriedpotato/effect_t/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 016a91729ad759fc5bd631dbf796460112cd58bae9a4f76fddc27e197b080cf1
- Size of remote file:
- 438 MB
- SHA256:
- 5db4a27d15634be90695f7db3011318906110bc78c0c8fae1d90d21a9cf3baeb
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