Instructions to use avichr/hebEMO_joy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use avichr/hebEMO_joy with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="avichr/hebEMO_joy")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("avichr/hebEMO_joy") model = AutoModelForSequenceClassification.from_pretrained("avichr/hebEMO_joy", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from avichr/hebEMO_joy: direct link, hf CLI and curl.
- Browser
- Download file 1.78 kB
-
https://huggingface.co/avichr/hebEMO_joy/resolve/main/training_args.bin
- Command line
-
hf download hf://avichr/hebEMO_joy/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/avichr/hebEMO_joy/resolve/main/training_args.bin
1.78 kB
- Xet hash:
- 6fe1e29796170ef40736feed91512cc25438cc825f00ae11909f46c7bdb8bb4d
- Size of remote file:
- 1.78 kB
- SHA256:
- c5cdc848bc20646ff10258b732887758181fddcf93f95c48da565bf0449c8083
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