Instructions to use suku9/em with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use suku9/em with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="suku9/em")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("suku9/em") model = AutoModelForSequenceClassification.from_pretrained("suku9/em", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from suku9/em: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/suku9/em/resolve/main/training_args.bin
- Command line
-
hf download hf://suku9/em/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/suku9/em/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 2bd0b84bf3e8c663f10bb4a9481dcb46685129d3e24bd6f58f5a622b0f2a443d
- Size of remote file:
- 5.37 kB
- SHA256:
- eb89aa4ba481500c58e2e47b165d059ca40197d3be567f2a2045130ad3c4448a
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