Instructions to use Etelis/Sentiment140_XLNET_5E with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Etelis/Sentiment140_XLNET_5E with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Etelis/Sentiment140_XLNET_5E")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Etelis/Sentiment140_XLNET_5E") model = AutoModelForSequenceClassification.from_pretrained("Etelis/Sentiment140_XLNET_5E", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Etelis/Sentiment140_XLNET_5E: direct link, hf CLI and curl.
- Browser
- Download file 3.39 kB
-
https://huggingface.co/Etelis/Sentiment140_XLNET_5E/resolve/main/training_args.bin
- Command line
-
hf download hf://Etelis/Sentiment140_XLNET_5E/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Etelis/Sentiment140_XLNET_5E/resolve/main/training_args.bin
3.39 kB
- Xet hash:
- 370d60497eeb8207b92457ca7b890bfce3104b5561edafe0fbde5035d6ab24fa
- Size of remote file:
- 3.39 kB
- SHA256:
- fc89f36e3183b262bde263a943e787cb86bdb03b11735e04e555025dc31b9f98
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