Instructions to use Etelis/rtm_XLNET_5E with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Etelis/rtm_XLNET_5E with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Etelis/rtm_XLNET_5E")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Etelis/rtm_XLNET_5E") model = AutoModelForSequenceClassification.from_pretrained("Etelis/rtm_XLNET_5E", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fba326334f655be497f63a9e0125f72d5b6be0ab06381a1b89f47f678556c00a
- Size of remote file:
- 469 MB
- SHA256:
- 50943cf603938a2a1b97b822ca77bd4ed07072ba5710a45191e25a649d88e4d2
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