Instructions to use hf-tiny-model-private/tiny-random-LukeForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-LukeForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-tiny-model-private/tiny-random-LukeForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-LukeForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-LukeForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ffa86e2234bdc0ffe09e134bb5aa3cbb73f43586f1166797d40e1a767b8bfc70
- Size of remote file:
- 6.79 MB
- SHA256:
- 5882f0c283e27a4bdf2be541d530187cc89f204d3b1166a2f3adbc75ec8cb5b6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.