Instructions to use hf-tiny-model-private/tiny-random-RobertaPreLayerNormForSequenceClassification 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-RobertaPreLayerNormForSequenceClassification 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-RobertaPreLayerNormForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-RobertaPreLayerNormForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-RobertaPreLayerNormForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dd7e62e55e2d61396bf40af72a915bfa81188f1e2616ff9df67184294d6246b5
- Size of remote file:
- 374 kB
- SHA256:
- 11917271a0cc4ad1ea5fc3f10dda932a8e2b7788b7b99f541d331896a5868272
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.