Instructions to use hf-tiny-model-private/tiny-random-MobileBertForSequenceClassification 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-MobileBertForSequenceClassification 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-MobileBertForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-MobileBertForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-MobileBertForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 12546abed843a3bec3260179e964ae8a67510aef574b69d8eb13e7f7627326b8
- Size of remote file:
- 3.13 MB
- SHA256:
- 660e042521e10fc2bf04a433032b7631582ed1a581e509df388616f3e8e8f01d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.