Instructions to use hf-tiny-model-private/tiny-random-RoCBertForSequenceClassification 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-RoCBertForSequenceClassification 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-RoCBertForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-RoCBertForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-RoCBertForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 10dd24d4665534a567eeb45f344e17df36d371571740d558a5f2d5b1c7a529d8
- Size of remote file:
- 2.97 MB
- SHA256:
- cfec7138ec8321758eb079eda722724d60e5285f19ed374a444655f48fc4a1a5
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