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:
- e1e560e489d21ab6edebf611ecd12edb803a15858aa29ded024e4b6f00bf5f54
- Size of remote file:
- 2.98 MB
- SHA256:
- 7b98b24e100f648fe4455c46a64c41e2009365ac8f7885cd4464cd034888ce68
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