Instructions to use hf-internal-testing/tiny-random-RoCBertForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-RoCBertForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-RoCBertForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-RoCBertForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-RoCBertForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3cb10653f576a867917039febfd0b8dc3c27f8ee0d59cfed61a1169006e891ec
- Size of remote file:
- 2.97 MB
- SHA256:
- 04ff95166343797885954a92dc86f64b4504471ab73673cf60b8a9a0b8dcbd13
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