Instructions to use hf-internal-testing/tiny-random-RemBertForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-RemBertForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-RemBertForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-RemBertForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-RemBertForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 73ab8f500de1ed30d871fd998932838911c396838f91a91cfc9036274ef6912b
- Size of remote file:
- 18.2 MB
- SHA256:
- 846d07c9ad6ea9efa48a4deb11dea71ab1130dffdb577913ce9306bd612839c3
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