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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1e510edb35c0abd13e240a7714a5acc9618daae6de649b1f353cf61556d3b5d1
- Size of remote file:
- 18.2 MB
- SHA256:
- ac407dc390a14856322d3abbe9f00c2cc40fd0ad05b726dbe2e86f9ee754488d
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