Instructions to use hf-tiny-model-private/tiny-random-DistilBertForSequenceClassification 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-DistilBertForSequenceClassification 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-DistilBertForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-DistilBertForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-DistilBertForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 502147b6fcdf55a5131c61c4875121b639474b6f79cef9831553686354673d23
- Size of remote file:
- 378 kB
- SHA256:
- 133bdfe4bd42e03dd2bbf1934f26e88c8740ad24b5f85d342f3293db1ed47c74
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