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:
- f89ef8dff4d8d800c948af1d4abc3a2c308ce573bdf6169fff96f6111b9821d9
- Size of remote file:
- 464 kB
- SHA256:
- f813e2693eb716e3cbe3c104e76a6f9708346b09cb7c44e890018822c28b7fe3
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