Instructions to use hf-internal-testing/tiny-random-DebertaV2ForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-DebertaV2ForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-DebertaV2ForSequenceClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-DebertaV2ForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-DebertaV2ForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-internal-testing/tiny-random-DebertaV2ForSequenceClassification: direct link, hf CLI and curl.
- Browser
- Download file 16.6 MB
-
https://huggingface.co/hf-internal-testing/tiny-random-DebertaV2ForSequenceClassification/resolve/8be492d69a7ca8fee52febbc8cd5776cec5f6762/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-DebertaV2ForSequenceClassification@8be492d69a7ca8fee52febbc8cd5776cec5f6762/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-DebertaV2ForSequenceClassification/resolve/8be492d69a7ca8fee52febbc8cd5776cec5f6762/pytorch_model.bin
16.6 MB
- Xet hash:
- 9c48c1abd7085b1478a6bc89206d61e9e9e894809b748fd177b489787c2f7c19
- Size of remote file:
- 16.6 MB
- SHA256:
- fafecbf0d68eea4ca064863cdd4ec0cc52c14c80410130fd00e696fe26efe3ce
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