Instructions to use hf-internal-testing/tiny-random-RobertaPreLayerNormForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-RobertaPreLayerNormForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-internal-testing/tiny-random-RobertaPreLayerNormForTokenClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-RobertaPreLayerNormForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-internal-testing/tiny-random-RobertaPreLayerNormForTokenClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-internal-testing/tiny-random-RobertaPreLayerNormForTokenClassification: direct link, hf CLI and curl.
- Browser
- Download file 369 kB
-
https://huggingface.co/hf-internal-testing/tiny-random-RobertaPreLayerNormForTokenClassification/resolve/3ad5814ba126b41e18c1978c970e396fab6da9bf/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-RobertaPreLayerNormForTokenClassification@3ad5814ba126b41e18c1978c970e396fab6da9bf/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-RobertaPreLayerNormForTokenClassification/resolve/3ad5814ba126b41e18c1978c970e396fab6da9bf/pytorch_model.bin
369 kB
- Xet hash:
- 834c1b064944989dd32f1da1dc0d3cf8443f3c561847803e0af15d9ce69c981b
- Size of remote file:
- 369 kB
- SHA256:
- dd768c54ea286d9ce0b3ec90eab970a87a70ca3788c9560f9ce439eeaf693b56
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