Instructions to use devansvd/bert-model-test-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devansvd/bert-model-test-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="devansvd/bert-model-test-2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("devansvd/bert-model-test-2") model = AutoModelForTokenClassification.from_pretrained("devansvd/bert-model-test-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from devansvd/bert-model-test-2: direct link, hf CLI and curl.
- Browser
- Download file 436 MB
-
https://huggingface.co/devansvd/bert-model-test-2/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://devansvd/bert-model-test-2/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/devansvd/bert-model-test-2/resolve/main/flax_model.msgpack
436 MB
- Xet hash:
- 90d91cd2ead900c344b170cfac8c08ef055786563cb85f5c334f105107410512
- Size of remote file:
- 436 MB
- SHA256:
- 0bdacf56abd5187c721f0df9ef910a7c0cd1f9687d4fb54608ea0998f57ac541
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