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 tokenizer_config.json from devansvd/bert-model-test-2: direct link, hf CLI and curl.
- Browser
- Download file 252 Bytes
-
https://huggingface.co/devansvd/bert-model-test-2/resolve/refs%2Fpr%2F1/tokenizer_config.json
- Command line
-
hf download hf://devansvd/bert-model-test-2@refs/pr/1/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/devansvd/bert-model-test-2/resolve/refs%2Fpr%2F1/tokenizer_config.json
252 Bytes
- Xet hash:
- 242a158fac5f3f4cdaec0d39bb86ef79d140255f195d5d06e2494c5f2ca8c1a1
- Size of remote file:
- 252 Bytes
- SHA256:
- ffc7913f6084b138dad68de5a0c2f0ee25ac983709260901e01f53be5796b9d9
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