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 special_tokens_map.json from devansvd/bert-model-test-2: direct link, hf CLI and curl.
- Browser
- Download file 112 Bytes
-
https://huggingface.co/devansvd/bert-model-test-2/resolve/main/special_tokens_map.json
- Command line
-
hf download hf://devansvd/bert-model-test-2/special_tokens_map.json
-
curl -L -o special_tokens_map.json https://huggingface.co/devansvd/bert-model-test-2/resolve/main/special_tokens_map.json
112 Bytes
- Xet hash:
- c9020bad8c9b997f1fd69153dd57d3f6b70d3d5aa4b6f35ec9cb9b79811acaba
- Size of remote file:
- 112 Bytes
- SHA256:
- 303df45a03609e4ead04bc3dc1536d0ab19b5358db685b6f3da123d05ec200e3
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