Instructions to use fhswf/bert_de_ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fhswf/bert_de_ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="fhswf/bert_de_ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("fhswf/bert_de_ner") model = AutoModelForTokenClassification.from_pretrained("fhswf/bert_de_ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from fhswf/bert_de_ner: direct link, hf CLI and curl.
- Browser
- Download file 437 MB
-
https://huggingface.co/fhswf/bert_de_ner/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://fhswf/bert_de_ner/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/fhswf/bert_de_ner/resolve/main/flax_model.msgpack
437 MB
- Xet hash:
- 9315d96a241ee152fe877e9c290290aebb1d000b711853e115e62161ff821e25
- Size of remote file:
- 437 MB
- SHA256:
- ba17dd4164d716517bd05f3a7548ba138b9b997e08ee9268aa1c3fd00fb07af5
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