Token Classification
Transformers
PyTorch
TensorFlow
JAX
Rust
ONNX
Safetensors
English
bert
Eval Results (legacy)
Instructions to use mdizak/bert-large-NER-rust with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mdizak/bert-large-NER-rust with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mdizak/bert-large-NER-rust")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mdizak/bert-large-NER-rust") model = AutoModelForTokenClassification.from_pretrained("mdizak/bert-large-NER-rust", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from mdizak/bert-large-NER-rust: direct link, hf CLI and curl.
- Browser
- Download file 1.33 GB
-
https://huggingface.co/mdizak/bert-large-NER-rust/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://mdizak/bert-large-NER-rust/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/mdizak/bert-large-NER-rust/resolve/main/flax_model.msgpack
1.33 GB
- Xet hash:
- 31f32ac5141649857e79120012da8e7a5faa796ac7663002cd6965194d44738b
- Size of remote file:
- 1.33 GB
- SHA256:
- d63d39520929bbeab16344157582be46f09a3ab43095b14f1b1586636a50f171
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.