Token Classification
Transformers
ONNX
Safetensors
modernbert
ner
on-device
privacy
flowx
openner
logistics
de-identification
Instructions to use flowxai/bolparse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flowxai/bolparse with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="flowxai/bolparse")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("flowxai/bolparse") model = AutoModelForTokenClassification.from_pretrained("flowxai/bolparse", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from flowxai/bolparse: direct link, hf CLI and curl.
- Browser
- Download file 598 MB
-
https://huggingface.co/flowxai/bolparse/resolve/main/model.safetensors
- Command line
-
hf download hf://flowxai/bolparse/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/flowxai/bolparse/resolve/main/model.safetensors
598 MB
- Xet hash:
- 7c1c38272eebc063c41eb105f4460a1c5c1a307defb87271926f2f55bb046c1c
- Size of remote file:
- 598 MB
- SHA256:
- 5d20b7206e5b50c0e7e34f26c4e5bf76ba7edfe95be8942a307941f6451c3de1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.