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")# 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
- Xet hash:
- e8ce6c693713554041f5e2e8e0d19b4e34a92f8db0912b53466a5bd26c016bf3
- Size of remote file:
- 2.62 MB
- SHA256:
- 439c0e818c7eab8d3462852bf525fe97a706d75d2c43faf3a7d1252e9b33f3e6
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