Token Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
Eval Results (legacy)
Instructions to use Osquery/1a5e2b8e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Osquery/1a5e2b8e with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Osquery/1a5e2b8e")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Osquery/1a5e2b8e") model = AutoModelForTokenClassification.from_pretrained("Osquery/1a5e2b8e", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Osquery/1a5e2b8e: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/Osquery/1a5e2b8e/resolve/main/tokenizer.json
- Command line
-
hf download hf://Osquery/1a5e2b8e/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Osquery/1a5e2b8e/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 0abd02c8ae3c446b4b196b4041ef6b8dc3dce7d172091fa5ca5ce17b4eb7d627
- Size of remote file:
- 17.1 MB
- SHA256:
- f59925fcb90c92b894cb93e51bb9b4a6105c5c249fe54ce1c704420ac39b81af
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.