Token Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use redevaaa/test2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use redevaaa/test2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="redevaaa/test2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("redevaaa/test2") model = AutoModelForTokenClassification.from_pretrained("redevaaa/test2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from redevaaa/test2: direct link, hf CLI and curl.
- Browser
- Download file 431 MB
-
https://huggingface.co/redevaaa/test2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://redevaaa/test2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/redevaaa/test2/resolve/main/pytorch_model.bin
431 MB
- Xet hash:
- 8f3f5df4907c55697b73b106a57e56c939733f25362da27703d9ee8a7ef79225
- Size of remote file:
- 431 MB
- SHA256:
- 620713a825beb335db3a57a9d028d29337bd17dcc3b177c625257a09cb45431d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.