Token Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use redevaaa/fin5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use redevaaa/fin5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="redevaaa/fin5")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("redevaaa/fin5") model = AutoModelForTokenClassification.from_pretrained("redevaaa/fin5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from redevaaa/fin5: direct link, hf CLI and curl.
- Browser
- Download file 434 MB
-
https://huggingface.co/redevaaa/fin5/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://redevaaa/fin5@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/redevaaa/fin5/resolve/refs%2Fpr%2F1/pytorch_model.bin
434 MB
- Xet hash:
- 5f74681fe05ab9c13d51ad24d31651b285dd7bc47c0d79740116bf691804fc8e
- Size of remote file:
- 434 MB
- SHA256:
- 8f0c2c83bd918de50e11ab23bd0dcb51f5553680c39a635be0b9b00c1ee2d55e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.