Instructions to use Jsevisal/balanced-augmented-roberta-gest-pred-seqeval-partialmatch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jsevisal/balanced-augmented-roberta-gest-pred-seqeval-partialmatch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Jsevisal/balanced-augmented-roberta-gest-pred-seqeval-partialmatch")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Jsevisal/balanced-augmented-roberta-gest-pred-seqeval-partialmatch") model = AutoModelForTokenClassification.from_pretrained("Jsevisal/balanced-augmented-roberta-gest-pred-seqeval-partialmatch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Jsevisal/balanced-augmented-roberta-gest-pred-seqeval-partialmatch: direct link, hf CLI and curl.
- Browser
- Download file 2.11 MB
-
https://huggingface.co/Jsevisal/balanced-augmented-roberta-gest-pred-seqeval-partialmatch/resolve/main/tokenizer.json
- Command line
-
hf download hf://Jsevisal/balanced-augmented-roberta-gest-pred-seqeval-partialmatch/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Jsevisal/balanced-augmented-roberta-gest-pred-seqeval-partialmatch/resolve/main/tokenizer.json
2.11 MB
- Xet hash:
- c9d8d4bba0887c4142e903395826aa4fcdc81abb3dc065b98886226add3faaf9
- Size of remote file:
- 2.11 MB
- SHA256:
- a60abad17917ed62a89b321bd425a54df25c2d9af89161f31a67bfefa04a9ac9
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