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 training_args.bin from Jsevisal/balanced-augmented-roberta-gest-pred-seqeval-partialmatch: direct link, hf CLI and curl.
- Browser
- Download file 3.64 kB
-
https://huggingface.co/Jsevisal/balanced-augmented-roberta-gest-pred-seqeval-partialmatch/resolve/main/training_args.bin
- Command line
-
hf download hf://Jsevisal/balanced-augmented-roberta-gest-pred-seqeval-partialmatch/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Jsevisal/balanced-augmented-roberta-gest-pred-seqeval-partialmatch/resolve/main/training_args.bin
3.64 kB
- Xet hash:
- f33d28083986865688913f208233219e18d96900c5071b85e8a817f044bcf396
- Size of remote file:
- 3.64 kB
- SHA256:
- 0caf09d4ae5dbe2d1239fef5b416333b1bee7c7cc2108397dbc9003d0f1ce956
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