Instructions to use Jsevisal/balanced-augmented-roberta-large-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-large-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-large-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-large-gest-pred-seqeval-partialmatch") model = AutoModelForTokenClassification.from_pretrained("Jsevisal/balanced-augmented-roberta-large-gest-pred-seqeval-partialmatch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Jsevisal/balanced-augmented-roberta-large-gest-pred-seqeval-partialmatch: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/Jsevisal/balanced-augmented-roberta-large-gest-pred-seqeval-partialmatch/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Jsevisal/balanced-augmented-roberta-large-gest-pred-seqeval-partialmatch/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Jsevisal/balanced-augmented-roberta-large-gest-pred-seqeval-partialmatch/resolve/main/pytorch_model.bin
1.42 GB
- Xet hash:
- b184813bacb679ffb7d5749815764b163856ce53526e71a7e6c1b161daf09535
- Size of remote file:
- 1.42 GB
- SHA256:
- b344a64c8c1c07662369d4fc29ba33a33c44ce1c4b6330eedf0896c32642368c
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