Instructions to use Jsevisal/balanced-augmented-bert-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-bert-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-bert-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-bert-large-gest-pred-seqeval-partialmatch") model = AutoModelForTokenClassification.from_pretrained("Jsevisal/balanced-augmented-bert-large-gest-pred-seqeval-partialmatch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Jsevisal/balanced-augmented-bert-large-gest-pred-seqeval-partialmatch: direct link, hf CLI and curl.
- Browser
- Download file 3.64 kB
-
https://huggingface.co/Jsevisal/balanced-augmented-bert-large-gest-pred-seqeval-partialmatch/resolve/main/training_args.bin
- Command line
-
hf download hf://Jsevisal/balanced-augmented-bert-large-gest-pred-seqeval-partialmatch/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Jsevisal/balanced-augmented-bert-large-gest-pred-seqeval-partialmatch/resolve/main/training_args.bin
3.64 kB
- Xet hash:
- 2ebc28e2b626b739cb500ab87d67a92a7105265549a9aa178fb63af64ab459e0
- Size of remote file:
- 3.64 kB
- SHA256:
- 31baa22e509ee258183caa7f8590ecbc10b8be5acf8dcbe4b4138b72aee3842f
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