Instructions to use Jsevisal/balanced-augmented-bert-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-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-gest-pred-seqeval-partialmatch")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Jsevisal/balanced-augmented-bert-gest-pred-seqeval-partialmatch") model = AutoModelForTokenClassification.from_pretrained("Jsevisal/balanced-augmented-bert-gest-pred-seqeval-partialmatch") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c3fdda04ede43fc01ae9ee8d7e8af857c2cb857dd81e7bac22c617a67010cd4f
- Size of remote file:
- 431 MB
- SHA256:
- 0c094d78b0f0ca66c7c8fe751af43768e1c48ff09cf6e6a4bd6b4b414a6523d6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.