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 pytorch_model.bin from Jsevisal/balanced-augmented-bert-large-gest-pred-seqeval-partialmatch: direct link, hf CLI and curl.
- Browser
- Download file 1.33 GB
-
https://huggingface.co/Jsevisal/balanced-augmented-bert-large-gest-pred-seqeval-partialmatch/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Jsevisal/balanced-augmented-bert-large-gest-pred-seqeval-partialmatch/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Jsevisal/balanced-augmented-bert-large-gest-pred-seqeval-partialmatch/resolve/main/pytorch_model.bin
1.33 GB
- Xet hash:
- 345d31c77df5566cded1c8a38d7d42d184f90c1c3293905eb7dcc2588cd1c796
- Size of remote file:
- 1.33 GB
- SHA256:
- 4823d93418d7d67f2a0a53d01a701b15632372451d25e2f6bff844a0d9cc42db
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