Instructions to use Jsevisal/balanced-augmented-bert-gest-pred-seqeval-partialmatch-2 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-2 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-2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Jsevisal/balanced-augmented-bert-gest-pred-seqeval-partialmatch-2") model = AutoModelForTokenClassification.from_pretrained("Jsevisal/balanced-augmented-bert-gest-pred-seqeval-partialmatch-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Jsevisal/balanced-augmented-bert-gest-pred-seqeval-partialmatch-2: direct link, hf CLI and curl.
- Browser
- Download file 431 MB
-
https://huggingface.co/Jsevisal/balanced-augmented-bert-gest-pred-seqeval-partialmatch-2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Jsevisal/balanced-augmented-bert-gest-pred-seqeval-partialmatch-2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Jsevisal/balanced-augmented-bert-gest-pred-seqeval-partialmatch-2/resolve/main/pytorch_model.bin
431 MB
- Xet hash:
- b072586bceb7b68555f7337ca3a81ef5b4e3ce215c7b75969153cf5605c55ab4
- Size of remote file:
- 431 MB
- SHA256:
- 5ddae85c76ce39e0b69efc0b370f93e8f90f0f24ac9ecff2a6fb70e32ecb6012
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