Instructions to use Jsevisal/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/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/bert-large-gest-pred-seqeval-partialmatch")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Jsevisal/bert-large-gest-pred-seqeval-partialmatch") model = AutoModelForTokenClassification.from_pretrained("Jsevisal/bert-large-gest-pred-seqeval-partialmatch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Jsevisal/bert-large-gest-pred-seqeval-partialmatch: direct link, hf CLI and curl.
- Browser
- Download file 3.64 kB
-
https://huggingface.co/Jsevisal/bert-large-gest-pred-seqeval-partialmatch/resolve/main/training_args.bin
- Command line
-
hf download hf://Jsevisal/bert-large-gest-pred-seqeval-partialmatch/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Jsevisal/bert-large-gest-pred-seqeval-partialmatch/resolve/main/training_args.bin
3.64 kB
- Xet hash:
- a28e60a251edbc25451e0696ff2e2987b7e11acaf2126ba13f86a3a1cddb2541
- Size of remote file:
- 3.64 kB
- SHA256:
- ab03f19d98b6a4800e7a1b1ebdee0ba5a86a5e73ab50ab5a2431e2ef2e1404ac
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