Instructions to use Jsevisal/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/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/bert-gest-pred-seqeval-partialmatch")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Jsevisal/bert-gest-pred-seqeval-partialmatch") model = AutoModelForTokenClassification.from_pretrained("Jsevisal/bert-gest-pred-seqeval-partialmatch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Jsevisal/bert-gest-pred-seqeval-partialmatch: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/Jsevisal/bert-gest-pred-seqeval-partialmatch/resolve/main/training_args.bin
- Command line
-
hf download hf://Jsevisal/bert-gest-pred-seqeval-partialmatch/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Jsevisal/bert-gest-pred-seqeval-partialmatch/resolve/main/training_args.bin
3.58 kB
- Xet hash:
- 0fc1522c641362d17f364fa7552fe2e9bcbd827bc22723fd3f9a7aa0c3904052
- Size of remote file:
- 3.58 kB
- SHA256:
- 7e8ca4ebd7e13fd2d4cbca182c1271296ad2d8cf36fe90447d5dd09eb1939bdd
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