Instructions to use Jsevisal/ft-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/ft-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/ft-bert-large-gest-pred-seqeval-partialmatch")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Jsevisal/ft-bert-large-gest-pred-seqeval-partialmatch") model = AutoModelForTokenClassification.from_pretrained("Jsevisal/ft-bert-large-gest-pred-seqeval-partialmatch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Jsevisal/ft-bert-large-gest-pred-seqeval-partialmatch: direct link, hf CLI and curl.
- Browser
- Download file 3.64 kB
-
https://huggingface.co/Jsevisal/ft-bert-large-gest-pred-seqeval-partialmatch/resolve/main/training_args.bin
- Command line
-
hf download hf://Jsevisal/ft-bert-large-gest-pred-seqeval-partialmatch/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Jsevisal/ft-bert-large-gest-pred-seqeval-partialmatch/resolve/main/training_args.bin
3.64 kB
- Xet hash:
- 98f883adc6df67e1623fd0ae14c92b9b0ef7798aabb5dd188b00caf6f4cd3f00
- Size of remote file:
- 3.64 kB
- SHA256:
- 276b452b734fbe42283566958d5569795ad4893734ca6401d9050d92612eb825
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.