Instructions to use eawang/bart_num_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eawang/bart_num_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eawang/bart_num_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eawang/bart_num_model") model = AutoModelForSequenceClassification.from_pretrained("eawang/bart_num_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from eawang/bart_num_model: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/eawang/bart_num_model/resolve/main/training_args.bin
- Command line
-
hf download hf://eawang/bart_num_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/eawang/bart_num_model/resolve/main/training_args.bin
3.58 kB
- Xet hash:
- 35a13dbad2c23afc6e0ff76d9e4495a8b79c39b993c0ee816ac4785af4913d82
- Size of remote file:
- 3.58 kB
- SHA256:
- e5ecb723867fa81edbd39781579d4edda4d03548e9d2b4dc0886f262e2651eff
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.