Instructions to use FINDA-FIT/T5-Base-FinArg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FINDA-FIT/T5-Base-FinArg with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("FINDA-FIT/T5-Base-FinArg") model = AutoModelForSeq2SeqLM.from_pretrained("FINDA-FIT/T5-Base-FinArg", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 928d99cbafe79de7f64fb2a0e41de95afd0cb3f840f4b54f923d2c866ef71b16
- Size of remote file:
- 892 MB
- SHA256:
- 78eaf53ebeae4f26e96106e2a6eff4e3a79df110cd9b07fd5fc15476a259d80e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.