Instructions to use Souvik123/bankstatementmodelver1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Souvik123/bankstatementmodelver1 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="Souvik123/bankstatementmodelver1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Souvik123/bankstatementmodelver1") model = AutoModelForQuestionAnswering.from_pretrained("Souvik123/bankstatementmodelver1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Souvik123/bankstatementmodelver1: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/Souvik123/bankstatementmodelver1/resolve/main/training_args.bin
- Command line
-
hf download hf://Souvik123/bankstatementmodelver1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Souvik123/bankstatementmodelver1/resolve/main/training_args.bin
4.03 kB
- Xet hash:
- d67079379c4cedc94e65e6d234a820cc5a07f49e346787629003b1629fc47b17
- Size of remote file:
- 4.03 kB
- SHA256:
- f6b1b8c38ab9ee6b2ecff98bfac1d9074bdd6b7b48261b3dce0bb9861032638c
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.