Instructions to use Jose-Ribeir/stt_Huggin_face_tech_new_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jose-Ribeir/stt_Huggin_face_tech_new_data 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="Jose-Ribeir/stt_Huggin_face_tech_new_data")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Jose-Ribeir/stt_Huggin_face_tech_new_data") model = AutoModelForQuestionAnswering.from_pretrained("Jose-Ribeir/stt_Huggin_face_tech_new_data", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Jose-Ribeir/stt_Huggin_face_tech_new_data: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/Jose-Ribeir/stt_Huggin_face_tech_new_data/resolve/main/training_args.bin
- Command line
-
hf download hf://Jose-Ribeir/stt_Huggin_face_tech_new_data/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Jose-Ribeir/stt_Huggin_face_tech_new_data/resolve/main/training_args.bin
4.98 kB
- Xet hash:
- 7f7f7982231d2ec309be444ad2ef18d19edf946139e17635cdaef51d2fefd245
- Size of remote file:
- 4.98 kB
- SHA256:
- cc979162d54729014edc387935a06ee29a331120e3f1ec66f28731b9d5340ea6
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.