Instructions to use Jose-Ribeir/stt_Huggin_face_tech 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 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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Jose-Ribeir/stt_Huggin_face_tech") model = AutoModelForQuestionAnswering.from_pretrained("Jose-Ribeir/stt_Huggin_face_tech", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Jose-Ribeir/stt_Huggin_face_tech: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/Jose-Ribeir/stt_Huggin_face_tech/resolve/main/training_args.bin
- Command line
-
hf download hf://Jose-Ribeir/stt_Huggin_face_tech/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Jose-Ribeir/stt_Huggin_face_tech/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- 653e0a0bd89db03968f32d4476cbf59fc69c415111b4c78773b3cc858aa60eff
- Size of remote file:
- 4.92 kB
- SHA256:
- 2e1d8199540dfbfea812382793c690802947c4c0b6d6ac531946e83d56e049e5
路
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