Instructions to use erwannd/question_answer_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use erwannd/question_answer_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="erwannd/question_answer_model")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("erwannd/question_answer_model") model = AutoModelForQuestionAnswering.from_pretrained("erwannd/question_answer_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from erwannd/question_answer_model: direct link, hf CLI and curl.
- Browser
- Download file 266 MB
-
https://huggingface.co/erwannd/question_answer_model/resolve/main/tf_model.h5
- Command line
-
hf download hf://erwannd/question_answer_model/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/erwannd/question_answer_model/resolve/main/tf_model.h5
266 MB
- Xet hash:
- 6aeeb19750698ffc1442fd6182a08e135bab35e4dbf4c55bb7300bdf544485ca
- Size of remote file:
- 266 MB
- SHA256:
- f656d586a053623bd917e1fbe11f5d9e027881fae203494937ff06432c8ec3b5
路
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