Instructions to use hf-tiny-model-private/tiny-random-XLMForQuestionAnsweringSimple with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-XLMForQuestionAnsweringSimple 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="hf-tiny-model-private/tiny-random-XLMForQuestionAnsweringSimple")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-XLMForQuestionAnsweringSimple") model = AutoModelForQuestionAnswering.from_pretrained("hf-tiny-model-private/tiny-random-XLMForQuestionAnsweringSimple", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from hf-tiny-model-private/tiny-random-XLMForQuestionAnsweringSimple: direct link, hf CLI and curl.
- Browser
- Download file 4.28 MB
-
https://huggingface.co/hf-tiny-model-private/tiny-random-XLMForQuestionAnsweringSimple/resolve/main/tf_model.h5
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-XLMForQuestionAnsweringSimple/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/hf-tiny-model-private/tiny-random-XLMForQuestionAnsweringSimple/resolve/main/tf_model.h5
4.28 MB
- Xet hash:
- e81a98bcc68bccf07a24a3e6458f0caa6482bfa6f32076b9e2a6a1eaf51f8d16
- Size of remote file:
- 4.28 MB
- SHA256:
- a0bccaf0abd0771b156dda1766ae4f92e8573ba3b296e2f1871ff7e442b4f709
路
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