Instructions to use harshil10/tiny_bert_flax with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harshil10/tiny_bert_flax with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="harshil10/tiny_bert_flax")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("harshil10/tiny_bert_flax") model = AutoModelForQuestionAnswering.from_pretrained("harshil10/tiny_bert_flax") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f615459d6a00ca7f7b20277a83a2ffa70c248f37e1b668bc18ba931b08d8fdef
- Size of remote file:
- 16.3 MB
- SHA256:
- 73a283022b2553b466662c883cc362832862f69c5be8bee62ae9c629ea66c6eb
路
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