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:
- eb4ae4dfff6b99650fe742830b94de235a2f77998f6c4832a9f085744f1ff0a5
- Size of remote file:
- 17.5 MB
- SHA256:
- 9ed0e94d84dbc40d763d5fff5b11ce849426755c312978e4431586b6ecd5dde7
路
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