Instructions to use harshil10/tiny_bert_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harshil10/tiny_bert_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="harshil10/tiny_bert_2")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("harshil10/tiny_bert_2") model = AutoModelForQuestionAnswering.from_pretrained("harshil10/tiny_bert_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c91d47782ac656a529fc2ad55bae1df5aa914356c163b5b0e3e87994c72c40b1
- Size of remote file:
- 19.5 MB
- SHA256:
- ad24b4202232659a57e12d5f4c0697bb1206b113d3fa86779c9e36bb61b82a18
路
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