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:
- 3e16dac645cf44d02e19c0b6585af554216b5df36a3f2fc21a9488e32d05ec54
- Size of remote file:
- 1.89 kB
- SHA256:
- 106adb5bb0c1812deec4c7270814dd3134f3c3f29bde5902e5fbaac8126e10d3
路
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