Instructions to use horsbug98/Part_2_mBERT_Model_E2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use horsbug98/Part_2_mBERT_Model_E2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="horsbug98/Part_2_mBERT_Model_E2")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("horsbug98/Part_2_mBERT_Model_E2") model = AutoModelForQuestionAnswering.from_pretrained("horsbug98/Part_2_mBERT_Model_E2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 40025dac8bce2d4608bc07b92ed1d9be4508acbde3f240563330ebe72fe35f59
- Size of remote file:
- 2.99 kB
- SHA256:
- 21527e8aaea499e4b2935477ee18a433cea479ac188448eae0bfccc8d9b2f87f
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