Instructions to use horsbug98/Part_2_XLM_Model_E1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use horsbug98/Part_2_XLM_Model_E1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="horsbug98/Part_2_XLM_Model_E1")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("horsbug98/Part_2_XLM_Model_E1") model = AutoModelForQuestionAnswering.from_pretrained("horsbug98/Part_2_XLM_Model_E1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d2c38a1e9d3590d0035a460075552c9948c64fb021ca8ccb09d25722e6a0b0d9
- Size of remote file:
- 2.99 kB
- SHA256:
- ecb77d6a35f6020c6917ab9414cc69c7b65c7e2f30c821759cec8008d252417d
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