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:
- 2f15eeee687f8b6814684aae55da788cda9fff76434922a8d485983728813a37
- Size of remote file:
- 1.11 GB
- SHA256:
- 56c76bbc2f52e74710db18c271346fe5e202a63a7717fa5b0f0476ba95cab854
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.