Instructions to use hf-tiny-model-private/tiny-random-XLMForQuestionAnsweringSimple with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-XLMForQuestionAnsweringSimple with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="hf-tiny-model-private/tiny-random-XLMForQuestionAnsweringSimple")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-XLMForQuestionAnsweringSimple") model = AutoModelForQuestionAnswering.from_pretrained("hf-tiny-model-private/tiny-random-XLMForQuestionAnsweringSimple") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b08486006092e98c72ddc6600d3d36a6ec86d63079dd78f7f4d1d25899a58a91
- Size of remote file:
- 4.19 MB
- SHA256:
- 01357ee1a0130416c660b3b8ea57cee16fc710657d10b56a950ebcff6fc05294
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.