Instructions to use xdai/mimic_longformer_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xdai/mimic_longformer_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="xdai/mimic_longformer_base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("xdai/mimic_longformer_base") model = AutoModelForMaskedLM.from_pretrained("xdai/mimic_longformer_base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b8f15e73044d69e8d8373f0a3237af53a4b0dbbb5b5466ce591b6aa99d89a13a
- Size of remote file:
- 1.19 GB
- SHA256:
- ae8d47f67550b4595037d92d9f0e3bc2a533efa7c0bdd3f2b93bd20f3428a105
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.