Instructions to use ccdv/lsg-distilbert-base-uncased-4096 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ccdv/lsg-distilbert-base-uncased-4096 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ccdv/lsg-distilbert-base-uncased-4096", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ccdv/lsg-distilbert-base-uncased-4096", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("ccdv/lsg-distilbert-base-uncased-4096", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 06717859ec3c9a885fc52591a175835a19e2106396bedc265625b2e1beffbede
- Size of remote file:
- 282 MB
- SHA256:
- 000339bb8ceb93f662bf72fc5c4232a62ac88f2ab7ec28b72a728078a7531ba3
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