Instructions to use sfarrukhm/distilbert-da-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sfarrukhm/distilbert-da-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="sfarrukhm/distilbert-da-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("sfarrukhm/distilbert-da-v1") model = AutoModelForMaskedLM.from_pretrained("sfarrukhm/distilbert-da-v1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 44123991534d839b71a9d3f82f392fa66aa6a31d9eefe7b6c7d0a9a8edbba7d9
- Size of remote file:
- 5.43 kB
- SHA256:
- 5d1d637cd1057cb96ddde3312b9a48cb7997b612b5b4eea20ea26dacb47fa716
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