Instructions to use jamescalam/bert-base-dv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jamescalam/bert-base-dv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jamescalam/bert-base-dv")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jamescalam/bert-base-dv") model = AutoModelForMaskedLM.from_pretrained("jamescalam/bert-base-dv", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- de1e26dd173cab9af74b813acee9fe3ace9ba9f4f017477cde1e880a0dbde783
- Size of remote file:
- 266 MB
- SHA256:
- a67f0ca6645f646e4a82bb52d5535ec1ae39b120c17776719c8bf48fdb65c769
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