Instructions to use wilsontam/bert-base-uncased-dstc9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wilsontam/bert-base-uncased-dstc9 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="wilsontam/bert-base-uncased-dstc9")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("wilsontam/bert-base-uncased-dstc9") model = AutoModelForMaskedLM.from_pretrained("wilsontam/bert-base-uncased-dstc9") - Notebooks
- Google Colab
- Kaggle
Goal
This Bert model is trained using DSTC9 training + validation data for dialogue modeling purpose. Data link: https://github.com/alexa/alexa-with-dstc9-track1-dataset
Credit: Shuhan Yuan, Wilson Tam
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