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from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") model = AutoModelForMaskedLM.from_pretrained("bert-base-uncased")
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Our Research contributions
We’re on a journey to advance and democratize NLP for everyone. Along the way, we contribute to the development of technology for the better.
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
A smaller, faster, lighter, cheaper version of BERT obtained via model distillation.Read more
Hierarchical Multi-Task Learning
Learning embeddings from semantic tasks for multi-task learning. We have open-sourced code and a demo.Read more
Dynamical Language Models
Meta-learning for language modeling
A meta learner is trained via gradient descent to continuously and dynamically update language model weights.Read more
State of the art
Our open source coreference resolution library for coreference. You can train it on your own dataset and language.Read more
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