We hope this blog post has provided a helpful introduction to the WALS-Roberta 136.zip model. As you explore the capabilities of this model, we're excited to see the innovative applications and use cases that emerge!
Getting your hands on the new model is straightforward. You can download the weights directly from our repository. wals roberta sets 136zip new
If "136zip" is an archive for a RoBERTa-related release, expected files: We hope this blog post has provided a
# Test a quick encoding text = "The new 136zip release is incredibly fast." inputs = tokenizer(text, return_tensors="pt") outputs = model(**inputs) You can download the weights directly from our repository
WALS Roberta takes the RoBERTa model to the next level by scaling up its architecture and training data. The model has 13.6 billion parameters, making it one of the largest language models ever trained. To put this into perspective, the original BERT model had 340 million parameters, while the largest version of RoBERTa had 355 million parameters.