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from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained("bert-base-uncased")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input) |
You can easily switch to different models by changing the model name, e.g., bert-large-uncased, which has 340 million parameters. Generally, models with more parameters tend to deliver better performance.
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