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Update README.md
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README.md
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@@ -19,9 +19,6 @@ Pretrained model on English language using a masked language modeling (MLM) obje
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[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
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between english and English.
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Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by
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the Hugging Face team.
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## Model description
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Please, refer to the [original model card](https://huggingface.co/bert-base-uncased) for more details on bert-base-uncased.
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@@ -42,7 +39,7 @@ from transformers import BertTokenizer
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import mlx.core as mx
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model = create_model("bert-base-uncased") # it will download weights from this repository
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tokenizer = BertTokenizer.from_pretrained("bert-
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batch = ["This is an example of BERT working on MLX."]
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tokens = tokenizer(batch, return_tensors="np", padding=True)
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[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
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between english and English.
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## Model description
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Please, refer to the [original model card](https://huggingface.co/bert-base-uncased) for more details on bert-base-uncased.
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import mlx.core as mx
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model = create_model("bert-base-uncased") # it will download weights from this repository
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tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
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batch = ["This is an example of BERT working on MLX."]
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tokens = tokenizer(batch, return_tensors="np", padding=True)
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