BERT base model (uncased) - MLX
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is uncased: it does not make a difference between english and English.
Model description
Please, refer to the original model card for more details on bert-base-uncased.
Use it with mlx-llm
Install mlx-llm from GitHub.
git clone https://github.com/riccardomusmeci/mlx-llm
cd mlx-llm
pip install .
Run
from mlx_llm.model import create_model
from transformers import BertTokenizer
import mlx.core as mx
model = create_model("bert-base-uncased") # it will download weights from this repository
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
batch = ["This is an example of BERT working on MLX."]
tokens = tokenizer(batch, return_tensors="np", padding=True)
tokens = {key: mx.array(v) for key, v in tokens.items()}
output, pooled = model(**tokens)