bert

BERT

An implementation of BERT (Devlin, et al., 2019) within MLX.

Setup

Install the requirements:

pip install -r requirements.txt

Then convert the weights with:

python convert.py \
    --bert-model bert-base-uncased \
    --mlx-model weights/bert-base-uncased.npz

Usage

To use the Bert model in your own code, you can load it with:

from model import Bert, load_model

model, tokenizer = load_model(
    "bert-base-uncased",
    "weights/bert-base-uncased.npz")

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)

The output contains a Batch x Tokens x Dims tensor, representing a vector for every input token. If you want to train anything at a token-level, you'll want to use this.

The pooled contains a Batch x Dims tensor, which is the pooled representation for each input. If you want to train a classification model, you'll want to use this.

Test

You can check the output for the default model (bert-base-uncased) matches the Hugging Face version with:

python test.py