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* Update convert.py * Update model.py * Update test.py * Update model.py * Update convert.py * Add files via upload * Update convert.py * format * nit * nit --------- Co-authored-by: Awni Hannun <awni@apple.com>
57 lines
1.2 KiB
Markdown
57 lines
1.2 KiB
Markdown
# BERT
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An implementation of BERT [(Devlin, et al., 2019)](https://aclanthology.org/N19-1423/) in MLX.
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## Setup
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Install the requirements:
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```
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pip install -r requirements.txt
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```
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Then convert the weights with:
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```
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python convert.py \
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--bert-model bert-base-uncased \
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--mlx-model weights/bert-base-uncased.npz
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```
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## Usage
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To use the `Bert` model in your own code, you can load it with:
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```python
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import mlx.core as mx
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from model import Bert, load_model
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model, tokenizer = load_model(
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"bert-base-uncased",
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"weights/bert-base-uncased.npz")
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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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tokens = {key: mx.array(v) for key, v in tokens.items()}
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output, pooled = model(**tokens)
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```
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The `output` contains a `Batch x Tokens x Dims` tensor, representing a vector
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for every input token. If you want to train anything at the **token-level**,
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use this.
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The `pooled` contains a `Batch x Dims` tensor, which is the pooled
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representation for each input. If you want to train a **classification**
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model, use this.
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## Test
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You can check the output for the default model (`bert-base-uncased`) matches the
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Hugging Face version with:
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```
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python test.py
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```
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