README.md · keras/bert_base_en at 328feb9d7470bc8d5e675e2f84e2853bd0180942

metadata

library_name: keras-hub
license: apache-2.0
language:
  - en
tags:
  - text-classification
pipeline_tag: text-classification

Model Overview

BERT (Bidirectional Encoder Representations from Transformers) is a set of language models published by Google. They are intended for classification and embedding of text, not for text-generation. See the model card below for benchmarks, data sources, and intended use cases.

Weights and Keras model code are released under the Apache 2 License.

Installation

Keras and KerasHub can be installed with:

pip install -U -q keras-hub
pip install -U -q keras>=3

Jax, TensorFlow, and Torch come preinstalled in Kaggle Notebooks. For instruction on installing them in another environment see the Keras Getting Started page.

Presets

The following model checkpoints are provided by the Keras team. Full code examples for each are available below.

Preset name	Parameters	Description
`bert_tiny_en_uncased`	4.39M	2-layer BERT model where all input is lowercased.
`bert_small_en_uncased`	28.76M	4-layer BERT model where all input is lowercased.
`bert_medium_en_uncased`	41.37M	8-layer BERT model where all input is lowercased.
`bert_base_en_uncased`	109.48M	12-layer BERT model where all input is lowercased.
`bert_base_en`	108.31M	12-layer BERT model where case is maintained.
`bert_base_zh`	102.27M	12-layer BERT model. Trained on Chinese Wikipedia.
`bert_base_multi`	177.85M	12-layer BERT model where case is maintained.
`bert_large_en_uncased`	335.14M	24-layer BERT model where all input is lowercased.
`bert_large_en`	333.58M	24-layer BERT model where case is maintained.

Example Usage

import keras
import keras_hub
import numpy as np

Raw string data.

features = ["The quick brown fox jumped.", "I forgot my homework."]
labels = [0, 3]

# Pretrained classifier.
classifier = keras_hub.models.BertClassifier.from_preset(
    "bert_base_en",
    num_classes=4,
)
classifier.fit(x=features, y=labels, batch_size=2)
classifier.predict(x=features, batch_size=2)

# Re-compile (e.g., with a new learning rate).
classifier.compile(
    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer=keras.optimizers.Adam(5e-5),
    jit_compile=True,
)
# Access backbone programmatically (e.g., to change `trainable`).
classifier.backbone.trainable = False
# Fit again.
classifier.fit(x=features, y=labels, batch_size=2)

Preprocessed integer data.

features = {
    "token_ids": np.ones(shape=(2, 12), dtype="int32"),
    "segment_ids": np.array([[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0]] * 2),
    "padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]] * 2),
}
labels = [0, 3]

# Pretrained classifier without preprocessing.
classifier = keras_hub.models.BertClassifier.from_preset(
    "bert_base_en",
    num_classes=4,
    preprocessor=None,
)
classifier.fit(x=features, y=labels, batch_size=2)

Example Usage with Hugging Face URI

import keras
import keras_hub
import numpy as np