 Lihat di TensorFlow.org |  Jalankan di Google Colab |  Lihat di GitHub |  Unduh buku catatan |  Lihat model TF Hub |
Colab ini menunjukkan cara:
- Model beban Bert dari TensorFlow Hub yang telah dilatih pada tugas yang berbeda termasuk MNLI, skuad, dan PubMed
- Gunakan model prapemrosesan yang cocok untuk menandai teks mentah dan mengonversinya menjadi id
- Hasilkan output yang dikumpulkan dan diurutkan dari id input token menggunakan model yang dimuat
- Lihatlah kesamaan semantik dari keluaran gabungan dari kalimat yang berbeda
Catatan: Colab ini harus dijalankan dengan runtime GPU
Siapkan dan impor
pip3 install --quiet tensorflowpip3 install --quiet tensorflow_text
import seaborn as sns
from sklearn.metrics import pairwise
import tensorflow as tf
import tensorflow_hub as hub
import tensorflow_text as text # Imports TF ops for preprocessing.
Konfigurasikan modelnya
BERT_MODEL = "https://tfhub.dev/google/experts/bert/wiki_books/2" # @param {type: "string"} ["https://tfhub.dev/google/experts/bert/wiki_books/2", "https://tfhub.dev/google/experts/bert/wiki_books/mnli/2", "https://tfhub.dev/google/experts/bert/wiki_books/qnli/2", "https://tfhub.dev/google/experts/bert/wiki_books/qqp/2", "https://tfhub.dev/google/experts/bert/wiki_books/squad2/2", "https://tfhub.dev/google/experts/bert/wiki_books/sst2/2", "https://tfhub.dev/google/experts/bert/pubmed/2", "https://tfhub.dev/google/experts/bert/pubmed/squad2/2"]
# Preprocessing must match the model, but all the above use the same.
PREPROCESS_MODEL = "https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3"
Kalimat
Mari kita ambil beberapa kalimat dari Wikipedia untuk dijalankan melalui model
sentences = [
"Here We Go Then, You And I is a 1999 album by Norwegian pop artist Morten Abel. It was Abel's second CD as a solo artist.",
"The album went straight to number one on the Norwegian album chart, and sold to double platinum.",
"Among the singles released from the album were the songs \"Be My Lover\" and \"Hard To Stay Awake\".",
"Riccardo Zegna is an Italian jazz musician.",
"Rajko Maksimović is a composer, writer, and music pedagogue.",
"One of the most significant Serbian composers of our time, Maksimović has been and remains active in creating works for different ensembles.",
"Ceylon spinach is a common name for several plants and may refer to: Basella alba Talinum fruticosum",
"A solar eclipse occurs when the Moon passes between Earth and the Sun, thereby totally or partly obscuring the image of the Sun for a viewer on Earth.",
"A partial solar eclipse occurs in the polar regions of the Earth when the center of the Moon's shadow misses the Earth.",
]
Jalankan modelnya
Kami akan memuat model BERT dari TF-Hub, menandai kalimat kami menggunakan model pra-pemrosesan yang cocok dari TF-Hub, kemudian memasukkan kalimat token ke model. Agar colab ini tetap cepat dan sederhana, sebaiknya jalankan di GPU.
Pergi ke Runtime → Ubah jenis runtime memastikan bahwa GPU yang dipilih
preprocess = hub.load(PREPROCESS_MODEL)
bert = hub.load(BERT_MODEL)
inputs = preprocess(sentences)
outputs = bert(inputs)
print("Sentences:")
print(sentences)
print("\nBERT inputs:")
print(inputs)
print("\nPooled embeddings:")
print(outputs["pooled_output"])
print("\nPer token embeddings:")
print(outputs["sequence_output"])
Sentences:
["Here We Go Then, You And I is a 1999 album by Norwegian pop artist Morten Abel. It was Abel's second CD as a solo artist.", 'The album went straight to number one on the Norwegian album chart, and sold to double platinum.', 'Among the singles released from the album were the songs "Be My Lover" and "Hard To Stay Awake".', 'Riccardo Zegna is an Italian jazz musician.', 'Rajko Maksimović is a composer, writer, and music pedagogue.', 'One of the most significant Serbian composers of our time, Maksimović has been and remains active in creating works for different ensembles.', 'Ceylon spinach is a common name for several plants and may refer to: Basella alba Talinum fruticosum', 'A solar eclipse occurs when the Moon passes between Earth and the Sun, thereby totally or partly obscuring the image of the Sun for a viewer on Earth.', "A partial solar eclipse occurs in the polar regions of the Earth when the center of the Moon's shadow misses the Earth."]
BERT inputs:
{'input_word_ids': <tf.Tensor: shape=(9, 128), dtype=int32, numpy=
array([[ 101, 2182, 2057, ..., 0, 0, 0],
[ 101, 1996, 2201, ..., 0, 0, 0],
[ 101, 2426, 1996, ..., 0, 0, 0],
...,
[ 101, 16447, 6714, ..., 0, 0, 0],
[ 101, 1037, 5943, ..., 0, 0, 0],
[ 101, 1037, 7704, ..., 0, 0, 0]], dtype=int32)>, 'input_type_ids': <tf.Tensor: shape=(9, 128), dtype=int32, numpy=
array([[0, 0, 0, ..., 0, 0, 0],
[0, 0, 0, ..., 0, 0, 0],
[0, 0, 0, ..., 0, 0, 0],
...,
[0, 0, 0, ..., 0, 0, 0],
[0, 0, 0, ..., 0, 0, 0],
[0, 0, 0, ..., 0, 0, 0]], dtype=int32)>, 'input_mask': <tf.Tensor: shape=(9, 128), dtype=int32, numpy=
array([[1, 1, 1, ..., 0, 0, 0],
[1, 1, 1, ..., 0, 0, 0],
[1, 1, 1, ..., 0, 0, 0],
...,
[1, 1, 1, ..., 0, 0, 0],
[1, 1, 1, ..., 0, 0, 0],
[1, 1, 1, ..., 0, 0, 0]], dtype=int32)>}
Pooled embeddings:
tf.Tensor(
[[ 0.7975967 -0.48580563 0.49781477 ... -0.3448825 0.3972752
-0.2063976 ]
[ 0.57120323 -0.41205275 0.7048914 ... -0.35185075 0.19032307
-0.4041895 ]
[-0.699383 0.1586691 0.06569938 ... -0.0623244 -0.81550187
-0.07923658]
...
[-0.35727128 0.7708977 0.1575658 ... 0.44185698 -0.8644815
0.04504769]
[ 0.91077 0.41501352 0.5606345 ... -0.49263868 0.39640594
-0.05036103]
[ 0.90502906 -0.15505145 0.72672117 ... -0.34734493 0.5052651
-0.19543159]], shape=(9, 768), dtype=float32)
Per token embeddings:
tf.Tensor(
[[[ 1.0919718e+00 -5.3055555e-01 5.4639673e-01 ... -3.5962367e-01
4.2040938e-01 -2.0940571e-01]
[ 1.0143853e+00 7.8079259e-01 8.5375798e-01 ... 5.5282074e-01
-1.1245787e+00 5.6027526e-01]
[ 7.8862888e-01 7.7776514e-02 9.5150793e-01 ... -1.9075295e-01
5.9206045e-01 6.1910731e-01]
...
[-3.2203159e-01 -4.2521179e-01 -1.2823829e-01 ... -3.9094865e-01
-7.9097575e-01 4.2236605e-01]
[-3.1039350e-02 2.3985808e-01 -2.1994556e-01 ... -1.1440065e-01
-1.2680519e+00 -1.6136172e-01]
[-4.2063516e-01 5.4972863e-01 -3.2444897e-01 ... -1.8478543e-01
-1.1342984e+00 -5.8974154e-02]]
[[ 6.4930701e-01 -4.3808129e-01 8.7695646e-01 ... -3.6755449e-01
1.9267237e-01 -4.2864648e-01]
[-1.1248719e+00 2.9931602e-01 1.1799662e+00 ... 4.8729455e-01
5.3400528e-01 2.2836192e-01]
[-2.7057338e-01 3.2351881e-02 1.0425698e+00 ... 5.8993816e-01
1.5367918e+00 5.8425623e-01]
...
[-1.4762508e+00 1.8239072e-01 5.5875197e-02 ... -1.6733241e+00
-6.7398834e-01 -7.2449744e-01]
[-1.5138135e+00 5.8184558e-01 1.6141933e-01 ... -1.2640834e+00
-4.0272138e-01 -9.7197199e-01]
[-4.7153085e-01 2.2817247e-01 5.2776134e-01 ... -7.5483751e-01
-9.0903056e-01 -1.6954714e-01]]
[[-8.6609173e-01 1.6002113e-01 6.5794155e-02 ... -6.2405296e-02
-1.1432388e+00 -7.9403043e-02]
[ 7.7117836e-01 7.0804822e-01 1.1350115e-01 ... 7.8831035e-01
-3.1438148e-01 -9.7487110e-01]
[-4.4002479e-01 -3.0059522e-01 3.5479453e-01 ... 7.9739094e-02
-4.7393662e-01 -1.1001848e+00]
...
[-1.0205302e+00 2.6938522e-01 -4.7310370e-01 ... -6.6319543e-01
-1.4579915e+00 -3.4665459e-01]
[-9.7003460e-01 -4.5014530e-02 -5.9779549e-01 ... -3.0526626e-01
-1.2744237e+00 -2.8051588e-01]
[-7.3144108e-01 1.7699355e-01 -4.6257967e-01 ... -1.6062307e-01
-1.6346070e+00 -3.2060605e-01]]
...
[[-3.7375441e-01 1.0225365e+00 1.5888955e-01 ... 4.7453594e-01
-1.3108152e+00 4.5078207e-02]
[-4.1589144e-01 5.0019276e-01 -4.5844245e-01 ... 4.1482472e-01
-6.2065876e-01 -7.1555024e-01]
[-1.2504390e+00 5.0936425e-01 -5.7103634e-01 ... 3.5491806e-01
2.4368477e-01 -2.0577228e+00]
...
[ 1.3393667e-01 1.1859171e+00 -2.2169831e-01 ... -8.1946820e-01
-1.6737309e+00 -3.9692628e-01]
[-3.3662504e-01 1.6556220e+00 -3.7812781e-01 ... -9.6745497e-01
-1.4801039e+00 -8.3330971e-01]
[-2.2649485e-01 1.6178465e+00 -6.7044652e-01 ... -4.9078423e-01
-1.4535751e+00 -7.1707505e-01]]
[[ 1.5320227e+00 4.4165283e-01 6.3375801e-01 ... -5.3953874e-01
4.1937760e-01 -5.0403677e-02]
[ 8.9377600e-01 8.9395344e-01 3.0626178e-02 ... 5.9039176e-02
-2.0649448e-01 -8.4811246e-01]
[-1.8557828e-02 1.0479081e+00 -1.3329606e+00 ... -1.3869843e-01
-3.7879568e-01 -4.9068305e-01]
...
[ 1.4275622e+00 1.0696816e-01 -4.0635362e-02 ... -3.1778324e-02
-4.1460156e-01 7.0036823e-01]
[ 1.1286633e+00 1.4547651e-01 -6.1372471e-01 ... 4.7491628e-01
-3.9852056e-01 4.3124324e-01]
[ 1.4393284e+00 1.8030575e-01 -4.2854339e-01 ... -2.5022790e-01
-1.0000544e+00 3.5985461e-01]]
[[ 1.4993407e+00 -1.5631223e-01 9.2174333e-01 ... -3.6242130e-01
5.5635113e-01 -1.9797830e-01]
[ 1.1110539e+00 3.6651433e-01 3.5505858e-01 ... -5.4297698e-01
1.4471304e-01 -3.1675813e-01]
[ 2.4048802e-01 3.8115788e-01 -5.9182465e-01 ... 3.7410852e-01
-5.9829473e-01 -1.0166264e+00]
...
[ 1.0158644e+00 5.0260526e-01 1.0737082e-01 ... -9.5642781e-01
-4.1039532e-01 -2.6760197e-01]
[ 1.1848929e+00 6.5479934e-01 1.0166168e-03 ... -8.6154389e-01
-8.8036627e-02 -3.0636966e-01]
[ 1.2669108e+00 4.7768092e-01 6.6289604e-03 ... -1.1585802e+00
-7.0675731e-02 -1.8678737e-01]]], shape=(9, 128, 768), dtype=float32)
Kesamaan semantik
Sekarang mari kita lihat pada pooled_output embeddings kalimat dan bandingkan betapa miripnya mereka di seluruh kalimat.
Fungsi pembantu
def plot_similarity(features, labels):
"""Plot a similarity matrix of the embeddings."""
cos_sim = pairwise.cosine_similarity(features)
sns.set(font_scale=1.2)
cbar_kws=dict(use_gridspec=False, location="left")
g = sns.heatmap(
cos_sim, xticklabels=labels, yticklabels=labels,
vmin=0, vmax=1, cmap="Blues", cbar_kws=cbar_kws)
g.tick_params(labelright=True, labelleft=False)
g.set_yticklabels(labels, rotation=0)
g.set_title("Semantic Textual Similarity")
plot_similarity(outputs["pooled_output"], sentences)
Belajarlah lagi
- Menemukan lebih banyak model Bert pada TensorFlow Hub
- Notebook ini menunjukkan inferensi sederhana dengan Bert, Anda dapat menemukan tutorial yang lebih maju tentang fine-tuning BERT di tensorflow.org/official_models/fine_tuning_bert
- Kami menggunakan hanya satu chip GPU untuk menjalankan model, Anda dapat mempelajari lebih lanjut tentang bagaimana model beban menggunakan tf.distribute di tensorflow.org/tutorials/distribute/save_and_load