این صفحه به‌وسیله ‏Cloud Translation API‏ ترجمه شده است.

کارشناسان BERT از TF-Hub

مشاهده در TensorFlow.org

در Google Colab اجرا شود

در GitHub مشاهده کنید

دانلود دفترچه یادداشت

مدل های TF Hub را ببینید

این مجموعه نشان می دهد که چگونه:

مدل بار برت از TensorFlow توپی که در انجام وظایف مختلف از جمله MNLI، پیراهن، و PubMed آموزش داده شده است
از یک مدل پیش پردازش منطبق برای توکن کردن متن خام و تبدیل آن به شناسه استفاده کنید
خروجی ترکیبی و توالی را از شناسه های ورودی نشانه با استفاده از مدل بارگذاری شده تولید کنید
به شباهت معنایی خروجی های ترکیبی جملات مختلف نگاه کنید

توجه: این colab باید با زمان اجرا GPU اجرا شود

راه اندازی و واردات

pip3 install --quiet tensorflow
pip3 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.

مدل را پیکربندی کنید

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"

جمله ها

بیایید چند جمله از ویکی پدیا برای اجرا کردن مدل برداریم

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.",
]

مدل را اجرا کنید

مدل BERT را از TF-Hub بارگذاری می کنیم، جملات خود را با استفاده از مدل پیش پردازش منطبق از TF-Hub توکن می کنیم، سپس جملات نشانه گذاری شده را به مدل وارد می کنیم. برای اینکه این کولب را سریع و ساده نگه دارید، توصیه می کنیم روی GPU اجرا کنید.

برو به زمان اجرا → تغییر نوع زمان اجرا را به مطمئن شوید که GPU انتخاب شده است

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)

شباهت معنایی

حالا اجازه دهید یک نگاهی به pooled_output درونه گیریها از جملات و مقایسه چگونه مشابه آنها در سراسر جملات هستند.

توابع کمکی

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)

png

بیشتر بدانید

پیدا کردن مدل برت بیشتر در TensorFlow توپی
این نوت بوک نشان می دهد استنتاج ساده با برت، شما می توانید یک آموزش پیشرفته تر در مورد برت ریز تنظیم در پیدا tensorflow.org/official_models/fine_tuning_bert
ما با استفاده از فقط یک تراشه GPU برای اجرای مدل، شما می توانید در مورد چگونه به مدل بار با استفاده از tf.distribute در کسب اطلاعات بیشتر tensorflow.org/tutorials/distribute/save_and_load