خبراء BERT من TF-Hub

عرض على TensorFlow.org تشغيل في Google Colab عرض على جيثب تحميل دفتر انظر نماذج TF Hub

يوضح هذا الكولاب كيفية:

  • نماذج تحميل بيرت من TensorFlow المحور الذين تم تدريبهم على مهام مختلفة بما في ذلك MNLI، والتشكيلات، ومجلات
  • استخدم نموذج معالجة مسبقة مطابقًا لترميز النص الخام وتحويله إلى معرفات
  • قم بإنشاء الإخراج المجمّع والمتسلسل من معرّفات إدخال الرمز المميز باستخدام النموذج المحمل
  • انظر إلى التشابه الدلالي للمخرجات المجمعة للجمل المختلفة

ملاحظة: يجب تشغيل هذا colab مع وقت تشغيل GPU

انشاء والاستيراد

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.

تكوين النموذج

جمل

لنأخذ بعض الجمل من ويكيبيديا لتشغيل النموذج

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.",
]

قم بتشغيل النموذج

سنقوم بتحميل نموذج BERT من TF-Hub ، ونقوم بترميز جملنا باستخدام نموذج المعالجة المسبقة المطابق من TF-Hub ، ثم نقوم بإدخال الجمل المميزة إلى النموذج. للحفاظ على هذا colab سريعًا وبسيطًا ، نوصي بتشغيله على وحدة معالجة الرسومات.

إذهب إلى وقت التشغيلتغيير نوع وقت التشغيل للتأكد من أن يتم تحديد GPU

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)

التشابه الدلالي

الآن دعونا نلقي نظرة على pooled_output التضمينات الأحكام لدينا، ومقارنة مدى تشابه هم في الجمل.

وظائف المساعد

plot_similarity(outputs["pooled_output"], sentences)

بي إن جي

يتعلم أكثر