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এই কোল্যাব দেখায় কিভাবে:
- লোড বার্ট মডেল থেকে TensorFlow হাব যে MNLI, দল, এবং পাবমেড সহ বিভিন্ন কর্ম উপর প্রশিক্ষণ প্রদান করা হয়েছে
- কাঁচা টেক্সটকে টোকেনাইজ করতে এবং এটিকে আইডিতে রূপান্তর করতে একটি ম্যাচিং প্রিপ্রসেসিং মডেল ব্যবহার করুন
- লোড করা মডেল ব্যবহার করে টোকেন ইনপুট আইডি থেকে পুলড এবং সিকোয়েন্স আউটপুট তৈরি করুন
- বিভিন্ন বাক্যের পুল আউটপুটগুলির শব্দার্থগত মিল দেখুন
দ্রষ্টব্য: এই কোল্যাবটি একটি GPU রানটাইম দিয়ে চালানো উচিত
সেট আপ এবং আমদানি
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.
মডেল কনফিগার করুন
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"
বাক্য
মডেলের মাধ্যমে চালানোর জন্য উইকিপিডিয়া থেকে কিছু বাক্য নেওয়া যাক
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.",
]
মডেল চালান
আমরা TF-Hub থেকে BERT মডেলটি লোড করব, TF-Hub থেকে ম্যাচিং প্রিপ্রসেসিং মডেল ব্যবহার করে আমাদের বাক্যগুলিকে টোকেনাইজ করব, তারপর মডেলটিতে টোকেনাইজড বাক্যগুলি ফিড করব৷ এই কোল্যাবটিকে দ্রুত এবং সহজ রাখতে, আমরা GPU-তে চালানোর পরামর্শ দিই।
রানটাইম → পরিবর্তন রানটাইম টাইপ যান নিশ্চিত যাতে GPU নির্বাচন করা হয় করতে
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)
শব্দার্থিক মিল
এখন কটাক্ষপাত করা যাক pooled_output আমাদের বাক্যের embeddings এবং তুলনা কিভাবে অনুরূপ তারা বাক্য জুড়ে আছে।
হেল্পার ফাংশন
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)
আরও জানুন
- সম্পর্কে আরও বার্ট মডেলের খুঁজুন TensorFlow হাব
- এই নোটবুক বার্ট সঙ্গে সহজ অনুমান প্রমান তোমার দিকে ফাইন টিউনিং বার্ট সম্পর্কে একটি আরো উন্নত টিউটোরিয়াল জানতে পারেন tensorflow.org/official_models/fine_tuning_bert
- আমরা মডেল চালানোর জন্য শুধু একটা জিপিইউ চিপ ব্যবহৃত, কেমন ভার মডেলের এ tf.distribute ব্যবহার সম্পর্কে আরও জানতে পারেন tensorflow.org/tutorials/distribute/save_and_load