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Cette collaboration montre comment :
- Modèles de charge BERT de tensorflow Hub qui ont été formés à différentes tâches , notamment Mnll, SQUAD et PubMed
- Utilisez un modèle de prétraitement correspondant pour tokeniser le texte brut et le convertir en identifiants
- Générer la sortie groupée et de séquence à partir des identifiants d'entrée de jeton à l'aide du modèle chargé
- Regardez la similitude sémantique des sorties groupées de différentes phrases
Remarque : Cette collaboration doit être exécutée avec un environnement d'exécution GPU
Configurer et importer
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.
Configurer le modèle
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"
Phrases
Prenons quelques phrases de Wikipedia pour parcourir le modèle
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.",
]
Exécuter le modèle
Nous allons charger le modèle BERT de TF-Hub, tokeniser nos phrases à l'aide du modèle de prétraitement correspondant de TF-Hub, puis introduire les phrases tokenisées dans le modèle. Pour que cette collaboration reste simple et rapide, nous vous recommandons de l'exécuter sur GPU.
Aller à Runtime → Changer le type d'exécution pour vous assurer que le GPU est sélectionné
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)
Similitude sémantique
Maintenant , nous allons jeter un oeil à la pooled_output incorporations de nos phrases et de comparer la façon dont ils sont semblables dans des phrases.
Fonctions d'assistance
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)
Apprendre encore plus
- Trouver plus de modèles BERT sur tensorflow Hub
- Ce bloc - notes montre l' inférence simple avec BERT, vous pouvez trouver un tutoriel plus avancé au sujet BERT réglage fin à tensorflow.org/official_models/fine_tuning_bert
- Nous avons utilisé une seule puce GPU pour exécuter le modèle, vous pouvez en savoir plus sur les modèles de charge à l' aide tf.distribute à tensorflow.org/tutorials/distribute/save_and_load