 Visualizza su TensorFlow.org |  Esegui in Google Colab |  Visualizza su GitHub |  Scarica taccuino |  Vedi i modelli di mozzo TF |
Questa collaborazione mostra come:
- Modelli di carico BERT da tensorflow Hub che sono stati addestrati in compiti diversi, tra cui MnlI, Squad, e PubMed
- Usa un modello di pre-elaborazione corrispondente per tokenizzare il testo non elaborato e convertirlo in ID
- Genera l'output in pool e in sequenza dagli ID di input del token utilizzando il modello caricato
- Guarda la somiglianza semantica degli output aggregati di diverse frasi
Nota: questa collaborazione dovrebbe essere eseguita con un runtime GPU
Configurazione e importazione
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.
Configura il modello
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"
Frasi
Prendiamo alcune frasi da Wikipedia per scorrere il modello
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.",
]
Esegui il modello
Caricheremo il modello BERT da TF-Hub, tokenizziamo le nostre frasi utilizzando il modello di pre-elaborazione corrispondente da TF-Hub, quindi inseriamo le frasi tokenizzate nel modello. Per mantenere questa collaborazione veloce e semplice, ti consigliamo di eseguire su GPU.
Vai a runtime → Cambia il tipo di esecuzione per assicurarsi che sia selezionato 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)
Somiglianza semantica
Ora diamo uno sguardo ai pooled_output incastri delle nostre frasi e confrontare quanto sono simili in tutta frasi.
Funzioni di supporto
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)
Scopri di più
- Trova più modelli BERT su tensorflow Hub
- Questo notebook dimostra semplice inferenza con BERT, è possibile trovare un tutorial più avanzato sulla messa a punto BERT a tensorflow.org/official_models/fine_tuning_bert
- Abbiamo usato un solo circuito integrato GPU per eseguire il modello, si può imparare di più su come modelli di carico utilizzando tf.distribute a tensorflow.org/tutorials/distribute/save_and_load