examples/tutorial/BERTSeqClassifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "split-salad",
   "metadata": {},
   "source": [
    "# BERTSeqClassifier\n",
    "\n",
    "可用的中文预训练参数：[`bert-base`](https://storage.googleapis.com/bert_models/2018_11_03/chinese_L-12_H-768_A-12.zip),[`roberta-wwm-ext-base`](https://drive.google.com/uc?export=download&id=1jMAKIJmPn7kADgD3yQZhpsqM-IRM1qZt),[`roberta-wwm-ext-large`](https://drive.google.com/uc?export=download&id=1dtad0FFzG11CBsawu8hvwwzU2R0FDI94),[`macbert-base`](https://drive.google.com/uc?export=download&id=1aV69OhYzIwj_hn-kO1RiBa-m8QAusQ5b),[`macbert-large`](https://drive.google.com/uc?export=download&id=1lWYxnk1EqTA2Q20_IShxBrCPc5VSDCkT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "rotary-academy",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "v2.5.0\n"
     ]
    }
   ],
   "source": [
    "import uf\n",
    "\n",
    "print(uf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "velvet-symbol",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uf.BERTSeqClassifier(\n",
      "    config_file=\"../../ref/bert_config.json\",\n",
      "    vocab_file=\"../../ref/vocab.txt\",\n",
      "    max_seq_length=128,\n",
      "    label_size=None,\n",
      "    init_checkpoint=None,\n",
      "    output_dir=None,\n",
      "    gpu_ids=None,\n",
      "    do_lower_case=True,\n",
      "    truncate_method=\"LIFO\",\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "model = uf.BERTSeqClassifier(\"../../ref/bert_config.json\", \"../../ref/vocab.txt\")\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "drawn-breathing",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_tokenized = [[\"天\", \"亮\", \"以\", \"前\", \"说\", \"再\", \"见\"], [\"笑\", \"着\", \"泪\", \"流\", \"满\", \"面\"]]\n",
    "y = [[0, 0, 0, 0, 0, 1, 1], [0, 0, 1, 1, 0, 0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "vanilla-fault",
   "metadata": {},
   "source": [
    "# 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "excellent-religion",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\Users\\Luv_d\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\tensorflow\\python\\util\\dispatch.py:1176: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Luv_d\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\uf-2.5.0-py3.9.egg\\uf\\apps\\bert\\bert.py:272: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.\n",
      "  query_layer = tf.layers.dense(\n",
      "c:\\Users\\Luv_d\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\uf-2.5.0-py3.9.egg\\uf\\apps\\bert\\bert.py:282: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.\n",
      "  key_layer = tf.layers.dense(\n",
      "c:\\Users\\Luv_d\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\uf-2.5.0-py3.9.egg\\uf\\apps\\bert\\bert.py:292: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.\n",
      "  value_layer = tf.layers.dense(\n",
      "c:\\Users\\Luv_d\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\uf-2.5.0-py3.9.egg\\uf\\apps\\bert\\bert.py:416: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.\n",
      "  attention_output = tf.layers.dense(\n",
      "c:\\Users\\Luv_d\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\uf-2.5.0-py3.9.egg\\uf\\apps\\bert\\bert.py:428: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.\n",
      "  intermediate_output = tf.layers.dense(\n",
      "c:\\Users\\Luv_d\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\uf-2.5.0-py3.9.egg\\uf\\apps\\bert\\bert.py:438: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.\n",
      "  layer_output = tf.layers.dense(\n",
      "c:\\Users\\Luv_d\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\uf-2.5.0-py3.9.egg\\uf\\apps\\bert\\bert.py:107: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.\n",
      "  self.pooled_output = tf.layers.dense(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Build graph with 305,626,374 parameters (among which 102,269,186 are trainable)\n",
      "INFO:tensorflow:Running local_init_op\n",
      "INFO:tensorflow:Done running local_init_op\n",
      "INFO:tensorflow:Running training on 2 samples (step 0 -> 20)\n",
      "INFO:tensorflow:step 1, accuracy 0.3846, loss 0.818061, 0.07 steps/sec, 0.13 examples/sec\n",
      "INFO:tensorflow:step 2, accuracy 0.2308, loss 0.862863, 0.21 steps/sec, 0.42 examples/sec\n",
      "INFO:tensorflow:step 3, accuracy 0.6923, loss 0.921003, 0.33 steps/sec, 0.65 examples/sec\n",
      "INFO:tensorflow:step 4, accuracy 0.6923, loss 0.698519, 0.27 steps/sec, 0.54 examples/sec\n",
      "INFO:tensorflow:step 5, accuracy 1.0000, loss 0.111205, 0.26 steps/sec, 0.52 examples/sec\n",
      "INFO:tensorflow:step 6, accuracy 0.7692, loss 0.361469, 0.31 steps/sec, 0.62 examples/sec\n",
      "INFO:tensorflow:step 7, accuracy 0.8462, loss 0.243153, 0.33 steps/sec, 0.66 examples/sec\n",
      "INFO:tensorflow:step 8, accuracy 1.0000, loss 0.023673, 0.34 steps/sec, 0.68 examples/sec\n",
      "INFO:tensorflow:step 9, accuracy 1.0000, loss 0.004654, 0.34 steps/sec, 0.67 examples/sec\n",
      "INFO:tensorflow:step 10, accuracy 1.0000, loss 0.026563, 0.36 steps/sec, 0.72 examples/sec\n",
      "INFO:tensorflow:step 11, accuracy 1.0000, loss 0.003843, 0.39 steps/sec, 0.78 examples/sec\n",
      "INFO:tensorflow:step 12, accuracy 1.0000, loss 0.001234, 0.34 steps/sec, 0.68 examples/sec\n",
      "INFO:tensorflow:step 13, accuracy 1.0000, loss 0.000638, 0.43 steps/sec, 0.85 examples/sec\n",
      "INFO:tensorflow:step 14, accuracy 1.0000, loss 0.000225, 0.49 steps/sec, 0.99 examples/sec\n",
      "INFO:tensorflow:step 15, accuracy 1.0000, loss 0.000090, 0.50 steps/sec, 1.00 examples/sec\n",
      "INFO:tensorflow:step 16, accuracy 1.0000, loss 0.000078, 0.62 steps/sec, 1.24 examples/sec\n",
      "INFO:tensorflow:step 17, accuracy 1.0000, loss 0.000050, 0.56 steps/sec, 1.11 examples/sec\n",
      "INFO:tensorflow:step 18, accuracy 1.0000, loss 0.000066, 0.50 steps/sec, 1.00 examples/sec\n",
      "INFO:tensorflow:step 19, accuracy 1.0000, loss 0.000050, 0.58 steps/sec, 1.15 examples/sec\n",
      "INFO:tensorflow:step 20, accuracy 1.0000, loss 0.000065, 0.52 steps/sec, 1.05 examples/sec\n"
     ]
    }
   ],
   "source": [
    "model.fit(X_tokenized=X_tokenized, y=y, total_steps=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fallen-hundred",
   "metadata": {},
   "source": [
    "# 推理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "opposite-pantyhose",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Running inference on 2 samples\n",
      "INFO:tensorflow:process 100.0%, 0.81 examples/sec\n"
     ]
    },
    {
     "data": {
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       "         [9.97146785e-01, 2.85317306e-03],\n",
       "         [9.20480967e-01, 7.95190483e-02],\n",
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       "         [9.93564069e-01, 6.43593725e-03],\n",
       "         [9.99054730e-01, 9.45289386e-04],\n",
       "         [9.94963527e-01, 5.03648166e-03],\n",
       "         [9.99651909e-01, 3.48112750e-04],\n",
       "         [9.99404669e-01, 5.95345511e-04],\n",
       "         [9.93211389e-01, 6.78855553e-03],\n",
       "         [9.90409136e-01, 9.59090050e-03],\n",
       "         [9.98480260e-01, 1.51975651e-03],\n",
       "         [9.98511493e-01, 1.48847664e-03],\n",
       "         [9.98519242e-01, 1.48080289e-03],\n",
       "         [9.97487545e-01, 2.51239538e-03],\n",
       "         [8.75199258e-01, 1.24800794e-01],\n",
       "         [9.98905063e-01, 1.09491160e-03],\n",
       "         [9.91819382e-01, 8.18062946e-03],\n",
       "         [9.88370180e-01, 1.16298143e-02],\n",
       "         [9.96986210e-01, 3.01377289e-03],\n",
       "         [9.99479830e-01, 5.20166359e-04],\n",
       "         [9.97923315e-01, 2.07673968e-03],\n",
       "         [9.97304678e-01, 2.69526709e-03],\n",
       "         [9.95851636e-01, 4.14836686e-03],\n",
       "         [9.99739826e-01, 2.60192377e-04],\n",
       "         [9.99591887e-01, 4.08096821e-04],\n",
       "         [9.99213099e-01, 7.86847086e-04],\n",
       "         [9.95714128e-01, 4.28583473e-03],\n",
       "         [9.91098702e-01, 8.90130736e-03],\n",
       "         [9.96828973e-01, 3.17100622e-03],\n",
       "         [9.91474867e-01, 8.52519739e-03],\n",
       "         [9.98737872e-01, 1.26210810e-03],\n",
       "         [9.79015231e-01, 2.09846906e-02],\n",
       "         [9.83045101e-01, 1.69549398e-02],\n",
       "         [9.92425442e-01, 7.57451635e-03],\n",
       "         [9.97715116e-01, 2.28488934e-03],\n",
       "         [9.84786093e-01, 1.52138472e-02],\n",
       "         [9.94420826e-01, 5.57921641e-03],\n",
       "         [9.98063862e-01, 1.93614827e-03],\n",
       "         [9.99502063e-01, 4.97918460e-04],\n",
       "         [9.99075055e-01, 9.24880675e-04],\n",
       "         [9.97004211e-01, 2.99584563e-03],\n",
       "         [9.96096194e-01, 3.90378665e-03],\n",
       "         [9.90615904e-01, 9.38403979e-03],\n",
       "         [9.99698520e-01, 3.01465887e-04],\n",
       "         [9.98481929e-01, 1.51807733e-03],\n",
       "         [9.98170257e-01, 1.82974746e-03],\n",
       "         [9.98496294e-01, 1.50375336e-03],\n",
       "         [9.98969078e-01, 1.03091612e-03],\n",
       "         [9.98556197e-01, 1.44375034e-03],\n",
       "         [9.99707162e-01, 2.92789686e-04],\n",
       "         [9.97975886e-01, 2.02417723e-03],\n",
       "         [9.97478306e-01, 2.52169417e-03],\n",
       "         [9.98968363e-01, 1.03164115e-03],\n",
       "         [9.88916397e-01, 1.10836001e-02],\n",
       "         [9.90646124e-01, 9.35391989e-03],\n",
       "         [9.80012238e-01, 1.99877247e-02],\n",
       "         [9.98353481e-01, 1.64658588e-03],\n",
       "         [9.99444902e-01, 5.55126637e-04],\n",
       "         [9.98048663e-01, 1.95130112e-03],\n",
       "         [9.96851861e-01, 3.14819044e-03],\n",
       "         [9.92620945e-01, 7.37899123e-03],\n",
       "         [9.94030893e-01, 5.96906152e-03],\n",
       "         [9.95959103e-01, 4.04084753e-03],\n",
       "         [9.98370230e-01, 1.62978121e-03],\n",
       "         [9.99669552e-01, 3.30440555e-04],\n",
       "         [9.88988221e-01, 1.10117476e-02],\n",
       "         [9.98781979e-01, 1.21808203e-03],\n",
       "         [9.93286669e-01, 6.71330327e-03],\n",
       "         [9.98085976e-01, 1.91410014e-03],\n",
       "         [9.97733951e-01, 2.26600422e-03],\n",
       "         [9.97576535e-01, 2.42352369e-03],\n",
       "         [9.94557977e-01, 5.44208800e-03],\n",
       "         [9.97888744e-01, 2.11123028e-03],\n",
       "         [9.86376941e-01, 1.36230784e-02],\n",
       "         [9.89111006e-01, 1.08890124e-02],\n",
       "         [9.98386979e-01, 1.61298609e-03],\n",
       "         [9.97373104e-01, 2.62684468e-03],\n",
       "         [9.54500854e-01, 4.54990938e-02]]], dtype=float32)}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_tokenized=X_tokenized)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fundamental-poverty",
   "metadata": {},
   "source": [
    "# 评分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "accessory-lesbian",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Running scoring on 2 samples\n",
      "INFO:tensorflow:process 100.0%, 3.77 examples/sec\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 1.0, 'loss': 1.5555212e-05}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_tokenized=X_tokenized, y=y)"
   ]
  }
 ],
 "metadata": {
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   "language": "python",
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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