commit 45fab02c7db64ee4f92749aca91a4858f00c3e8e
parent aa9282e06910c6d7d1bc0f6b71780ef23bd5a24d
Author: MTRNord <mtrnord1@gmail.com>
Date: Sat, 10 Dec 2022 22:21:53 +0100
New bert model
Diffstat:
2 files changed, 237 insertions(+), 150 deletions(-)
diff --git a/bert.ipynb b/bert.ipynb
@@ -2,14 +2,51 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Model: \"model_1\"\n",
+ "env: TF_GPU_ALLOCATOR=cuda_malloc_async\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2022-12-10 21:21:09.925809: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n",
+ "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+ "2022-12-10 21:21:10.036521: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+ "2022-12-10 21:21:11.247528: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+ "2022-12-10 21:21:11.251948: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+ "2022-12-10 21:21:11.252191: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
+ "/home/marcel/.conda/envs/tf/lib/python3.9/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.data_structures has been moved to tensorflow.python.trackable.data_structures. The old module will be deleted in version 2.11.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2022-12-10 21:21:12.482467: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n",
+ "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+ "WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.data_structures has been moved to tensorflow.python.trackable.data_structures. The old module will be deleted in version 2.11.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"model\"\n",
"__________________________________________________________________________________________________\n",
" Layer (type) Output Shape Param # Connected to \n",
"==================================================================================================\n",
@@ -22,250 +59,289 @@
" 'input_mask': (Non \n",
" e, 128)} \n",
" \n",
- " BERT_encoder (KerasLayer) {'sequence_output': 13548801 ['preprocessing[0][0]', \n",
- " (None, 128, 256), 'preprocessing[0][1]', \n",
- " 'encoder_outputs': 'preprocessing[0][2]'] \n",
- " [(None, 128, 256), \n",
- " (None, 128, 256), \n",
- " (None, 128, 256), \n",
- " (None, 128, 256), \n",
- " (None, 128, 256), \n",
- " (None, 128, 256), \n",
- " (None, 128, 256), \n",
- " (None, 128, 256), \n",
- " (None, 128, 256), \n",
- " (None, 128, 256), \n",
- " (None, 128, 256), \n",
- " (None, 128, 256)], \n",
- " 'default': (None, \n",
- " 256), \n",
- " 'pooled_output': ( \n",
- " None, 256)} \n",
+ " BERT_encoder (KerasLayer) {'default': (None, 109482241 ['preprocessing[0][0]', \n",
+ " 768), 'preprocessing[0][1]', \n",
+ " 'pooled_output': ( 'preprocessing[0][2]'] \n",
+ " None, 768), \n",
+ " 'sequence_output': \n",
+ " (None, 128, 768), \n",
+ " 'encoder_outputs': \n",
+ " [(None, 128, 768), \n",
+ " (None, 128, 768), \n",
+ " (None, 128, 768), \n",
+ " (None, 128, 768), \n",
+ " (None, 128, 768), \n",
+ " (None, 128, 768), \n",
+ " (None, 128, 768), \n",
+ " (None, 128, 768), \n",
+ " (None, 128, 768), \n",
+ " (None, 128, 768), \n",
+ " (None, 128, 768), \n",
+ " (None, 128, 768)]} \n",
" \n",
- " dropout_1 (Dropout) (None, 256) 0 ['BERT_encoder[0][13]'] \n",
+ " dropout (Dropout) (None, 768) 0 ['BERT_encoder[0][13]'] \n",
" \n",
- " classifier (Dense) (None, 1) 257 ['dropout_1[0][0]'] \n",
+ " classifier (Dense) (None, 1) 769 ['dropout[0][0]'] \n",
" \n",
"==================================================================================================\n",
- "Total params: 13,549,058\n",
- "Trainable params: 13,549,057\n",
+ "Total params: 109,483,010\n",
+ "Trainable params: 109,483,009\n",
"Non-trainable params: 1\n",
"__________________________________________________________________________________________________\n",
- "Training model with https://tfhub.dev/google/electra_small/2\n",
- "Epoch 1/34\n",
- "42/42 [==============================] - 37s 255ms/step - loss: 0.5633 - binary_accuracy: 0.7036 - val_loss: 0.3529 - val_binary_accuracy: 0.8482\n",
- "Epoch 2/34\n",
- "42/42 [==============================] - 10s 237ms/step - loss: 0.2114 - binary_accuracy: 0.9289 - val_loss: 0.0842 - val_binary_accuracy: 0.9773\n",
- "Epoch 3/34\n",
- "42/42 [==============================] - 10s 237ms/step - loss: 0.0651 - binary_accuracy: 0.9813 - val_loss: 0.0626 - val_binary_accuracy: 0.9791\n",
- "Epoch 4/34\n",
- "42/42 [==============================] - 10s 237ms/step - loss: 0.0376 - binary_accuracy: 0.9903 - val_loss: 0.0551 - val_binary_accuracy: 0.9843\n",
- "Epoch 5/34\n",
- "42/42 [==============================] - 10s 236ms/step - loss: 0.0199 - binary_accuracy: 0.9948 - val_loss: 0.0757 - val_binary_accuracy: 0.9773\n",
- "Epoch 6/34\n",
- "42/42 [==============================] - 10s 237ms/step - loss: 0.0143 - binary_accuracy: 0.9970 - val_loss: 0.0709 - val_binary_accuracy: 0.9773\n",
- "Epoch 7/34\n",
- "42/42 [==============================] - 10s 235ms/step - loss: 0.0080 - binary_accuracy: 0.9978 - val_loss: 0.0553 - val_binary_accuracy: 0.9843\n",
- "Epoch 8/34\n",
- "42/42 [==============================] - 10s 239ms/step - loss: 0.0042 - binary_accuracy: 0.9993 - val_loss: 0.0608 - val_binary_accuracy: 0.9860\n",
- "Epoch 9/34\n",
- "42/42 [==============================] - 10s 244ms/step - loss: 0.0027 - binary_accuracy: 1.0000 - val_loss: 0.0617 - val_binary_accuracy: 0.9860\n",
- "Epoch 10/34\n",
- "42/42 [==============================] - 10s 239ms/step - loss: 0.0018 - binary_accuracy: 1.0000 - val_loss: 0.0679 - val_binary_accuracy: 0.9843\n",
- "Epoch 11/34\n",
- "42/42 [==============================] - 10s 239ms/step - loss: 0.0014 - binary_accuracy: 1.0000 - val_loss: 0.0721 - val_binary_accuracy: 0.9843\n",
- "Epoch 12/34\n",
- "42/42 [==============================] - 10s 236ms/step - loss: 0.0011 - binary_accuracy: 1.0000 - val_loss: 0.0772 - val_binary_accuracy: 0.9843\n",
- "Epoch 13/34\n",
- "42/42 [==============================] - 10s 235ms/step - loss: 0.0026 - binary_accuracy: 0.9993 - val_loss: 0.0946 - val_binary_accuracy: 0.9756\n",
- "Epoch 14/34\n",
- "42/42 [==============================] - 10s 236ms/step - loss: 0.0048 - binary_accuracy: 0.9985 - val_loss: 0.0864 - val_binary_accuracy: 0.9791\n",
- "Epoch 15/34\n",
- "42/42 [==============================] - 10s 237ms/step - loss: 0.0021 - binary_accuracy: 1.0000 - val_loss: 0.0864 - val_binary_accuracy: 0.9791\n",
- "Epoch 16/34\n",
- "42/42 [==============================] - 10s 238ms/step - loss: 0.0011 - binary_accuracy: 1.0000 - val_loss: 0.0792 - val_binary_accuracy: 0.9825\n",
- "Epoch 17/34\n",
- "42/42 [==============================] - 10s 232ms/step - loss: 8.2108e-04 - binary_accuracy: 1.0000 - val_loss: 0.0830 - val_binary_accuracy: 0.9808\n",
- "Epoch 18/34\n",
- "42/42 [==============================] - 10s 235ms/step - loss: 7.7923e-04 - binary_accuracy: 1.0000 - val_loss: 0.0829 - val_binary_accuracy: 0.9825\n",
- "Epoch 19/34\n",
- "42/42 [==============================] - 10s 237ms/step - loss: 6.6255e-04 - binary_accuracy: 1.0000 - val_loss: 0.0806 - val_binary_accuracy: 0.9843\n",
- "Epoch 20/34\n",
- "42/42 [==============================] - 10s 238ms/step - loss: 6.3440e-04 - binary_accuracy: 1.0000 - val_loss: 0.0848 - val_binary_accuracy: 0.9825\n",
- "Epoch 21/34\n",
- "42/42 [==============================] - 10s 237ms/step - loss: 5.3386e-04 - binary_accuracy: 1.0000 - val_loss: 0.0854 - val_binary_accuracy: 0.9825\n",
- "Epoch 22/34\n",
- "42/42 [==============================] - 10s 236ms/step - loss: 5.6435e-04 - binary_accuracy: 1.0000 - val_loss: 0.0874 - val_binary_accuracy: 0.9825\n",
- "Epoch 23/34\n",
- "42/42 [==============================] - 10s 235ms/step - loss: 5.6810e-04 - binary_accuracy: 1.0000 - val_loss: 0.0868 - val_binary_accuracy: 0.9825\n",
- "Epoch 24/34\n",
- "42/42 [==============================] - 10s 235ms/step - loss: 4.5731e-04 - binary_accuracy: 1.0000 - val_loss: 0.0867 - val_binary_accuracy: 0.9825\n",
- "Epoch 25/34\n",
- "42/42 [==============================] - 10s 237ms/step - loss: 4.4671e-04 - binary_accuracy: 1.0000 - val_loss: 0.0877 - val_binary_accuracy: 0.9825\n",
- "Epoch 26/34\n",
- "42/42 [==============================] - 10s 237ms/step - loss: 4.9401e-04 - binary_accuracy: 1.0000 - val_loss: 0.0900 - val_binary_accuracy: 0.9825\n",
- "Epoch 27/34\n",
- "42/42 [==============================] - 10s 238ms/step - loss: 3.6857e-04 - binary_accuracy: 1.0000 - val_loss: 0.0903 - val_binary_accuracy: 0.9825\n",
- "Epoch 28/34\n",
- "42/42 [==============================] - 10s 235ms/step - loss: 3.8106e-04 - binary_accuracy: 1.0000 - val_loss: 0.0905 - val_binary_accuracy: 0.9825\n",
- "Epoch 29/34\n",
- "42/42 [==============================] - 10s 236ms/step - loss: 3.7847e-04 - binary_accuracy: 1.0000 - val_loss: 0.0910 - val_binary_accuracy: 0.9825\n",
- "Epoch 30/34\n",
- "42/42 [==============================] - 10s 236ms/step - loss: 3.7153e-04 - binary_accuracy: 1.0000 - val_loss: 0.0911 - val_binary_accuracy: 0.9825\n",
- "Epoch 31/34\n",
- "42/42 [==============================] - 10s 237ms/step - loss: 3.4990e-04 - binary_accuracy: 1.0000 - val_loss: 0.0913 - val_binary_accuracy: 0.9825\n",
- "Epoch 32/34\n",
- "42/42 [==============================] - 10s 236ms/step - loss: 3.6358e-04 - binary_accuracy: 1.0000 - val_loss: 0.0911 - val_binary_accuracy: 0.9825\n",
- "Epoch 33/34\n",
- "42/42 [==============================] - 10s 236ms/step - loss: 3.5723e-04 - binary_accuracy: 1.0000 - val_loss: 0.0910 - val_binary_accuracy: 0.9825\n",
- "Epoch 34/34\n",
- "42/42 [==============================] - 10s 234ms/step - loss: 3.7230e-04 - binary_accuracy: 1.0000 - val_loss: 0.0915 - val_binary_accuracy: 0.9825\n",
- "18/18 [==============================] - 1s 64ms/step - loss: 0.0915 - binary_accuracy: 0.9825\n",
- "Loss: 0.09151919186115265\n",
- "Accuracy: 0.9825479984283447\n"
+ "Training model with https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-12_H-768_A-12/2\n",
+ "Epoch 1/10\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2022-12-10 21:21:53.199015: I tensorflow/compiler/xla/service/service.cc:173] XLA service 0x424858d0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
+ "2022-12-10 21:21:53.199320: I tensorflow/compiler/xla/service/service.cc:181] StreamExecutor device (0): Host, Default Version\n",
+ "2022-12-10 21:21:53.212952: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
+ "2022-12-10 21:21:53.276686: I tensorflow/compiler/jit/xla_compilation_cache.cc:476] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "42/42 [==============================] - ETA: 0s - loss: 0.3651 - binary_accuracy: 0.8204"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2022-12-10 21:27:46.480163: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 5625815040 exceeds 10% of free system memory.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "42/42 [==============================] - 378s 9s/step - loss: 0.3651 - binary_accuracy: 0.8204 - val_loss: 0.0910 - val_binary_accuracy: 0.9738\n",
+ "Epoch 2/10\n",
+ "42/42 [==============================] - ETA: 0s - loss: 0.0524 - binary_accuracy: 0.9850"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2022-12-10 21:33:39.322284: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 5625815040 exceeds 10% of free system memory.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "42/42 [==============================] - 353s 8s/step - loss: 0.0524 - binary_accuracy: 0.9850 - val_loss: 0.0860 - val_binary_accuracy: 0.9843\n",
+ "Epoch 3/10\n",
+ "42/42 [==============================] - ETA: 0s - loss: 0.0194 - binary_accuracy: 0.9955"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2022-12-10 21:39:28.636886: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 5625815040 exceeds 10% of free system memory.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "42/42 [==============================] - 348s 8s/step - loss: 0.0194 - binary_accuracy: 0.9955 - val_loss: 0.0950 - val_binary_accuracy: 0.9791\n",
+ "Epoch 4/10\n",
+ "42/42 [==============================] - ETA: 0s - loss: 0.0062 - binary_accuracy: 0.9985"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2022-12-10 21:45:18.897732: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 5625815040 exceeds 10% of free system memory.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "42/42 [==============================] - 350s 8s/step - loss: 0.0062 - binary_accuracy: 0.9985 - val_loss: 0.0881 - val_binary_accuracy: 0.9860\n",
+ "Epoch 5/10\n",
+ "42/42 [==============================] - ETA: 0s - loss: 0.0037 - binary_accuracy: 0.9993"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "WARNING:absl:Found untraced functions such as restored_function_body, restored_function_body, restored_function_body, restored_function_body, restored_function_body while saving (showing 5 of 360). These functions will not be directly callable after loading.\n"
+ "2022-12-10 21:51:03.660678: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 5625815040 exceeds 10% of free system memory.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "INFO:tensorflow:Assets written to: ./bert_models/1670690385.641988/assets\n"
+ "42/42 [==============================] - 345s 8s/step - loss: 0.0037 - binary_accuracy: 0.9993 - val_loss: 0.0873 - val_binary_accuracy: 0.9843\n",
+ "Epoch 6/10\n",
+ "42/42 [==============================] - 349s 8s/step - loss: 0.0018 - binary_accuracy: 0.9993 - val_loss: 0.0896 - val_binary_accuracy: 0.9860\n",
+ "Epoch 7/10\n",
+ "42/42 [==============================] - 350s 8s/step - loss: 0.0017 - binary_accuracy: 0.9993 - val_loss: 0.0904 - val_binary_accuracy: 0.9825\n",
+ "Epoch 8/10\n",
+ "42/42 [==============================] - 347s 8s/step - loss: 9.7578e-04 - binary_accuracy: 1.0000 - val_loss: 0.0922 - val_binary_accuracy: 0.9843\n",
+ "Epoch 9/10\n",
+ "42/42 [==============================] - 350s 8s/step - loss: 7.7726e-04 - binary_accuracy: 1.0000 - val_loss: 0.0928 - val_binary_accuracy: 0.9843\n",
+ "Epoch 10/10\n",
+ "42/42 [==============================] - 349s 8s/step - loss: 6.2757e-04 - binary_accuracy: 1.0000 - val_loss: 0.0931 - val_binary_accuracy: 0.9843\n",
+ "18/18 [==============================] - 41s 2s/step - loss: 0.0931 - binary_accuracy: 0.9843\n",
+ "Loss: 0.09307406097650528\n",
+ "Accuracy: 0.9842932224273682\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "INFO:tensorflow:Assets written to: ./bert_models/1670690385.641988/assets\n"
+ "WARNING:absl:Found untraced functions such as restored_function_body, restored_function_body, restored_function_body, restored_function_body, restored_function_body while saving (showing 5 of 366). These functions will not be directly callable after loading.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
+ "INFO:tensorflow:Assets written to: ./bert_models/1670707256.1814783/assets\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: ./bert_models/1670707256.1814783/assets\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1/1 [==============================] - 2s 2s/step\n",
"Message: \"Greg, can you call me back once you get this?\"\n",
- "Likeliness of spam in percentage: 0.000258\n",
+ "Likeliness of spam in percentage: 0.00093\n",
"Vote by AI: Not Spam\n",
"Model predicted correctly\n",
"\n",
"\n",
"Message: \"Congrats on your new iPhone! Click here to claim your prize...\"\n",
- "Likeliness of spam in percentage: 0.858939\n",
- "Vote by AI: Spam\n",
- "Model predicted correctly\n",
+ "Likeliness of spam in percentage: 0.41126\n",
+ "Vote by AI: Not Spam\n",
+ "Model failed to predict correctly\n",
"\n",
"\n",
"Message: \"Really like that new photo of you\"\n",
- "Likeliness of spam in percentage: 0.007752\n",
+ "Likeliness of spam in percentage: 0.04146\n",
"Vote by AI: Not Spam\n",
"Model predicted correctly\n",
"\n",
"\n",
"Message: \"Did you hear the news today? Terrible what has happened...\"\n",
- "Likeliness of spam in percentage: 0.000284\n",
+ "Likeliness of spam in percentage: 0.00026\n",
"Vote by AI: Not Spam\n",
"Model predicted correctly\n",
"\n",
"\n",
"Message: \"Attend this free COVID webinar today: Book your session now...\"\n",
- "Likeliness of spam in percentage: 0.998953\n",
+ "Likeliness of spam in percentage: 0.99068\n",
"Vote by AI: Spam\n",
"Model predicted correctly\n",
"\n",
"\n",
"Message: \"Are you coming to the party tonight?\"\n",
- "Likeliness of spam in percentage: 0.007384\n",
+ "Likeliness of spam in percentage: 0.02573\n",
"Vote by AI: Not Spam\n",
"Model predicted correctly\n",
"\n",
"\n",
"Message: \"Your parcel has gone missing\"\n",
- "Likeliness of spam in percentage: 0.003767\n",
+ "Likeliness of spam in percentage: 0.00568\n",
"Vote by AI: Not Spam\n",
"Model predicted correctly\n",
"\n",
"\n",
"Message: \"Do not forget to bring friends!\"\n",
- "Likeliness of spam in percentage: 0.000505\n",
+ "Likeliness of spam in percentage: 0.01160\n",
"Vote by AI: Not Spam\n",
"Model predicted correctly\n",
"\n",
"\n",
"Message: \"You have won a million dollars! Fill out your bank details here...\"\n",
- "Likeliness of spam in percentage: 0.947860\n",
- "Vote by AI: Spam\n",
- "Model predicted correctly\n",
+ "Likeliness of spam in percentage: 0.00105\n",
+ "Vote by AI: Not Spam\n",
+ "Model failed to predict correctly\n",
"\n",
"\n",
"Message: \"Looking forward to seeing you again\"\n",
- "Likeliness of spam in percentage: 0.152917\n",
+ "Likeliness of spam in percentage: 0.11693\n",
"Vote by AI: Not Spam\n",
"Model predicted correctly\n",
"\n",
"\n",
"Message: \"oh wow https://github.com/MGCodesandStats/tensorflow-nlp/blob/master/spam%20detection%20tensorflow%20v2.ipynb works really good on spam detection. Guess I go with that as the base model then lol :D\"\n",
- "Likeliness of spam in percentage: 0.999562\n",
- "Vote by AI: Spam\n",
- "Model failed to predict correctly\n",
+ "Likeliness of spam in percentage: 0.53543\n",
+ "Vote by AI: Not Spam\n",
+ "Model predicted correctly\n",
"\n",
"\n",
"Message: \"ayo\"\n",
- "Likeliness of spam in percentage: 0.027977\n",
+ "Likeliness of spam in percentage: 0.43455\n",
"Vote by AI: Not Spam\n",
"Model predicted correctly\n",
"\n",
"\n",
"Message: \"Almost all my spam is coming to my non-gmail address actually\"\n",
- "Likeliness of spam in percentage: 0.057861\n",
+ "Likeliness of spam in percentage: 0.04262\n",
"Vote by AI: Not Spam\n",
"Model predicted correctly\n",
"\n",
"\n",
"Message: \"Oh neat I think I found the sizing sweetspot for my data :D\"\n",
- "Likeliness of spam in percentage: 0.000136\n",
+ "Likeliness of spam in percentage: 0.00090\n",
"Vote by AI: Not Spam\n",
"Model predicted correctly\n",
"\n",
"\n",
"Message: \"would never click on buttons in gmail :D always expecting there to be a bug in gmail that allows js to grab your google credentials :D XSS via email lol. I am too scared for touching spam in gmail\"\n",
- "Likeliness of spam in percentage: 0.212154\n",
+ "Likeliness of spam in percentage: 0.12625\n",
"Vote by AI: Not Spam\n",
"Model predicted correctly\n",
"\n",
"\n",
"Message: \"back to cacophony \"\n",
- "Likeliness of spam in percentage: 0.066419\n",
+ "Likeliness of spam in percentage: 0.16637\n",
"Vote by AI: Not Spam\n",
"Model predicted correctly\n",
"\n",
"\n",
"Message: \"Room version 11 when\"\n",
- "Likeliness of spam in percentage: 0.944931\n",
- "Vote by AI: Spam\n",
- "Model failed to predict correctly\n",
+ "Likeliness of spam in percentage: 0.38619\n",
+ "Vote by AI: Not Spam\n",
+ "Model predicted correctly\n",
"\n",
"\n",
"Message: \"skip 11 and go straight to 12\"\n",
- "Likeliness of spam in percentage: 0.006444\n",
+ "Likeliness of spam in percentage: 0.15472\n",
"Vote by AI: Not Spam\n",
"Model predicted correctly\n",
"\n",
"\n",
"Message: \"100 events should clear out any events that might be causing a request to fail lol\"\n",
- "Likeliness of spam in percentage: 0.019123\n",
+ "Likeliness of spam in percentage: 0.00092\n",
"Vote by AI: Not Spam\n",
"Model predicted correctly\n",
"\n",
"\n",
"Message: \"I'll help anyone interested on how to invest and earn $30k, $50k, $100k, $200k or more in just 72hours from the crypto market.But you will have to pay me my commission! when you receive your profit! if interested send me a direct message let's get started or via WhatsApp +1 (605) 953‑6801\"\n",
- "Likeliness of spam in percentage: 0.999778\n",
+ "Likeliness of spam in percentage: 0.99976\n",
"Vote by AI: Spam\n",
"Model predicted correctly\n",
"\n",
@@ -276,7 +352,7 @@
},
{
"data": {
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",
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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -286,7 +362,7 @@
},
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
"<Figure size 1000x600 with 2 Axes>"
]
@@ -297,10 +373,13 @@
],
"source": [
"!pip3 install --quiet tensorflow-text numpy pandas tf-models-official\n",
+ "%env TF_GPU_ALLOCATOR=cuda_malloc_async\n",
+ "%load_ext tensorboard\n",
"\n",
"import csv\n",
"import numpy as np\n",
"import tensorflow as tf\n",
+ "tf.config.set_visible_devices([], 'GPU')\n",
"import tensorflow_hub as hub\n",
"import pandas as pd\n",
"import tensorflow as tf\n",
@@ -309,6 +388,7 @@
"#from official.nlp import optimization # to create AdamW optimizer\n",
"import tensorflow_text as text # needed even if unused\n",
"import time\n",
+ "import datetime\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
@@ -363,7 +443,9 @@
"# Load the BERT encoder and preprocessing models\n",
"# Alternative https://tfhub.dev/google/electra_small/2\n",
"tfhub_handle_preprocess = 'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3'\n",
- "tfhub_handle_encoder = 'https://tfhub.dev/google/electra_small/2'\n",
+ "#tfhub_handle_encoder = 'https://tfhub.dev/google/electra_small/2'\n",
+ "#tfhub_handle_encoder = 'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-4_H-512_A-8/2'\n",
+ "tfhub_handle_encoder = 'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-12_H-768_A-12/2'\n",
"\n",
"def build_classifier_model():\n",
" text_input = tf.keras.layers.Input(shape=(), dtype=tf.string, name='text')\n",
@@ -373,7 +455,8 @@
" outputs = encoder(encoder_inputs)\n",
" net = outputs['pooled_output']\n",
" net = tf.keras.layers.Dropout(0.1)(net)\n",
- " net = tf.keras.layers.Dense(1, activation=None, name='classifier')(net)\n",
+ " #net = tf.keras.layers.Dense(1, activation=None, name='classifier')(net)\n",
+ " net = tf.keras.layers.Dense(1, activation='sigmoid', name='classifier')(net)\n",
" return tf.keras.Model(text_input, net)\n",
"\n",
"\n",
@@ -383,10 +466,11 @@
"# print(tf.sigmoid(bert_raw_result))\n",
"tf.keras.utils.plot_model(classifier_model, show_dtype=True)\n",
"\n",
- "loss = tf.keras.losses.BinaryCrossentropy(from_logits=True)\n",
+ "#loss = tf.keras.losses.BinaryCrossentropy(from_logits=True)\n",
+ "loss = tf.keras.losses.BinaryCrossentropy(from_logits=False)\n",
"metrics = tf.metrics.BinaryAccuracy()\n",
"\n",
- "epochs = 34\n",
+ "epochs = 10\n",
"steps_per_epoch = tf.data.experimental.cardinality(train_ds).numpy()\n",
"num_train_steps = steps_per_epoch * epochs\n",
"num_warmup_steps = int(0.1*num_train_steps)\n",
@@ -422,9 +506,11 @@
" metrics=metrics)\n",
"\n",
"print(f'Training model with {tfhub_handle_encoder}')\n",
+ "log_dir = \"logs/fit/\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
+ "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n",
"history = classifier_model.fit(x=train_ds,\n",
" validation_data=val_ds,\n",
- " epochs=epochs)\n",
+ " epochs=epochs,callbacks=[tensorboard_callback])\n",
"\n",
"loss, accuracy = classifier_model.evaluate(test_ds)\n",
"\n",
@@ -517,14 +603,15 @@
" # print(text_messages)\n",
"\n",
" # Create the sequences\n",
- " results = tf.sigmoid(model(tf.constant(text_messages)))\n",
+ " #results = tf.sigmoid(model(tf.constant(text_messages)))\n",
+ " results = model.predict(tf.constant(text_messages))\n",
"\n",
" # The closer the class is to 1, the more likely that the message is spam\n",
" correct = 0\n",
" expected = len(spam_no_spam)\n",
" for x in range(len(text_messages)):\n",
" print(f'Message: \"{text_messages[x]}\"')\n",
- " print(f\"Likeliness of spam in percentage: {results[x][0]:.6f}\")\n",
+ " print(f\"Likeliness of spam in percentage: {results[x][0]:.5f}\")\n",
" spam = results[x][0] >= 0.8\n",
" if spam:\n",
" print(\"Vote by AI: Spam\")\n",
diff --git a/dataset_analysis.ipynb b/dataset_analysis.ipynb
@@ -9,7 +9,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@@ -26,7 +26,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 2,
"metadata": {},
"outputs": [
{
@@ -35,13 +35,13 @@
"<AxesSubplot: xlabel='label', ylabel='count'>"
]
},
- "execution_count": 6,
+ "execution_count": 2,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -56,7 +56,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 3,
"metadata": {},
"outputs": [
{
@@ -65,13 +65,13 @@
"<AxesSubplot: ylabel='count'>"
]
},
- "execution_count": 7,
+ "execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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",
+ "image/png": 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AgPOqRT+6+OWXX2r8+PGtHQsAAMB516JkaPz48frlL3/Z2rEAAACcdy36mOyyyy7TE088oa1bt+qaa65RbGxsQP1jjz3WKsEBTjfszX+WJL0zalmEIwGA6NWiZOill15SYmKiNm/erM2bNwfUuVwukiEAAHDBaFEytGfPntaOAwAAICJaNGYIAAAgWrSoZ2jSpElN1i9ZsqRFwQAAAJxvLUqGvvzyy4C/a2trtXPnTpWXl4f8B64AAADtVYuSoZKSkkbT6urq9Mgjj+jSSy8956AAAADOl1YbM+R2u1VYWKh58+a1VpMAAABtrlUHUP/5z3/WyZMnW7NJAACANtWij8kKCwsD/jbG6ODBg3r77bc1ceLEVgkMAADgfGhRMvTxxx8H/O12u5Wenq5nn332jN80AwAAaE9alAy99957rR0HAABARLQoGbIcPnxYu3btkiRdccUVSk9Pb5WgAAAAzpcWDaA+duyYJk2apOzsbA0cOFADBw5UTk6OJk+erOPHj7d2jAAAAG2mRclQYWGhNm/erPXr16u8vFzl5eV68803tXnzZn3rW99q7RgBAADaTIs+Jlu9erVWrVql2267zZ42fPhw+Xw+3XvvvVq4cGFrxQcAANCmWtQzdPz4cWVmZjaanpGRwcdkAADggtKiZCgvL0+zZ89WVVWVPe3EiROaM2eO8vLyWi04AACAttaij8nmz5+voUOHqnPnzurTp48k6X/+53/k9Xr1y1/+slUDBAAAaEstSoauueYa7d69Wz//+c/1pz/9SZJ0//33a8KECfL5fK0aIAAAQFtqUTI0d+5cZWZmasqUKQHTlyxZosOHD2vmzJmtEhwAAEBba9GYoRdffFE9e/ZsNP2qq67SokWLzjkoAACA86VFyVBpaamys7MbTU9PT9fBgwfPOSgAAIDzpUXJUG5urn7zm980mv6b3/xGOTk55xwUAADA+dKiMUNTpkzRtGnTVFtbq0GDBkmSNm3apBkzZvAL1AAA4ILSomTo8ccf1xdffKFvfOMbqqmpkSTFx8dr5syZKioqatUAgWgzpWSoJKnGVf/3a6M3RDAaAECLkiGXy6Uf/vCHeuKJJ/THP/5RPp9PPXr0kNfrbe34AAAA2lSLkiFLYmKibrzxxtaKBQAA4Lxr0QBqAACAaEEyBAAAHI1kCAAAOBrJEAAAcDSSIQAA4GgkQwAAwNFIhgAAgKORDAEAAEeLaDK0cOFC9e7dW8nJyUpOTlZeXp7eeecdu76qqkoFBQVKS0tTYmKi8vPzVVZWFsGIAQBAtIloMtS5c2f94Ac/0I4dO/TRRx9p0KBBGjVqlP7whz9IkqZPn67169dr5cqV2rx5sw4cOKCxY8dGMmQAABBlzunfcZyru+++O+Dvp59+WgsXLtTWrVvVuXNnLV68WMuWLdOgQYMkSUuXLlWvXr20detW9e/fPxIhAwCAKNNuxgydOnVKy5cv17Fjx5SXl6cdO3aotrZWgwcPtufp2bOnunTpoi1btoRtp7q6WpWVlQEFAAAgnIgnQ59++qkSExPl9Xr18MMPq6SkRFdeeaVKS0sVFxen1NTUgPkzMzNVWloatr25c+cqJSXFLrm5uW28BUDzFKwZGukQAAAhRDwZuuKKK/TJJ59o27ZteuSRRzRx4kR99tlnLW6vqKhIFRUVdtm3b18rRgsAAKJNRMcMSVJcXJwuu+wySVLfvn21fft2/ehHP9J9992nmpoalZeXB/QOlZWVKSsrK2x7Xq9XXq+3rcMGAABRIuI9Q8Hq6upUXV2tvn37KjY2Vps2bbLrdu3apb179yovLy+CEQIAgGgS0Z6hoqIiDRs2TF26dNGRI0e0bNkyvf/++9q4caNSUlI0efJkFRYWqmPHjkpOTtbUqVOVl5fHN8kAAECriWgydOjQIT3wwAM6ePCgUlJS1Lt3b23cuFF33nmnJGnevHlyu93Kz89XdXW1hgwZogULFkQyZKBZvr2qfrD0f47bEOFIAABnEtFkaPHixU3Wx8fHq7i4WMXFxecpIgAA4DTtbswQAADA+UQyBAAAHI1kCAAAOBrJEC4Ir716l1579a42afu5ZUP03LIhbdI2AKD9IxkCAACORjIEAAAcjWQIAAA4GskQAABwNJIhAADgaCRDwDn6t5VD9W8rh0Y6DABAC5EMAQAARyMZAgAAjkYyBAAAHI1kCAAAOFpMpAMAotm01fUDq+fnb2jW/He/2TAQ2xXfViEBAILQMwQAAByNZAgAADgayRAAAHA0kiEAAOBoDKAGWmj2G9Zg58jGAQA4N/QMAQAARyMZAgAAjkYyBAAAHI1kCAAAOBoDqBExK5cO1fgHm/fLzG3hR8uGRGzdAID2g54hAADgaCRDAADA0UiGAACAo5EMAQAAR2MANXCWnlzRMPDadWH99PTwklmSpF+MeTLCkQBA+0LPEAAAcDSSIQAA4GgkQwAAwNFIhgAAgKORDMERin82RMU/4xenJWl4yRwNL5kT6TAAoN0gGQIAAI5GMgQAAByNZAgAADgayRAAAHA0foEaEbdi6VBJ0n0PbohwJKc983r9YOtTDT8yXfSVjc1a7jurhrZVSACANhLRnqG5c+fqxhtvVFJSkjIyMjR69Gjt2rUrYJ6qqioVFBQoLS1NiYmJys/PV1lZWYQiBgAA0SaiydDmzZtVUFCgrVu36t1331Vtba3uuusuHTt2zJ5n+vTpWr9+vVauXKnNmzfrwIEDGjt2bASjBgAA0SSiH5Nt2BD4scirr76qjIwM7dixQwMHDlRFRYUWL16sZcuWadCgQZKkpUuXqlevXtq6dav69+8fibABAEAUaVcDqCsqKiRJHTt2lCTt2LFDtbW1Gjx4sD1Pz5491aVLF23ZsiVkG9XV1aqsrAwoAAAA4bSbAdR1dXWaNm2abrnlFl199dWSpNLSUsXFxSk1NTVg3szMTJWWloZsZ+7cuZozh1/XRb0F/Oo0AOAM2k3PUEFBgXbu3Knly5efUztFRUWqqKiwy759+1opQgAAEI3aRc/Qo48+qrfeeksffPCBOnfubE/PyspSTU2NysvLA3qHysrKlJWVFbItr9crr9fb1iEDAIAoEdGeIWOMHn30UZWUlOi//uu/1L1794D6vn37KjY2Vps2bbKn7dq1S3v37lVeXt75DhcAAEShiPYMFRQUaNmyZXrzzTeVlJRkjwNKSUmRz+dTSkqKJk+erMLCQnXs2FHJycmaOnWq8vLy+CYZAABoFRHtGVq4cKEqKip02223KTs72y4rVqyw55k3b55Gjhyp/Px8DRw4UFlZWVqzZk0Eo0ZLrFk6VGuW8uvM4dy3ln0DAJES0Z4hY8wZ54mPj1dxcbGKi4vPQ0QAAMBp2s23yQAAACKBZAgAADgayRAAAHA0kqF2aM8Lo7XnhdGRDiNqvfBzfpUaAHAayRAAAHA0kiEAAOBoJEMAAMDRSIYAAICjkQw51G9eGqnfvDQy0mG0mUU/HaJFPz37gdL/+TqDqwHAaUiGAACAo5EMAQAARyMZAgAAjkYyBAAAHI1kCI2898oIvffKCEnSpobHd18ZrndfGd7kcm8tGaa3lgxrcp5VS4dq1dKhrRNokJd+OkQvtWDQNADA2UiGAACAo5EMAQAARyMZAgAAjkYyBAAAHC0m0gGg9e1YdLckqc5V//eNX19v121ppV+dfmdx/WDqYZN/obcXNwyadrVK0wF++mrggOilr90lSXpw4i/taa/8ZEibrd/y9Ir6dfzbfRvbbiUAgIigZwgAADgayRAAAHA0kiEAAOBoJEMAAMDRSIbOs/3FU7W/eGrIur3P52vv8/ln3ebvF95zrmG1Oz9/lV+SjqQRJc9oRMkzkQ4DAM4LkiEAAOBoJEMAAMDRSIYAAICjkQwBAABH4xeoLzB/LK4fLN2rYF2rt7355RH1T9rwl5wBAGhv6BkCAACORjIEAAAcjWQIAAA4GskQAABwNJIhtKm1S4Zp7ZJhkQ4DYQwveSrSIQBAxJEMAQAARyMZAgAAjkYyBAAAHI1kCAAAOBq/QB1h+174F0lS7tSfRjiS5tm4eHibtf36q0MkSfd/bWObrQMAgGAR7Rn64IMPdPfddysnJ0cul0tr164NqDfGaNasWcrOzpbP59PgwYO1e/fuyAQLAACiUkSToWPHjqlPnz4qLi4OWf/MM8/o+eef16JFi7Rt2zYlJCRoyJAhqqqqOs+RAgCAaBXRj8mGDRumYcNC/waNMUbz58/Xd7/7XY0aNUqS9JOf/ESZmZlau3atvvKVr5zPUAEAQJRqtwOo9+zZo9LSUg0ePNielpKSon79+mnLli1hl6uurlZlZWVAAQAACKfdDqAuLS2VJGVmZgZMz8zMtOtCmTt3rubMmdOqsRxa9LwkKePhx1q13TP5/Mf1PWKXPfrmObe17cWR9U9c59wUotjwkh9Iklzt9z4JAFpd1F3xioqKVFFRYZd9+/ZFOiQAANCOtdtkKCsrS5JUVlYWML2srMyuC8Xr9So5OTmgAAAAhNNuk6Hu3bsrKytLmzZtsqdVVlZq27ZtysvLi2BkAAAgmkR0zNDRo0f1+eef23/v2bNHn3zyiTp27KguXbpo2rRpeuqpp9SjRw91795dTzzxhHJycjR69OjIBQ0AAKJKRJOhjz76SLfffrv9d2FhoSRp4sSJevXVVzVjxgwdO3ZMDz30kMrLyzVgwABt2LBB8fHxkQpZhxbNU8bD05s178EFRZKk7G/MbcuQztmvXx6hW6e83WrtrVtS/3MJ90x6p9XaDGXJa3fVP3ExKjxajFy9RJL0Vv6kCEcCwEkimgzddtttMsaErXe5XHryySf15JNPnseoAACAk7TbMUMAAADnA8kQAABwNJIhAADgaO32F6gvRKUL/11Zjzyh0gWzIrL+jxfdHZH1wllGrJkvSXp77LSIxgEArYWeIQAA4GgkQwAAwNFIhgAAgKORDAEAAEcjGTpHZQvnqmxh+/6FaaA9GLH6RY1Y/WKkwwCARkiGAACAo5EMAQAARyMZAgAAjkYyBAAAHI1foG6hsoX/cU7L//3HD53V/LuKR+mKgjfPaZ3AhWzkqp9Ikt4a90CEIwEQbegZAgAAjkYyBAAAHI1kCAAAOBrJEAAAcDTHJ0OHFy3R4UVLgqa9qMOL+KVcAC03etW7Gr3q3UiHEfXeff2w3n39cKTDwAXO8ckQAABwNpIhAADgaCRDAADA0UiGAACAo/EL1A0OL3pZkpT+8JRWb/tA8bclSTkF/9lqbe5ccI8k6epvrGu1NoGmjFjzXMOz8PdQI9YUS5LeHltwzusbufpVSdJb+V8757bgLO//vH5A9W0T0iMcCS4U9AwBAABHIxkCAACORjIEAAAcjWQIAAA4mmMHUB9e+Gr9E1dEwwDavRFrrIH/Z3fvNGLNAr099hsasXpRw5SWvdhGrn7trJYfuep1vTXufr+/V0iS3hp33+lpK1fVTxs/7qxiuWfVW5KkdeNGSpJGrXpHkvTmuGFn1c6Foqhkv+aOubhZ875QUiZJmjoms9XWv27l/0mS7hnfqdXajLSDzxyUJGXPyD497T/2KvvxLpEKSWU/+o0kKfObt0QshkijZwgAADgayRAAAHA0kiEAAOBoJEMAAMDRHDWA+vDCn0iS0h95oHnzL1rQluEA7cbwku83PGvZ/dGINS+c1fIjVr+st/OnaMTqVyRJb+f/v7Na38hVPw1Y31vjJjRzuTf01rh7G02/e9UaSdL6cWMb1d2zqv5X3teNu8dv2tuSJFeI7R216pcNdWce8D1m9QeSpJL8gY3qxq7eIklak5+n/NXbtDq/X8g2xq3+RKvyr7X/Hr/6D5KklflXhV3vV9b8VZK0fGy3M8Z4JvNKSiVJMQ3bu3BN/UDqR8Zm6uU1hyRJU8ZmnFWbJav+T2PGtc2g6d++Vv/r1DdPPPOvU//Py/Xx95lydvHvmV+/T7pPywo7z8Fn9kuSsmc0b4D6+VD2/K8lSZmP3XrObR36cf1rJOPREefc1vlAzxAAAHA0kiEAAOBoJEMAAMDRSIYAAICjOWYA9f+9skJJPm+kwwCiyog1zzc8O7efch+xenFDKy38lepVyyRJb437Z79py88Y292rVjea5+5Va4NiaV5Mo1Zt0JvjhoasG71qk9aOu0OjV/9XQ4vWfejp+9Exq/87aL2N71XzV28PWH5Vfl+7bvzq3zc880iS7l39R0nSG/m9dN/q/23YFE/D8h57uUlr9kqSfK769cY3rP/ZMZ3teWaXHJAkxTXU/duYbD1TclAzxpz+FeWmLG0YSO1pWP6BselatvpwwDQronH5jQdOv/VG/S9Rj7y3k36x4v80/L5O2ri8fpq1lzYtq2/vjn8+PTD6g5/VT3PX1f894IHTdVtfbYjJ1P9944MZ2rH4kPpOztDHrxwKaFuSdr5Ypqu/nqnPFpUFtGk9Xv5opv78fGnAcnufq/+7S2GW/v6fpQHbGUrpf/y1/onrlCQp69uXqvTZ3cr6Vg+VPrsroE6qX3FW4dUqnVd/7F0N06y6zOnXq2z+Rw3TGjbU1VD3zX4q+9EWZX4zL2QsZc9vVuZj/6Sy59+vX8wV2HbG1ME69MIvG9o0gXWPnv5F9kPF6wPrCkbpUHGJMgrG6FDxmoCYMr4xTocWLFfGN76iwwuX6ciJ46F3VBu4IHqGiouL1a1bN8XHx6tfv3768MMPIx0SAACIEu0+GVqxYoUKCws1e/Zs/e53v1OfPn00ZMgQHTp0KNKhAQCAKNDuk6HnnntOU6ZM0YMPPqgrr7xSixYtUocOHbRkyZJIhwYAAKJAux4zVFNTox07dqioqMie5na7NXjwYG3ZsiXkMtXV1aqurrb/rqiokCQdqToh6zNLb2Wljpw4UT+D/TnoqcZ1DdNMw2N8ZaWOnKiyH+udlCT5GqZ1qKzUkRPVDcvV1j+a+sfKykodOVHTMC3wsbKyUkeqahvWWr++Uw2fw1ZWVuroiVr7sb5OYevsT3b95jlm1TVMs+ax6kLN01Sd8Vv++In6fWBCtG3V1TVsS3BdqHn8605YdQ1r9I/NrmsY6xC6rnG8J47Xt111/GTD/j5dVxVcZ22TK3Ae//We9Kurbqg72dCmf11NQ9s1DfPU+NXVNtTV2uutbQj89Llz8niNAhjjV2ed89bWePzarrYf67n96qrsR/86V9A8/nXBywfyrzthP6qh1TPVuYLm8a+zTrCQdQHLHbcfz1TncoVe3j+WUHWn4/Xf3mP2Y+NtCa4LtS8Dlwuuq388GmK9RwPqrGMfPE/9ROv4nj4/ao4fqV/K1fgYVB8/Yj9KUp1dl6Cq40fsx/q11tfFNLzYKit9OtFQZ73ReOzlvToetJw1lqayMk7Hjx+xHyXJbRrXHbPrTNA83tN11p6s86s7EW65eB09ccR+DF3nO11XF9y2T0eqAus8dl0Hu85TFzi+JqGyUkeqjtiP9Qeh/nXcobJSR6qO2o/+ddby/nXBY4Z8/svZY4aMX90x+7G+ri5sXXDb9e+DxwLatNZRX3c8oM3A5Y6HnCe4rv59WzLGar8NmXZs//79RpL57W9/GzD98ccfNzfddFPIZWbPnm0ajgiFQqFQKJQLvOzbt6/N8412/zHZ2SoqKlJFRYVdvvzyS33yySeSpM8++0yStG/fPu3bty9g2rnWtWXbkVpvNG4T62W90bTeaNwm1st6g+fJyclRW2vXH5N16tRJHo9HZWVlAdPLysqUlRX6f754vV55vYFfoXe763O+pKQkSVJycrJdZ00717q2bDtS643GbWK9rDea1huN28R6Wa//PBdffLH9Ht6W2nXPUFxcnPr27atNmzbZ0+rq6rRp0ybl5YX+bQQAAICz0a57hiSpsLBQEydO1A033KCbbrpJ8+fP17Fjx/Tggw9GOjQAABAF2n0ydN999+nw4cOaNWuWSktLde2112rDhg3KzMxsdhter1ezZ89WcnKyZs+ebX+M5j/tXOvasu1IrTcat4n1st5oWm80bhPrZb3+8wQPe2krLmPOx3fWAAAA2qd2PWYIAACgrZEMAQAARyMZAgAAjkYyBAAAHK3df5usubp166a//e1v52VdHo9Hp06dOud23G636urqzjwjAAAOYP2/QI/Ho5MnT4adb+jQoTp06JB27typzp07KzMzU59++qkSEhI0ceJEPf3004qJaX6KEzU9Q9u3b9fBgwd18OBBffrpp1qxYoUk6ZVXXtG9994rSbrmmmskSenp6fZyLpcrYIfFxsbK7XbbByQ2Nlbdu3eXJCUmJkpSQAJzww03SKr/gUi3263U1NRGX/uPj4+3n3s8Hvu5MUYXXXSROnbsKEny+XwBy8XExNi/vGn94vbpfyzZtHDzNfeXPC+77LKwdU191TE2NjZsXefOnSVJXbp0CTuPFXdT29m1a9dG06x916NHjzO23VTdpZde2qguIyMjYB3++zD4xRa8fxMTEwPOpXBSU1MbtdehQ4eAefzPo4svvjigLjk5OeDv3NzcsDGdSfA2+Z+zFmubrr/+enua9bpq6vwYOHDgWcUSbr2hWHH77ydLXFxcs5b3vxAHa+r4NSV4f4baP9bxD7XesxEbGxu2jeZeO4LjPZs3lLNlXVv9YwuOPy4uzt731jXY+qXitLS0s1pfqNfr+fh14+bwer1h/+2E2+22Yw++LpyJ//6MiYk54zkW7vVuHaO4uDh17NhRLpdLHo+n0XkV7r9DBBszZkyjaVdeeaXdXl1dnWJiYnTJJZfYMSxbtkydO3eW2+1W165dNWnSJN15553as2ePhg4dqo8//lgrVqzQunXr9J3vfKdZcdja/L+fRcg3v/lNc+mll5q6ujrTrVs3k5SUZD926NDBuFwuI8lkZGSYSy+91EgyPp/PSDIXXXSR6dy5s5FkOnbsaEaMGGG6du1qhg4daiQZj8dj/wO5Pn36GEnm1ltvNS6Xy9x2222mV69eAf9kLi4uzkgyqamp9jqsMnbsWOPxeOz1+ZcOHTqYr371q0aSyc/PD2hLkikoKLDnC1523LhxRpK56qqrwv7zu+A6t9ttP3/iiSfCLtdU3TPPPBO27uOPPzaSTElJSdh5pkyZErZu4sSJRpKZOnVqo7p169YZSea5554Lu/z9999/xrat/e1flixZYiSZ++67z0gy3bp1M5JMz549TUZGhn38/R/9j6G1TX379m20v2NjY40kc/311xtJJisry17HP/3TPxlJJicnx0gyvXv3tpcdMmSIkWRyc3ONJHP55ZcHtB0TE2P69+9vJJmbb745YHm3223GjBljJJkuXbo02t477rjDSLLPYys2/zJ+/PiAY9m5c2f7PLTOgfT09EbLWf98+ZprrrHjDJ7npptuCljef55+/foZSSY+Pr7RctY+uPbaaxvV3XPPPUaSuf322xu1ed111wWs138+/xJ8rEOVUaNGNZr26KOPGkmma9euAevzL5MmTTKS7OuSJDN37tyAbR0wYECj5b71rW8ZSeayyy6zpz3++ONh4wu+zgwfPtxIMv/yL/9iX9e++93vBpyv1vWxpcU6/0KVQYMGNZo2f/58I8lkZmba06zXSffu3QMe/a9ZVklLS2u0T6xiXTOtY+lfvF5v2Dit4+J/fKTT117/63JCQkLAPNY1PykpySQkJAS8f3To0MF4PB57+Xnz5oVc/+TJk82zzz5rJJl///d/Ny6XyyQnJ9v11n7wn+a/76xjGR8f32gbQs0fvG/9t6+kpMS+7tx5550By7lcLvPyyy8HtHfxxReHXM+VV15pUlJSAqZ98MEH5oorrrD/Hj9+vHG5XPZ5/vrrr9vXpS1bthhjjBkwYIDx+XwB7//r1q0z8fHxprKystk5Q1QmQ9XV1SYtLc08/fTTprq62vh8PpOammri4+NNamqq6dGjR8CJap281kkSGxtrn1Rer9d89atfNW6324wdO9ZIoS/g1gmfnZ1tbrnllpAHv3v37vaJZhUrIbEulOFe2NaJ6f8m8OKLLwbU+Z+8d999t5EavwE39QL3f5Fab8qhys9//vOwdU0lStab25VXXhl2nq1bt4ats96Ag19AUn0CK8lcffXVAdP999c777wT8GL3P3bWG1yotq39Yl34rL/Hjx9v73PrDSN4+Ztuusk+tqEu3OGK1+ttcv5Q52BwCd5O/2JtS6h5kpKSGp0P4faJtX+t/S+dThas5NG/WInlQw89FPIc9G871EXb2r+h6hITEwPi9y/W66hTp06N5rHOdf83X/9rRHP2vbUfbrjhhkZ1w4YNM9LpJDrUcbXmcbvd9rXEmmZtl/82W21Yyax/IjdhwoSwsVv7zzru1nLf+9737Hmsa4eVhDfnXG2qPjg5ONO+tLbT/3iczfJNFWt/Bd+USuHftM+2hEs2Ql1nrVisaeH21SWXXGLH3NQ2hNofycnJ9ntXampqo/rgeKxz60xJU7gyY8aMsPsj+CYm+CZu/vz5xuv12stYiZF1fs6ZM8c+N5577jljjDH9+/c3CQkJATnAu+++aySZ9957r9l5Q1QmQytWrDAej8fs37/frFixwrjdbnPXXXed0wFuTomJibEveMGlY8eOIaf7fL5Gdyn+F8um3hisEipxsRKynj17hl3eyvatJMV/vU0lQ1bPwdkW66Ju3Y0Gv3Dj4uLMww8/bJ/s/nWheies/XY2Sca5lFAXEqtYbzLBb8RXXXVVo30fKuEKdYdsFetNyf9CEi6WUD0mTZWmEp5zLffee2+jaaNHjzZS494G6zxtzeMZqmcqVLHWFxMT06w3V+t4+t8tB7flf3yCX9/p6emN3nj9b8SsaR06dDB9+vSx77iD15WWlmbcbrdxuVz2a8t6DBdzc6cHz5OYmNhoe/2TIOu8C+6ZCBWP9SZunXtNJe2h2vD5fPZ2+8dkTWvp+XK2idXZlrZ87zlTCdUDfKbif4MT6vi1xj5OSEgI2C+xsbHmnnvuafRpR3Z2tklNTTW33nqr6dWrl7nxxhvNjBkzjDHG7s1ftmyZOXnypPn73/9u35AtW7as2XlD+/iwtJUtXrxYw4YNU05OjhYvXqw+ffpo8+bN6tatm9LT0wM++wyWmJgoj8djfy7r8Xh0xx132PWxsbH2Z7f+4wvS09N18uRJ/fWvfw0Z0z/+8Y+Q00+cOKFDhw4FTPMfk2QN1DYhfijcGjdSWloadnuaYo1dsB79P6/u1atX2OUuuuiisHXW57uhPPvss5Kkffv2SZI9OM7anzU1NSopKQmos+zcubPRuKCKigpJ9WN6rPFI//qv/xp2/XfddZekxmNKrLFekjR+/HhJgWMYrrjiCknShAkTwrZtjTWyxkBY/vCHP9h11113naTA45SQkCBJ+vrXvx62beu4VFVV2dMGDx4sScrOzg6Y138e/7Fx4Vjnl//2Wue+NZbCGp/hr1OnTpICtyV4/MYbb7whKXCcxpYtWyRJf/nLXwKWsf5uypnGvFjrseI9fPhws5a1Xm8nT55scsCmxRq3Yp0z/u1b67bGuLjdbvs8dbvd6tChgyorKxtdD6zXoM/ns/d7XV2ddu7cKbfbLWOMvX1WvTHGHqtoxV1dXS23220fF+vcM8bYx8yfNT7EGk8pNb6OuFwuHT161N7enj17Sqp/vVqsa1Hv3r0Dlj169Ki93quuukrS6fPr1KlT6tq1q2prawNisbbTGgfnf73xeDw6ceKEOnToIGOMamtr5fF45PF4ZOpv7huNCwp1XbT2of94oeYce38xMTHyer32sbfaCh5z43K57HMmeJ3B45WaGrMWPBbHWtb/3A4e12bNs3fv3kbtnWl85pdfftloXklavnx5wLrcbneT1xr/6+3JkycD2jp27JiMMfbr5uTJk1q3bp19HevevbtiYmI0evRoVVRUaPv27Zo8eXJA+5deeqlycnL08MMPy+v16vLLL9fw4cMDtr9ZWrtXJtL++te/GrfbbdauXWs/v+iii4zL5TJpaWn2nWlCQoLJyMgwycnJdq9NVlaW8Xg8Jj4+3u5W93q9xufzhbwLtIrX6zVXXHGF8fl89l2/z+czw4cPt3uk7rzzTpOenm6Sk5Ptu8KLL77YdOrUqcnuYOtOK9Td8vr16wMyeP+suzkfkzVVrLEZoYrVexOqhOoNsIo1viq4l8z/I0JrW4J7PhISEux9GapY47mCxxP593BZPVrBbcfHxzca53W+S3CPUlJSkj3uy/roxb/Xxzovwn286r8vg3t/XC6X3WsSqifJOtea+vgj1N18U71brVFcLpfdo2R93BV8joRbNjs720ine6Ssed1ut/2xrcfjMb179z5jz5S1X/xfb9Y5ZbXrv8+t9kLFHBx7SkqKvaz1OrHWY50jVntxcXF2z5F1PJrqffD5fHbs1nEfOXKkkQJ7X6z1hRqL2NrFWpfL5bKvndZHIsEfT/vvgxEjRgRsl3+bTfUIBo81aupYe71eExsb22Tvaah1Wdc5q3Tp0iWg9y45Odler8/nO+M6EhMTTffu3Rt9pNReSmxsbMA1PXioQvBHztZ4o8WLFxup/nVhnZdr164N6DWeNWuWkWQOHz5sH69Dhw6ZLl262B+TzZ492/Tp08fU1dWZ/fv3m+PHj5vPPvvMSDIffvhhs3OHqOsZWrp0qTIyMjRixAj7eVVVlZKSkmSM0e9+9ztJ9Xclxu/OQqq/iwnuiTl58qRqamo0YMAAde3aVfHx8fZIeqn+rv2SSy5RTU2NTpw4Yd8t1dTUaPDgwdq2bZuSk5P15Zdf6h//+EfAXeHBgwcVGxurdevWKSkpSY8//riSkpLsO6IOHTro5ptvlqRGX8FPTU3VJ598Iun0XaX/nY2VjVdXV4fdV7fffrskNbqzkUJ/Y8uya9eusHVTpkwJW2fFl5KSIun0HdusWbMazRv8rYr58+fbcQb3Wt1+++2N7gSsuxbj16P2+eefB9RZUlJSNGTIEEn1X9f05/V6ddNNN0k63etj3cX069fPfm5946xv374By990002aOXOmpPpvSgSzjt3YsWMDph85ckR9+vSRJLvn0L/Xx+qBGzlyZKM2LdZ2Bv8MREJCgt3LZkL0OFqvB+tu1jpe/qxz1P9bWv369ZN0+ptsob71Ys1vfaPQ6tELji8UY4x9Xlu9Cf53mdbdqdWT5183evRoSae/zXbs2DFJ9a8ra78mJiaqtLRUdXV1dg+H/zGzehys3iz/10t5ebmk0+et/zfGrONg9ZBcffXVjbbNiqd///728Tpy5EjAPNZxsR67deumw4cPq7a21l4mOTlZsbGx+tGPftRoHZdffrmqq6vlcrnsbd66daukwF5Ej8ejuLg4uwfI6n20zoPg3h/p9PUiuGfY5/PZ84f6pqZ1juTm5to9aJs3b5Z0+vhZrymPx6OjR4/K4/Fo48aNdkwnTpywX3/S6fPWism/Rzm4Nzo3N1cZGRm67rrr7Ne55ZJLLgnYt6GE6lUJ7mH64osvZIyx2/H/SZXY2Fi5XC7V1dXJ5XLpo48+anR9io2N1RdffGFfv2JiYhQbG6vMzEy7t8i/J8k6B2NiYpSamhrQXmxsrP0ass7f4G/3Wueu/+vX/3oc3Ivt/00zSTpw4EDAayP4J2/2798vt9utX/3qV5LqX9fW+1R5ebk9v9fr1aRJkwLO58suu0z/+Mc/tHfvXuXl5TWKIycnRz6fT6+//rpyc3MDvu16Rq3dMxNJp06dMl26dDEzZ860n8+YMcMkJCSYxMREk5eXZ2faLpfLxMTEGI/HE3CXm5KSYmJiYgLusJKSkozL5TJXX321iYmJCbibsMY6BN8pezwek5qaajIyMgK+1WDdbajhbqhnz56mY8eOJiMjw+Tk5ATcjYcbZ6Qz3P00VZpzRyQ1fRfbVLG+wdScGKzSnEF9Pp/PvpsN9dm+NX9T43qaKlYPknXX4l/8v3lonT9S/eC/4B7D4G/rZGRkmMcee8xI9YMg/eP33w7rjtV/HwX3zPjPb/UkWOdfW479aW6xvjVn7ctQx8Tad1ZPnHXXGOp8DNXL0dR2WvunqZ7W4LpQg5Lj4uKMy+U648Dg5hb/bxA11cNsxRBqu63p/vvJ7XY32m8ul8v06dPnnHoRrG8cBbcf3CPm3xNnbV/wazM9Pd1+3VptWfMGb0u48yB4n/mPSwq3P5s77szj8TR67Z3pPGtJac54obS0tGYPWpcaDz62tjv4C0HBJXgcaUJCQsCxCR735nK5AnrfrPfDs9m2UCUuLs4+p6x1+Y+Pi4+PNz/+8Y/NwIED7WVmzpxp8vLyTF5entm9e7f5+OOPzde//nXTqVMn88Ybb5hVq1aZ2bNnm9jYWFNSUnJW+UNUJUMbN240ksyuXbvs51ZX3AMPPNCsb8k09wXU1IvN5XKdcVAghUKhUCjnWlpzYLb/N7naU/F4PHZSNmbMGHPw4EH7p0eCy7XXXmt+8YtfnHX+4DImRD85AACAQ0TdmCEAAICzQTIEAAAcjWQIAAA4GskQAABwNJIhAADgaCRDAADA0UiGAACAo5EMAQAARyMZAgAAjkYyBAAAHI1kCAAAOBrJEAAAcLT/D8xaIbFcqWQ/AAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -86,24 +86,24 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "we have 54508 words in our Dataframe\n",
- "the average word count in every scentence is 28\n"
+ "we have 54912 words in our Dataframe\n",
+ "the average word count in every sentence is 28\n"
]
},
{
"data": {
"text/plain": [
- "([20, 28, 32, 26, 26], 54508, 28)"
+ "([20, 28, 32, 26, 26], 54912, 28)"
]
},
- "execution_count": 8,
+ "execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@@ -113,7 +113,7 @@
"total_length = np.sum(text_words_lengths)\n",
"text_words_mean = int(np.mean(text_words_lengths))\n",
"print('we have ' + str(total_length) + ' words in our Dataframe')\n",
- "print('the average word count in every scentence is ' + str(text_words_mean))\n",
+ "print('the average word count in every sentence is ' + str(text_words_mean))\n",
"text_words_lengths[:5], total_length, text_words_mean"
]
}
