Upload bidirectional_lstm_imdb.ipynb
Browse files- bidirectional_lstm_imdb.ipynb +1546 -0
bidirectional_lstm_imdb.ipynb
ADDED
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|
1 |
+
{
|
2 |
+
"cells": [
|
3 |
+
{
|
4 |
+
"cell_type": "markdown",
|
5 |
+
"metadata": {
|
6 |
+
"id": "6AFd8gCCDCa6"
|
7 |
+
},
|
8 |
+
"source": [
|
9 |
+
"# Bidirectional LSTM on IMDB\n",
|
10 |
+
"\n",
|
11 |
+
"**Author:** [fchollet](https://twitter.com/fchollet)<br>\n",
|
12 |
+
"**Date created:** 2020/05/03<br>\n",
|
13 |
+
"**Last modified:** 2020/05/03<br>\n",
|
14 |
+
"**Description:** Train a 2-layer bidirectional LSTM on the IMDB movie review sentiment classification dataset."
|
15 |
+
]
|
16 |
+
},
|
17 |
+
{
|
18 |
+
"cell_type": "markdown",
|
19 |
+
"metadata": {
|
20 |
+
"id": "HtH19l5aDCa9"
|
21 |
+
},
|
22 |
+
"source": [
|
23 |
+
"## Setup"
|
24 |
+
]
|
25 |
+
},
|
26 |
+
{
|
27 |
+
"cell_type": "code",
|
28 |
+
"execution_count": 2,
|
29 |
+
"metadata": {
|
30 |
+
"id": "zubbfOxCDCa-"
|
31 |
+
},
|
32 |
+
"outputs": [],
|
33 |
+
"source": [
|
34 |
+
"import numpy as np\n",
|
35 |
+
"from tensorflow import keras\n",
|
36 |
+
"from tensorflow.keras import layers\n",
|
37 |
+
"\n",
|
38 |
+
"max_features = 20000 # Only consider the top 20k words\n",
|
39 |
+
"maxlen = 200 # Only consider the first 200 words of each movie review\n"
|
40 |
+
]
|
41 |
+
},
|
42 |
+
{
|
43 |
+
"cell_type": "markdown",
|
44 |
+
"metadata": {
|
45 |
+
"id": "stZrr3dDDCa_"
|
46 |
+
},
|
47 |
+
"source": [
|
48 |
+
"## Build the model"
|
49 |
+
]
|
50 |
+
},
|
51 |
+
{
|
52 |
+
"cell_type": "code",
|
53 |
+
"execution_count": 3,
|
54 |
+
"metadata": {
|
55 |
+
"id": "vY-gyrLVDCa_",
|
56 |
+
"outputId": "b4adc98f-1452-4f26-a9cd-ff1a83c1186b",
|
57 |
+
"colab": {
|
58 |
+
"base_uri": "https://localhost:8080/"
|
59 |
+
}
|
60 |
+
},
|
61 |
+
"outputs": [
|
62 |
+
{
|
63 |
+
"output_type": "stream",
|
64 |
+
"name": "stdout",
|
65 |
+
"text": [
|
66 |
+
"Model: \"model\"\n",
|
67 |
+
"_________________________________________________________________\n",
|
68 |
+
" Layer (type) Output Shape Param # \n",
|
69 |
+
"=================================================================\n",
|
70 |
+
" input_1 (InputLayer) [(None, None)] 0 \n",
|
71 |
+
" \n",
|
72 |
+
" embedding (Embedding) (None, None, 128) 2560000 \n",
|
73 |
+
" \n",
|
74 |
+
" bidirectional (Bidirectiona (None, None, 128) 98816 \n",
|
75 |
+
" l) \n",
|
76 |
+
" \n",
|
77 |
+
" bidirectional_1 (Bidirectio (None, 128) 98816 \n",
|
78 |
+
" nal) \n",
|
79 |
+
" \n",
|
80 |
+
" dense (Dense) (None, 1) 129 \n",
|
81 |
+
" \n",
|
82 |
+
"=================================================================\n",
|
83 |
+
"Total params: 2,757,761\n",
|
84 |
+
"Trainable params: 2,757,761\n",
|
85 |
+
"Non-trainable params: 0\n",
|
86 |
+
"_________________________________________________________________\n"
|
87 |
+
]
|
88 |
+
}
|
89 |
+
],
|
90 |
+
"source": [
|
91 |
+
"# Input for variable-length sequences of integers\n",
|
92 |
+
"inputs = keras.Input(shape=(None,), dtype=\"int32\")\n",
|
93 |
+
"# Embed each integer in a 128-dimensional vector\n",
|
94 |
+
"x = layers.Embedding(max_features, 128)(inputs)\n",
|
95 |
+
"# Add 2 bidirectional LSTMs\n",
|
96 |
+
"x = layers.Bidirectional(layers.LSTM(64, return_sequences=True))(x)\n",
|
97 |
+
"x = layers.Bidirectional(layers.LSTM(64))(x)\n",
|
98 |
+
"# Add a classifier\n",
|
99 |
+
"outputs = layers.Dense(1, activation=\"sigmoid\")(x)\n",
|
100 |
+
"model = keras.Model(inputs, outputs)\n",
|
101 |
+
"model.summary()\n"
|
102 |
+
]
|
103 |
+
},
|
104 |
+
{
|
105 |
+
"cell_type": "markdown",
|
106 |
+
"metadata": {
|
107 |
+
"id": "q3uxzdZqDCbA"
|
108 |
+
},
|
109 |
+
"source": [
|
110 |
+
"## Load the IMDB movie review sentiment data"
|
111 |
+
]
|
112 |
+
},
|
113 |
+
{
|
114 |
+
"cell_type": "code",
|
115 |
+
"execution_count": 4,
|
116 |
+
"metadata": {
|
117 |
+
"id": "E6h8swe3DCbA",
|
118 |
+
"outputId": "9377f4e4-1586-4a46-81c0-21bd2af9ab1d",
|
119 |
+
"colab": {
|
120 |
+
"base_uri": "https://localhost:8080/"
|
121 |
+
}
|
122 |
+
},
|
123 |
+
"outputs": [
|
124 |
+
{
|
125 |
+
"output_type": "stream",
|
126 |
+
"name": "stdout",
|
127 |
+
"text": [
|
128 |
+
"Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz\n",
|
129 |
+
"17465344/17464789 [==============================] - 0s 0us/step\n",
|
130 |
+
"17473536/17464789 [==============================] - 0s 0us/step\n",
|
131 |
+
"25000 Training sequences\n",
|
132 |
+
"25000 Validation sequences\n"
|
133 |
+
]
|
134 |
+
}
|
135 |
+
],
|
136 |
+
"source": [
|
137 |
+
"(x_train, y_train), (x_val, y_val) = keras.datasets.imdb.load_data(\n",
|
138 |
+
" num_words=max_features\n",
|
139 |
+
")\n",
|
140 |
+
"print(len(x_train), \"Training sequences\")\n",
|
141 |
+
"print(len(x_val), \"Validation sequences\")\n",
|
142 |
+
"x_train = keras.preprocessing.sequence.pad_sequences(x_train, maxlen=maxlen)\n",
|
143 |
+
"x_val = keras.preprocessing.sequence.pad_sequences(x_val, maxlen=maxlen)\n"
|
144 |
+
]
|
145 |
+
},
|
146 |
+
{
|
147 |
+
"cell_type": "markdown",
|
148 |
+
"metadata": {
|
149 |
+
"id": "cFB5inKqDCbB"
|
150 |
+
},
|
151 |
+
"source": [
|
152 |
+
"## Train and evaluate the model"
|
153 |
+
]
|
154 |
+
},
|
155 |
+
{
|
156 |
+
"cell_type": "code",
|
157 |
+
"execution_count": 5,
|
158 |
+
"metadata": {
|
159 |
+
"id": "-LykThh3DCbC",
|
160 |
+
"outputId": "36b92bc2-2e61-4dee-bd33-693f60a6b35d",
|
161 |
+
"colab": {
|
162 |
+
"base_uri": "https://localhost:8080/"
|
163 |
+
}
|
164 |
+
},
|
165 |
+
"outputs": [
|
166 |
+
{
|
167 |
+
"output_type": "stream",
|
168 |
+
"name": "stdout",
|
169 |
+
"text": [
|
170 |
+
"Epoch 1/10\n",
|
171 |
+
"782/782 [==============================] - 60s 55ms/step - loss: 0.4061 - accuracy: 0.8174 - val_loss: 0.3313 - val_accuracy: 0.8699\n",
|
172 |
+
"Epoch 2/10\n",
|
173 |
+
"782/782 [==============================] - 43s 54ms/step - loss: 0.2099 - accuracy: 0.9220 - val_loss: 0.3656 - val_accuracy: 0.8599\n",
|
174 |
+
"Epoch 3/10\n",
|
175 |
+
"782/782 [==============================] - 43s 55ms/step - loss: 0.1419 - accuracy: 0.9496 - val_loss: 0.4033 - val_accuracy: 0.8422\n",
|
176 |
+
"Epoch 4/10\n",
|
177 |
+
"782/782 [==============================] - 41s 53ms/step - loss: 0.0931 - accuracy: 0.9690 - val_loss: 0.4559 - val_accuracy: 0.8564\n",
|
178 |
+
"Epoch 5/10\n",
|
179 |
+
"782/782 [==============================] - 41s 52ms/step - loss: 0.0657 - accuracy: 0.9794 - val_loss: 0.4797 - val_accuracy: 0.8520\n",
|
180 |
+
"Epoch 6/10\n",
|
181 |
+
"782/782 [==============================] - 41s 53ms/step - loss: 0.0687 - accuracy: 0.9772 - val_loss: 0.4637 - val_accuracy: 0.8446\n",
|
182 |
+
"Epoch 7/10\n",
|
183 |
+
"782/782 [==============================] - 42s 53ms/step - loss: 0.0444 - accuracy: 0.9859 - val_loss: 0.5390 - val_accuracy: 0.8501\n",
|
184 |
+
"Epoch 8/10\n",
|
185 |
+
"782/782 [==============================] - 41s 53ms/step - loss: 0.0272 - accuracy: 0.9918 - val_loss: 0.5995 - val_accuracy: 0.8476\n",
|
186 |
+
"Epoch 9/10\n",
|
187 |
+
"782/782 [==============================] - 41s 53ms/step - loss: 0.0217 - accuracy: 0.9933 - val_loss: 0.7357 - val_accuracy: 0.8380\n",
|
188 |
+
"Epoch 10/10\n",
|
189 |
+
"782/782 [==============================] - 42s 53ms/step - loss: 0.0346 - accuracy: 0.9899 - val_loss: 0.6166 - val_accuracy: 0.8493\n"
|
190 |
+
]
|
191 |
+
},
|
192 |
+
{
|
193 |
+
"output_type": "execute_result",
|
194 |
+
"data": {
|
195 |
+
"text/plain": [
|
196 |
+
"<keras.callbacks.History at 0x7fd40009af90>"
|
197 |
+
]
|
198 |
+
},
|
199 |
+
"metadata": {},
|
200 |
+
"execution_count": 5
|
201 |
+
}
|
202 |
+
],
|
203 |
+
"source": [
|
204 |
+
"model.compile(\"adam\", \"binary_crossentropy\", metrics=[\"accuracy\"])\n",
|
205 |
+
"model.fit(x_train, y_train, batch_size=32, epochs=10, validation_data=(x_val, y_val))\n"
|
206 |
+
]
|
207 |
+
},
|
208 |
+
{
|
209 |
+
"cell_type": "code",
|
210 |
+
"source": [
|
211 |
+
"!pip install huggingface-hub\n",
|
212 |
+
"!curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash\n",
|
213 |
+
"!sudo apt-get install git-lfs\n",
|
214 |
+
"!git-lfs install"
|
215 |
+
],
|
216 |
+
"metadata": {
|
217 |
+
"id": "zf9nO4dbDDkF",
|
218 |
+
"outputId": "c897c215-e1ac-48a7-ffa4-e2c009312e64",
|
219 |
+
"colab": {
|
220 |
+
"base_uri": "https://localhost:8080/"
|
221 |
+
}
|
222 |
+
},
|
223 |
+
"execution_count": 6,
|
224 |
+
"outputs": [
|
225 |
+
{
|
226 |
+
"output_type": "stream",
|
227 |
+
"name": "stdout",
|
228 |
+
"text": [
|
229 |
+
"Collecting huggingface-hub\n",
|
230 |
+
" Downloading huggingface_hub-0.2.1-py3-none-any.whl (61 kB)\n",
|
231 |
+
"\u001b[?25l\r\u001b[K |█████▎ | 10 kB 21.8 MB/s eta 0:00:01\r\u001b[K |██████████▋ | 20 kB 23.7 MB/s eta 0:00:01\r\u001b[K |███████████████▉ | 30 kB 16.0 MB/s eta 0:00:01\r\u001b[K |█████████████████████▏ | 40 kB 15.0 MB/s eta 0:00:01\r\u001b[K |██████████████████████████▌ | 51 kB 6.9 MB/s eta 0:00:01\r\u001b[K |███████████████████████████████▊| 61 kB 7.9 MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 61 kB 432 kB/s \n",
|
232 |
+
"\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from huggingface-hub) (3.4.0)\n",
|
233 |
+
"Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from huggingface-hub) (4.62.3)\n",
|
234 |
+
"Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub) (21.3)\n",
|
235 |
+
"Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from huggingface-hub) (3.13)\n",
|
236 |
+
"Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from huggingface-hub) (4.8.2)\n",
|
237 |
+
"Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from huggingface-hub) (2.23.0)\n",
|
238 |
+
"Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub) (3.10.0.2)\n",
|
239 |
+
"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.9->huggingface-hub) (3.0.6)\n",
|
240 |
+
"Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->huggingface-hub) (3.6.0)\n",
|
241 |
+
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub) (3.0.4)\n",
|
242 |
+
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub) (2.10)\n",
|
243 |
+
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub) (1.24.3)\n",
|
244 |
+
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub) (2021.10.8)\n",
|
245 |
+
"Installing collected packages: huggingface-hub\n",
|
246 |
+
"Successfully installed huggingface-hub-0.2.1\n",
|
247 |
+
"Detected operating system as Ubuntu/bionic.\n",
|
248 |
+
"Checking for curl...\n",
|
249 |
+
"Detected curl...\n",
|
250 |
+
"Checking for gpg...\n",
|
251 |
+
"Detected gpg...\n",
|
252 |
+
"Running apt-get update... done.\n",
|
253 |
+
"Installing apt-transport-https... done.\n",
|
254 |
+
"Installing /etc/apt/sources.list.d/github_git-lfs.list...done.\n",
|
255 |
+
"Importing packagecloud gpg key... done.\n",
|
256 |
+
"Running apt-get update... done.\n",
|
257 |
+
"\n",
|
258 |
+
"The repository is setup! You can now install packages.\n",
|
259 |
+
"Reading package lists... Done\n",
|
260 |
+
"Building dependency tree \n",
|
261 |
+
"Reading state information... Done\n",
|
262 |
+
"The following NEW packages will be installed:\n",
|
263 |
+
" git-lfs\n",
|
264 |
+
"0 upgraded, 1 newly installed, 0 to remove and 62 not upgraded.\n",
|
265 |
+
"Need to get 6,526 kB of archives.\n",
|
266 |
+
"After this operation, 14.7 MB of additional disk space will be used.\n",
|
267 |
+
"Get:1 https://packagecloud.io/github/git-lfs/ubuntu bionic/main amd64 git-lfs amd64 3.0.2 [6,526 kB]\n",
|
268 |
+
"Fetched 6,526 kB in 0s (15.6 MB/s)\n",
|
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+
"debconf: unable to initialize frontend: Dialog\n",
|
270 |
+
"debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 1.)\n",
|
271 |
+
"debconf: falling back to frontend: Readline\n",
|
272 |
+
"debconf: unable to initialize frontend: Readline\n",
|
273 |
+
"debconf: (This frontend requires a controlling tty.)\n",
|
274 |
+
"debconf: falling back to frontend: Teletype\n",
|
275 |
+
"dpkg-preconfigure: unable to re-open stdin: \n",
|
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"id": "MtqmkupzDHiw",
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"outputId": "8ed10181-ca9c-4300-b5fe-d332ab27e40f",
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"\n",
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" To login, `huggingface_hub` now requires a token generated from https://huggingface.co/settings/token.\n",
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" (Deprecated, will be removed in v0.3.0) To login with username and password instead, interrupt with Ctrl+C.\n",
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" \n",
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"Token: \n",
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"Login successful\n",
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"Your token has been saved to /root/.huggingface/token\n",
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"\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n",
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"You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n",
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"\n",
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"git config --global credential.helper store\u001b[0m\n"
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"from huggingface_hub.keras_mixin import push_to_hub_keras\n",
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"push_to_hub_keras(model = model, repo_url = \"https://huggingface.co/keras-io/bidirectional-lstm-imdb\", organization = \"keras-io\")"
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"name": "stderr",
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"text": [
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"Cloning https://huggingface.co/keras-io/bidirectional-lstm-imdb into local empty directory.\n",
|
384 |
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"WARNING:absl:Found untraced functions such as lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_2_layer_call_fn, lstm_cell_2_layer_call_and_return_conditional_losses, lstm_cell_4_layer_call_fn while saving (showing 5 of 20). These functions will not be directly callable after loading.\n"
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"text": [
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"INFO:tensorflow:Assets written to: bidirectional-lstm-imdb/assets\n"
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"text": [
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"INFO:tensorflow:Assets written to: bidirectional-lstm-imdb/assets\n",
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"WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7fd40f60f4d0> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
|
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"WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7fd4002e6090> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
|
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"WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7fd4001d3450> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
|
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"WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7fd400138710> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
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"Adding files tracked by Git LFS: ['variables/variables.data-00000-of-00001']. This may take a bit of time if the files are large.\n",
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"WARNING:huggingface_hub.repository:Adding files tracked by Git LFS: ['variables/variables.data-00000-of-00001']. This may take a bit of time if the files are large.\n"
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"text/plain": [
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"text": [
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"To https://huggingface.co/keras-io/bidirectional-lstm-imdb\n",
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" a166e04..c9b6149 main -> main\n",
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"\n",
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"WARNING:huggingface_hub.repository:To https://huggingface.co/keras-io/bidirectional-lstm-imdb\n",
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" a166e04..c9b6149 main -> main\n",
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