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  1. Putting_it_all_together_(TensorFlow).ipynb +1794 -0
Putting_it_all_together_(TensorFlow).ipynb ADDED
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1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "markdown",
5
+ "metadata": {
6
+ "id": "dPrEUyJ4XXrr"
7
+ },
8
+ "source": [
9
+ "# Putting it all together (TensorFlow)"
10
+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "metadata": {
15
+ "id": "xQjFMOvGXXru"
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+ },
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+ "source": [
18
+ "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
23
+ "execution_count": 1,
24
+ "metadata": {
25
+ "id": "4IyvuubVXXrx",
26
+ "outputId": "47421454-109f-499e-800f-635e9ffd5d79",
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+ "colab": {
28
+ "base_uri": "https://localhost:8080/"
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+ }
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+ },
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+ "outputs": [
32
+ {
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+ "output_type": "stream",
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+ "name": "stdout",
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+ "text": [
36
+ "Collecting datasets\n",
37
+ " Downloading datasets-2.14.4-py3-none-any.whl (519 kB)\n",
38
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m519.3/519.3 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "\u001b[?25hCollecting evaluate\n",
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+ " Downloading evaluate-0.4.0-py3-none-any.whl (81 kB)\n",
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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m81.4/81.4 kB\u001b[0m \u001b[31m9.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "\u001b[?25hCollecting transformers[sentencepiece]\n",
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+ " Downloading transformers-4.32.1-py3-none-any.whl (7.5 MB)\n",
44
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.5/7.5 MB\u001b[0m \u001b[31m24.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
45
+ "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.23.5)\n",
46
+ "Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (9.0.0)\n",
47
+ "Collecting dill<0.3.8,>=0.3.0 (from datasets)\n",
48
+ " Downloading dill-0.3.7-py3-none-any.whl (115 kB)\n",
49
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m13.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
50
+ "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (1.5.3)\n",
51
+ "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2.31.0)\n",
52
+ "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.66.1)\n",
53
+ "Collecting xxhash (from datasets)\n",
54
+ " Downloading xxhash-3.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
55
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m19.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
56
+ "\u001b[?25hCollecting multiprocess (from datasets)\n",
57
+ " Downloading multiprocess-0.70.15-py310-none-any.whl (134 kB)\n",
58
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m14.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
59
+ "\u001b[?25hRequirement already satisfied: fsspec[http]>=2021.11.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.6.0)\n",
60
+ "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.8.5)\n",
61
+ "Collecting huggingface-hub<1.0.0,>=0.14.0 (from datasets)\n",
62
+ " Downloading huggingface_hub-0.16.4-py3-none-any.whl (268 kB)\n",
63
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m25.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
64
+ "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (23.1)\n",
65
+ "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (6.0.1)\n",
66
+ "Collecting responses<0.19 (from evaluate)\n",
67
+ " Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
68
+ "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]) (3.12.2)\n",
69
+ "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]) (2023.6.3)\n",
70
+ "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers[sentencepiece])\n",
71
+ " Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
72
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m51.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
73
+ "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers[sentencepiece])\n",
74
+ " Downloading safetensors-0.3.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
75
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m53.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
76
+ "\u001b[?25hCollecting sentencepiece!=0.1.92,>=0.1.91 (from transformers[sentencepiece])\n",
77
+ " Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
78
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m63.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
79
+ "\u001b[?25hRequirement already satisfied: protobuf in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]) (3.20.3)\n",
80
+ "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (23.1.0)\n",
81
+ "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (3.2.0)\n",
82
+ "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.0.4)\n",
83
+ "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n",
84
+ "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.9.2)\n",
85
+ "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.4.0)\n",
86
+ "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n",
87
+ "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0.0,>=0.14.0->datasets) (4.7.1)\n",
88
+ "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.4)\n",
89
+ "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2.0.4)\n",
90
+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2023.7.22)\n",
91
+ "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
92
+ "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.3)\n",
93
+ "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
94
+ "Installing collected packages: tokenizers, sentencepiece, safetensors, xxhash, dill, responses, multiprocess, huggingface-hub, transformers, datasets, evaluate\n",
95
+ "Successfully installed datasets-2.14.4 dill-0.3.7 evaluate-0.4.0 huggingface-hub-0.16.4 multiprocess-0.70.15 responses-0.18.0 safetensors-0.3.3 sentencepiece-0.1.99 tokenizers-0.13.3 transformers-4.32.1 xxhash-3.3.0\n"
96
+ ]
97
+ }
98
+ ],
99
+ "source": [
100
+ "!pip install datasets evaluate transformers[sentencepiece]"
101
+ ]
102
+ },
103
+ {
104
+ "cell_type": "code",
105
+ "execution_count": 2,
106
+ "metadata": {
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+ "id": "_IaZrjk2XXr5",
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+ "outputId": "e56a47dd-a01f-4786-88aa-fc036f901d39",
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+ "colab": {
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+ "base_uri": "https://localhost:8080/",
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+ "height": 113,
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+ "referenced_widgets": [
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+ "6e8d9515b0d64541badafb94f886fb53",
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+ "64c20d1c08bb4ef5a2c805191da6e0c3",
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+ "2e0141a2e18745329743dc82a713ab3a",
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+ "62d78d871c4247f597f5281b08659a5a",
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+ "d80a1e1d92c7427d8050315b7929ac44",
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+ "d294acadea3c4083807ce5de4c85b139",
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+ "c18a297542a448138c12dc0cb663472e",
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+ "3b183e7bfa964561b385bb12397f767e",
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+ "8429cf84086540fe824480bdd89af8db",
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+ "673be5123a094a9f854eda985491a7b2",
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+ "114e3e99a9644a2d9af94b4790f0f43a",
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+ "4c8af8869fcf4f29a3fc24c50afb85c6",
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+ "748786cbc5e945cbb2b3d5662e5c7b52",
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+ "b7481b2e7dad4b4491c86e9b5d4fc082",
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+ "46a7d5c190334718a6d786e96e14adc9",
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+ "2e17ea86befe47d19abc6b3b40fccfe4",
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+ "94141f0ef5374572b85f01089ff9901f",
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+ "d6ca5e34870444848083c75e75516d5a"
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+ ]
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+ }
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+ },
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+ "outputs": [
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+ {
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+ "output_type": "display_data",
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+ "data": {
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+ "text/plain": [
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+ "Downloading (…)okenizer_config.json: 0%| | 0.00/48.0 [00:00<?, ?B/s]"
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+ ],
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+ "application/vnd.jupyter.widget-view+json": {
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+ "version_major": 2,
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+ "version_minor": 0,
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+ "model_id": "6e8d9515b0d64541badafb94f886fb53"
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+ }
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+ },
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+ "metadata": {}
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+ },
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+ {
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+ "output_type": "display_data",
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+ "data": {
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+ "text/plain": [
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+ "Downloading (…)lve/main/config.json: 0%| | 0.00/629 [00:00<?, ?B/s]"
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+ ],
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+ "application/vnd.jupyter.widget-view+json": {
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+ "version_major": 2,
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+ "version_minor": 0,
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+ "model_id": "fb8f882fd9e445f48c93abecb47c8707"
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+ }
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+ },
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+ "metadata": {}
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+ },
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+ {
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+ "output_type": "display_data",
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+ "data": {
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+ "text/plain": [
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+ "Downloading (…)solve/main/vocab.txt: 0%| | 0.00/232k [00:00<?, ?B/s]"
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+ ],
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+ "application/vnd.jupyter.widget-view+json": {
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+ "version_major": 2,
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+ "version_minor": 0,
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+ "model_id": "7695d2d28c834eee9d3c719cea0ac2cf"
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+ }
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+ },
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+ "metadata": {}
191
+ }
192
+ ],
193
+ "source": [
194
+ "from transformers import AutoTokenizer\n",
195
+ "\n",
196
+ "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
197
+ "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
198
+ "\n",
199
+ "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
200
+ "\n",
201
+ "model_inputs = tokenizer(sequence)"
202
+ ]
203
+ },
204
+ {
205
+ "cell_type": "code",
206
+ "execution_count": 3,
207
+ "metadata": {
208
+ "id": "1M-XZeCTXXr7"
209
+ },
210
+ "outputs": [],
211
+ "source": [
212
+ "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
213
+ "\n",
214
+ "model_inputs = tokenizer(sequence)"
215
+ ]
216
+ },
217
+ {
218
+ "cell_type": "code",
219
+ "execution_count": 4,
220
+ "metadata": {
221
+ "id": "LeKkAHGlXXr9"
222
+ },
223
+ "outputs": [],
224
+ "source": [
225
+ "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
226
+ "\n",
227
+ "model_inputs = tokenizer(sequences)"
228
+ ]
229
+ },
230
+ {
231
+ "cell_type": "code",
232
+ "execution_count": 5,
233
+ "metadata": {
234
+ "id": "lF1e6fKQXXr-"
235
+ },
236
+ "outputs": [],
237
+ "source": [
238
+ "# Will pad the sequences up to the maximum sequence length\n",
239
+ "model_inputs = tokenizer(sequences, padding=\"longest\")\n",
240
+ "\n",
241
+ "# Will pad the sequences up to the model max length\n",
242
+ "# (512 for BERT or DistilBERT)\n",
243
+ "model_inputs = tokenizer(sequences, padding=\"max_length\")\n",
244
+ "\n",
245
+ "# Will pad the sequences up to the specified max length\n",
246
+ "model_inputs = tokenizer(sequences, padding=\"max_length\", max_length=8)"
247
+ ]
248
+ },
249
+ {
250
+ "cell_type": "code",
251
+ "execution_count": 6,
252
+ "metadata": {
253
+ "id": "nhOlP4X7XXsA"
254
+ },
255
+ "outputs": [],
256
+ "source": [
257
+ "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
258
+ "\n",
259
+ "# Will truncate the sequences that are longer than the model max length\n",
260
+ "# (512 for BERT or DistilBERT)\n",
261
+ "model_inputs = tokenizer(sequences, truncation=True)\n",
262
+ "\n",
263
+ "# Will truncate the sequences that are longer than the specified max length\n",
264
+ "model_inputs = tokenizer(sequences, max_length=8, truncation=True)"
265
+ ]
266
+ },
267
+ {
268
+ "cell_type": "code",
269
+ "execution_count": 7,
270
+ "metadata": {
271
+ "id": "WU9TqDuqXXsB"
272
+ },
273
+ "outputs": [],
274
+ "source": [
275
+ "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
276
+ "\n",
277
+ "# Returns PyTorch tensors\n",
278
+ "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"pt\")\n",
279
+ "\n",
280
+ "# Returns TensorFlow tensors\n",
281
+ "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"tf\")\n",
282
+ "\n",
283
+ "# Returns NumPy arrays\n",
284
+ "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"np\")"
285
+ ]
286
+ },
287
+ {
288
+ "cell_type": "code",
289
+ "execution_count": 8,
290
+ "metadata": {
291
+ "id": "LidWgT5-XXsD",
292
+ "outputId": "01d47ecc-a8fc-4d7c-d534-88c090bb26fe",
293
+ "colab": {
294
+ "base_uri": "https://localhost:8080/"
295
+ }
296
+ },
297
+ "outputs": [
298
+ {
299
+ "output_type": "stream",
300
+ "name": "stdout",
301
+ "text": [
302
+ "[101, 1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012, 102]\n",
303
+ "[1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012]\n"
304
+ ]
305
+ }
306
+ ],
307
+ "source": [
308
+ "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
309
+ "\n",
310
+ "model_inputs = tokenizer(sequence)\n",
311
+ "print(model_inputs[\"input_ids\"])\n",
312
+ "\n",
313
+ "tokens = tokenizer.tokenize(sequence)\n",
314
+ "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
315
+ "print(ids)"
316
+ ]
317
+ },
318
+ {
319
+ "cell_type": "code",
320
+ "execution_count": 9,
321
+ "metadata": {
322
+ "id": "3rBbz5KFXXsE",
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+ "outputId": "cfcbde1b-fadc-4297-b336-ebb5e1a3f56c",
324
+ "colab": {
325
+ "base_uri": "https://localhost:8080/"
326
+ }
327
+ },
328
+ "outputs": [
329
+ {
330
+ "output_type": "stream",
331
+ "name": "stdout",
332
+ "text": [
333
+ "[CLS] i've been waiting for a huggingface course my whole life. [SEP]\n",
334
+ "i've been waiting for a huggingface course my whole life.\n"
335
+ ]
336
+ }
337
+ ],
338
+ "source": [
339
+ "print(tokenizer.decode(model_inputs[\"input_ids\"]))\n",
340
+ "print(tokenizer.decode(ids))"
341
+ ]
342
+ },
343
+ {
344
+ "cell_type": "code",
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+ "metadata": {
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+ "outputs": [
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+ {
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+ "output_type": "display_data",
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+ "data": {
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+ "text/plain": [
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+ "Downloading model.safetensors: 0%| | 0.00/268M [00:00<?, ?B/s]"
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+ }
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+ },
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+ "metadata": {}
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+ },
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+ {
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+ "output_type": "stream",
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+ "name": "stderr",
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+ "text": [
386
+ "All PyTorch model weights were used when initializing TFDistilBertForSequenceClassification.\n",
387
+ "\n",
388
+ "All the weights of TFDistilBertForSequenceClassification were initialized from the PyTorch model.\n",
389
+ "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFDistilBertForSequenceClassification for predictions without further training.\n"
390
+ ]
391
+ }
392
+ ],
393
+ "source": [
394
+ "import tensorflow as tf\n",
395
+ "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
396
+ "\n",
397
+ "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
398
+ "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
399
+ "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
400
+ "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
401
+ "\n",
402
+ "tokens = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"tf\")\n",
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+ "output = model(**tokens)"
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+ ]
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+ }
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