diff --git a/main (1).ipynb b/main (1).ipynb new file mode 100644 index 0000000..584d3ea --- /dev/null +++ b/main (1).ipynb @@ -0,0 +1,15509 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "r-UgA6tdOuJF" + }, + "source": [ + "# Lab | Transformers\n", + "\n", + "---\n", + "\n", + "### Section structure\n", + "\n", + "1. The open-source ecosystem: increasing accessibility to machine learning (ML) software and hardware\n", + "2. Some simple code demonstrations\n", + "3. Q&A" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-AlHgXZCVNr0" + }, + "source": [ + "## 1. Ease-of-use: Using Transformers in 3 lines of code" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LpOoqdeqbCI-" + }, + "source": [ + "\n", + "**Overview of different tasks that can be automated with ML**\n", + "* Key ingredients: (1) a model trained on a specific task; (2) input data (e.g. texts or images); (3) output produced by the model.\n", + "* Transformers are currently the most popular type of deep learning algorithm. Most tasks below are solved with Transformers. There might be other types of algorithms coming up in the medium term.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QP60sINRSprG" + }, + "source": [ + "**Install the Transformers library & dependencies**" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "dBMBmzH0StsF", + "outputId": "d4272e10-cc30-40f4-8aa7-5e64198083e7" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: transformers in /usr/local/lib/python3.11/dist-packages (4.48.3)\n", + "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from transformers) (3.17.0)\n", + "Requirement already satisfied: huggingface-hub<1.0,>=0.24.0 in /usr/local/lib/python3.11/dist-packages (from transformers) (0.28.1)\n", + "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.11/dist-packages (from transformers) 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satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->torch) (3.0.2)\n" + ] + } + ], + "source": [ + "!pip install transformers # The Transformers library from Hugging Face\n", + "!pip install sentencepiece\n", + "!pip install wikipedia\n", + "!pip install accelerate\n", + "!pip install tf-keras\n", + "!pip install torch\n", + "\n", + "# NOTE: you might need to restart you jupyter kernel after installing the libraries" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "D5FYbEIBEzEu" + }, + "source": [ + "**The Hugging Face Pipeline**\n", + "* Makes automation of many NLP tasks possible in 3 lines of code\n", + "* Detailed documentation is available [here](https://huggingface.co/transformers/main_classes/pipelines.html)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "0U1iHK0ETBXf" + }, + "outputs": [], + "source": [ + "from transformers import pipeline\n", + "import pandas as pd\n", + "import numpy as np\n", + "from pprint import pprint" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ni5m35-PHSuJ" + }, + "source": [ + "Note : You might need more libraries or updates to run the cells below, if that is the case, follow the error messages and pip install accordingly. Chat gpt can help you if given the error messages." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rKt3KNCBQ2fe" + }, + "source": [ + "### 2.1 Many models tailored to specific tasks\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GbZWdV3LWP2l" + }, + "source": [ + "#### 2.1.1 Text classification" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "390ar7USTP30" + }, + "source": [ + "Let's select a popular text classification model in the [HF model hub](https://huggingface.co/models?pipeline_tag=text-classification&sort=downloads).\n", + "\n", + "Here we chose \"cardiffnlp/twitter-roberta-base-irony\".\n", + "\n", + "We will classify text into ironic or non ironic." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 350, + "referenced_widgets": [ + "76754e1def0f470dbca898e934e53f72", + "61235886909442ec924c259089b4a1b7", + "ef85fd5f12e644b8a48329b7bc2c0f46", + "0b553e93f71e4b288471597a29345594", + "1ee464bbb2cc4c8ea7204c5aa883a566", + "32a0071d46774dc58faaf31250ec2764", + "11ee72d69d6a4a0799e5d00ae7db1deb", + "9562f46cb0e546c6b42148b3b57e97e1", + "8e420fdac5da4e24b6f0ba3a10fe2846", + "17f9d58c68d44cfaad03ea5faeee4d1f", + "f6e4d31c3b3247889a2d3bf5c39f2c10", + "1235c8ba5881424f8ece93f0964a51cf", + "56d753343abc481ba417d567ae1b4574", + "d1de387ca7b344b2a5812a8d5e227236", + "5f2058c5b04b498eaabb70e9bc88a8f1", + "d71a62196ac9437186baab35947b124e", + "342a014494e64274a52c3e842c85b16b", + "cd15107ffc5b4bb6a59a0940c7b803d0", + "3a17d396c0084d39a4e46a0963183367", + "6bc48000bf194171a0f663c50d188d67", + "b2bdb8b435284d51970e81c19777cc46", + "b2641fcfe34c4789b5d4f0f9701e09fb", + "3decbc79a410428581a627ccb3e13791", + "e364c48370c6491d9b7c6050b041c82f", + "3e16938dc8eb4ac28764a5f9a5b39873", + "dd4dc11f4d3447f2a76332a47c2af15e", + "aa4a6c641f4e457ca9e15f3ee66bb1d5", + "9d475ca2db4e404e8c9f080debc167e6", + "7515ad66973a43c4a7fe1671c0b48c2c", + "58fe6f4424ec4022a01fd7f6f59138df", + "08041186a3a44b788306163f7456faae", + "abfe54cd0809471b907de73af247dc36", + "6262fea115ad4c928b8b97180c3c674e", + "d73d217210cb4e5985016947f16b0eb4", + "065960f92da1423da404774adfc33da2", + "4032968a3a4d49e5992e7e8a06e6d7c7", + "22683f30591245ffbbebee52818bc45a", + "cbf52456bf764cfea4e6798e3f8e0cc3", + "b7027ced59d84e6bae331ae08a53079a", + "f07e45ee0b434f8798a977754518a1b9", + "29f420f50bff44b58ebb96371bb426ad", + "9c391fc750aa47bfb9a5193a2a4531e4", + "d70bbd266d234af0a628841543e756d2", + "9161327048f344b59c99c60e4e7c38a9", + "23f08884d82d43819d472a61b46d1d8d", + "7918407cf0b0499fa2ebe2481ef6d736", + "c59341e3b0ad4e3dac025126d8dfdec5", + "c16525fc0e684b60b0efcb228d162fca", + "200c78aa44d543baa30f6d82d8c90d83", + "db4e15316d074d218abe8f3549b200ea", + "b6e85a512e224d8e87bf75121522e084", + "82ed4c0c80574c45ae198ae5caa66e5f", + "aa635cc3ab124888ab35345bdf784b7a", + "33955e22a313439dbc19ef9f956aed71", + "a10504c233f34d21b967ed5a8ebb8329", + "e78e9c7c1e06429ca38d436336598d9d", + "23865930d63b40e1aaa6623828cee9d3", + "f4bcee22b8c64b229213489349d8fef3", + "601a4d63b5c84c73a5b7a75f2e0cda48", + "924a05fb8cc74ae081e62b6ad7b321a3", + "85ff4018b3154bb0a162442c07dde75d", + "a26b48d671a842408a6a68cf246884d2", + "c02be5837df84d2e9733be6c189c624a", + "42d4886853044d948d9df4200a05174f", + "b4d6acf70c394bd6b21d2966dcb8fb0d", + "559f3f3d9a984edcadd5eb77e3fcaefd" + ] + }, + "id": "cCf4vsXrbczm", + "outputId": "290b054d-7aec-4c9e-a59a-18d30faa737a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", + "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", + "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "config.json: 0%| | 0.00/705 [00:00\n", + "
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entity_groupscorewordstartend
0PER0.985677donald john trump017
1MISC0.991559american4553
2LOC0.991947united states134147
3MISC0.856172republican165175
4ORG0.641689party176181
5LOC0.994514new york city242255
6PER0.981356trump257262
7ORG0.634990university of pennsylvania282308
8ORG0.886364trump organization441459
9MISC0.918888the apprentice679693
10PER0.987900trump717722
11MISC0.995448democratic766776
12PER0.993702hillary clinton785800
13PER0.978521trump821826
14MISC0.991937muslim869875
15LOC0.977024u. s.909913
16LOC0.996966mexico914920
17MISC0.945072tax cuts and jobs act10441065
18ORG0.732919supreme court10871100
19PER0.984474trump11301135
20LOC0.994024u. s11491152
21LOC0.994569iran11931197
22LOC0.996577china12401245
23MISC0.985318north korean12601272
24PER0.982862kim jong un12801291
25MISC0.988051co13561358
26MISC0.928581##vid - 1913581364
27MISC0.973578cares act14491458
28PER0.982144trump14691474
29ORG0.614934congress15351543
30ORG0.989297senate15931599
31MISC0.995218american17201728
32PER0.985130trump17381743
33MISC0.351972trump17691774
34MISC0.992226american19911999
35PER0.992044joe biden20572066
36PER0.985875trump20682073
37MISC0.428098capitol21382145
38PER0.981908trump21712176
39LOC0.980291u. s23512354
40MISC0.465429.23542355
41PER0.996215kamal24422447
42PER0.983849##a harris24472455
43PER0.991261trump24572462
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\n", + " \n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "summary": "{\n \"name\": \"pd\",\n \"rows\": 44,\n \"fields\": [\n {\n \"column\": \"entity_group\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"MISC\",\n \"ORG\",\n \"PER\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"score\",\n \"properties\": {\n \"dtype\": \"float32\",\n \"num_unique_values\": 44,\n \"samples\": [\n 0.4280976951122284,\n 0.9828624129295349,\n 0.9880510568618774\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"word\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 32,\n \"samples\": [\n \".\",\n \"tax cuts and jobs act\",\n \"cares act\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"start\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 733,\n \"min\": 0,\n \"max\": 2457,\n \"num_unique_values\": 44,\n \"samples\": [\n 2138,\n 1280,\n 1356\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"end\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 731,\n \"min\": 17,\n \"max\": 2462,\n \"num_unique_values\": 44,\n \"samples\": [\n 2145,\n 1291,\n 1358\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 12 + } + ], + "source": [ + "import wikipedia\n", + "wikipedia.set_lang(\"en\")\n", + "\n", + "text_long = wikipedia.summary(\"Donald Trump\").replace('\\n', ' ')\n", + "\n", + "output = pipeline_ner(text_long)\n", + "\n", + "pd.DataFrame(output)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TXPa9hHmP8vJ" + }, + "source": [ + "### 2.2. Universal models" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lEGnV9KabQg4" + }, + "source": [ + "The models mentioned above are designed to excel at a single specific task on a particular dataset. The key advantage of these models is their high performance and accuracy on that specific task and dataset.\n", + "\n", + "However, in real-world applications, the problems you'll face often require solving slightly different tasks, possibly with varied category definitions or applied to different types of texts.\n", + "\n", + "Universal models can help address this challenge. Although they also focus on one task, the task is general or universal enough that many other tasks can be reformulated into it. Two examples of universal tasks are:\n", + "\n", + "- Natural Language Inference (NLI): A task that can effectively solve a wide range of classification tasks by determining whether a given premise supports, contradicts, or is neutral with respect to a hypothesis.\n", + "\n", + "- Token Generation: An even more universal task that can be applied to solve virtually any text-related task, including translation, summarization, and text completion.\n", + "\n", + "These universal tasks enable the models to be versatile and adaptable to various problems beyond the specific ones they were initially trained on." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ocJsfbLcQPpO" + }, + "source": [ + "#### Zero-shot classification\n", + "\n", + "\n", + "Zero-shot classification is a technique where a model can categorize data into classes it has never seen before.\n", + "\n", + "Instead of relying on labeled examples for each class, the model understands the relationship between the input and the class descriptions, allowing it to make accurate predictions without needing specific training on those classes." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 258, + "referenced_widgets": [ + "b6a1b5f9f51f46c8aabf40adb6b18fd0", + "896183c823d54a72bf257e532ba06f12", + "067298be27464f7aac4deceb8300c01c", + "d4e7284dd54a49a489757f2de4322d31", + "4994a83caa2c4179bbd59d6b8c02c22b", + "17343f96cf1d4ac8857a7396c858301f", + "84e84ae43e744773a403e926bc69c342", + "6e15876a41694fd8b9dc37a92b589b28", + "170a2d34602f420b96499c3e351b6ce9", + "0b3e8894299f4bfa9bf4464a643198b3", + "cf3c834b11994640a64f4e00fe8e55cb", + "8cedee0e87ad431eacfbd12553cb90ad", + "a6240c45bade4ec2aa692b9b01b63545", + "64562a733a3740128869f63d970d1d30", + "5dcb02c3f97c44cca3f056212ab5ca20", + "7df3237d2d2f492b8c728ea06e57de5d", + "3bba61a322644f098213358afefa0b21", + "fba3423fde604774b0426bf2d958b2a6", + "c8b0723edff6485497a461db8d458c3c", + "5af400d25aeb469aa90572da859eeda1", + "fbc788d872434f15bee697e7f19ec585", + "07254419d51548eab3361a49ba21c8a8", + "f5ca927a5fe14e2c9f043775269db391", + "d578ed4266d94a98b387c1f83d95c1ea", + "51bc6e6e393f452e8207512fa8b66eae", + "b90b23f374c44604bfef3b7f22ee151a", + "40f92bed24d74674b76ad8a47c641baf", + "f641dae3c2004fc7aacc8ab70160220b", + "1347162e2dca45b3be59151bd62bd63d", + "28a6fd98e64c41d49ee1847c04aa7e98", + "c83dfc95bf3c4703874bdb5a10419b4c", + "4a4ddd0a66f84d7d9d5bc1f233e094ce", + "285d1b2f70e447bdb4fe690d2683973f", + "84d7dc992cf44b2c8adf36167f0e2200", + "13f74534cc03494d81fc7fefe19184ce", + "9d3d488d087d4a1092d9c25822f57cc7", + "aa5ab683118d4eb5add18a7992e98d1a", + "2846ef1fe5c14c399f75fdfd086b4c10", + "d5cd7fc3a08f41a697ca4e888d8a4ef1", + "82f7265501d04873b208491bd99bf912", + "31554cb893494d2e94b4cb74b446b727", + "562fc10bd2f54597b299095a1e0de78d", + "0682750420294faeaa26023e6dc0a181", + "4cdf08a6df95487e9acb0a54272e08f4", + "32f7af3852f34911bfe56097111036dd", + "afee83cd9134429c8f0acd7980b05cc2", + "25e181817c8947c59def069f2c46bda5", + "38844c30dea84f94884e3c7a7eef4756", + "dfe09d493b5e481395efb6c5b0b78862", + "8ec7a78ad72a4655843ffb68ef3d2270", + "288703e6af634b5280a28325bae4ee2c", + "a831449fd4cb42e885e9866246556d74", + "ff57ede044564b9594d50844f77228bb", + "733052b175f5461a90bd0a394c8774fd", + "da5ecb81f5714175a05ca59c1d209b7a", + "84d16f526aac43de9127ebfbc7557257", + "5c03a05700a845e68afad9f4cbaec292", + "4530f5492bb54fcb8afe8f8202e3f537", + "b232ade618d8496497defa856145184f", + "a40b5f08a17f4ff4ac130f34056e0990", + "016a888c10da42e0aad30821db410fa8", + "5559e9fae41744eabff3443d03d10533", + "82a2834afbe04079b6addc8a4e3d8c45", + "a862ecf074324863a06c55026609257d", + "c0e47349d4a74c69b8b02e31dbc6f5d1", + "26168e1b4bc743a8b521dd18c8f5f900", + "73a7eab1286f42c59188f86346d825e3", + "700421afdf424f1cba6e6c5d04de88d9", + "486b3b295c044474bace55e0f08761e6", + "2c35cc0976f6433fbeda8a7aa05f31c0", + "df1e6f0ba091482ca5ba565c70df2f87", + "8476589e7160418abe8d33dbb2ff4521", + "6a998d004621455cbee913a56845983d", + "2ba3ea9657ae472abd9b97b5922b08b5", + "aed9542b2c684be3b1e753b494b312c3", + "b93bbabdfb5b4ecdba358394782829ae", + "f2b3d75af9d14aadb49eac09680efc11" + ] + }, + "id": "CQWu07esQPAF", + "outputId": "e4e8e845-deb8-475e-eb61-563607991317" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "config.json: 0%| | 0.00/1.07k [00:00\n", + "
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classprobability
0payment issues0.991132
1bug report0.076115
2travel advice0.018696
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\n", + " \n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "summary": "{\n \"name\": \"pd\",\n \"rows\": 3,\n \"fields\": [\n {\n \"column\": \"class\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"payment issues\",\n \"bug report\",\n \"travel advice\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"probability\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": 0.018695784732699394,\n \"max\": 0.9911324977874756,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.9911324977874756,\n 0.07611463218927383,\n 0.018695784732699394\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 14 + } + ], + "source": [ + "text = \"Customer: I have not received my reimbursement yet. What the hell is going on?\"\n", + "classes = ['payment issues', 'travel advice', 'bug report'] # \"account opening\", \"customer complaint\"\n", + "\n", + "#text = \"I do not think the government is trustworthy anymore. We need to mobilize and resist!\"\n", + "#classes = [\"civil disobedience\", \"praise of the government\", \"travel advice\"] # \"collective action\"\n", + "\n", + "output = pipeline_zeroshot_classification(text, classes, multi_label=True)\n", + "\n", + "pd.DataFrame(data=[output[\"labels\"], output[\"scores\"]], index=[\"class\", \"probability\"]).T\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JwM1gzfvA-Eb" + }, + "source": [ + "## Exercise\n", + "\n", + "Now it is your turn to go to the hugging face library https://huggingface.co/models?pipeline_tag=text-classification&sort=downloads\n", + "\n", + "(you can select on the left menu of the website the type of NLP tasks you want models to perform)\n", + "\n", + "- Find an NLP model that we have not used previously.\n", + "\n", + "- Get some data from wikipedia or elsewhere.\n", + "\n", + "- Perform inference with the model and print the result!\n", + "\n", + "- Comment your code along the way, describe what your model does and what your end goal is from input to output.\n", + "\n", + "Have fun!" + ] + }, + { + "cell_type": "code", + "source": [ + "from transformers import AutoModelForSequenceClassification\n", + "from transformers import TFAutoModelForSequenceClassification\n", + "from transformers import AutoTokenizer\n", + "import numpy as np\n", + "from scipy.special import softmax\n", + "import csv\n", + "import urllib.request\n", + "\n", + "# Preprocess text (username and link placeholders)\n", + "def preprocess(text):\n", + " new_text = []\n", + " for t in text.split(\" \"):\n", + " t = '@user' if t.startswith('@') and len(t) > 1 else t\n", + " t = 'http' if t.startswith('http') else t\n", + " new_text.append(t)\n", + " return \" \".join(new_text)\n", + "\n", + "# Tasks:\n", + "# emoji, emotion, hate, irony, offensive, sentiment\n", + "# stance/abortion, stance/atheism, stance/climate, stance/feminist, stance/hillary\n", + "\n", + "task='emotion'\n", + "MODEL = f\"cardiffnlp/twitter-roberta-base-{task}\"\n", + "\n", + "tokenizer = AutoTokenizer.from_pretrained(MODEL)\n", + "\n", + "# download label mapping\n", + "mapping_link = f\"https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/{task}/mapping.txt\"\n", + "with urllib.request.urlopen(mapping_link) as f:\n", + " html = f.read().decode('utf-8').split(\"\\n\")\n", + " csvreader = csv.reader(html, delimiter='\\t')\n", + "labels = [row[1] for row in csvreader if len(row) > 1]\n", + "\n", + "# PT\n", + "model = AutoModelForSequenceClassification.from_pretrained(MODEL)\n", + "model.save_pretrained(MODEL)\n", + "\n", + "text = \"Celebrating my promotion 😎\"\n", + "text = preprocess(text)\n", + "encoded_input = tokenizer(text, return_tensors='pt')\n", + "output = model(**encoded_input)\n", + "scores = output[0][0].detach().numpy()\n", + "scores = softmax(scores)\n", + "\n", + "# # TF\n", + "# model = TFAutoModelForSequenceClassification.from_pretrained(MODEL)\n", + "# model.save_pretrained(MODEL)\n", + "\n", + "# text = \"Celebrating my promotion 😎\"\n", + "# encoded_input = tokenizer(text, return_tensors='tf')\n", + "# output = model(encoded_input)\n", + "# scores = output[0][0].numpy()\n", + "# scores = softmax(scores)\n", + "\n", + "ranking = np.argsort(scores)\n", + "ranking = ranking[::-1]\n", + "for i in range(scores.shape[0]):\n", + " l = labels[ranking[i]]\n", + " s = scores[ranking[i]]\n", + " print(f\"{i+1}) {l} {np.round(float(s), 4)}\")\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 371, + "referenced_widgets": [ + "c8940be00da74cb58a82fd5ec31cb250", + "98b96b55893b4e2cbbab3d51176894c2", + "04fc262a7e63450bac251ca275321b38", + "c00f2aa1b5a44515a34929951ed20ff1", + "16c62b38d21744dbb670f5585c809ed1", + "de8f555e48344f79927efb1b56960498", + "4fd05df4017d4d1a96e2a0afcb9e497c", + "5fb9bd8a2e6d448a85f07af8e26c4f6f", + "1318cd1e77cc4965a8c46d0721a8ce56", + "d4061a195ea8446ca92792eb6d7b71ad", + "73b5b4f47901424fa2ee168d07089a94", + "eadcc2171408436fbcbeabe2ef234227", + "e9edfb8478994a60abdc4f85a1c3861c", + "8f22821635c846e0a552d67c570d857b", + "cf6767d9a33341ab8ba676563c1eebdc", + "8885b08cce9c482a9c57ddfe2917ca8f", + "22026e4526754c2c898cfa4c1846426c", + "f6b524f82ac24527b76fc822d481b150", + "92e74a7f37c04c1bbb06ceb039ecc80b", + "e5225ce96fe846fc80643c64ba663aef", + "b83de80549e942d2817b907aaf5199ed", + "7821b1801580413787d990f9290d05f9", + "c6f44333a70d45cb8392beb1b7419206", + "e0c9b037ce864f55a1ca76f7122c65e8", + "752b361446ed4f56834a9f677c8e302f", + "8158306cb21a4ffab620a0d9b83ac686", + "112bbe1d85f54084a1c49033149768f1", + "73b685125d0a4ed39b463a827157d0ca", + "1efde18c95ec49df82a32a6d1f68a727", + "d8d684cbec004bf6aa298a1a344afdfa", + "31d7a8bf7d784d84a8a0442d28f7834c", + "8cf49f16720c420eaa866f7344861d24", + "2092c62beef045ed901ff568e741aa90", + "fad579ea9b754ea58d96032d65aea43a", + "e9292b19dd9c46dca3e31d81604b93f4", + "cebaaab6056b48b5834ac5fe41aca5a7", + "969ead3ddb9c4da6acd8fd2ae749e640", + "b5e22568b50e48d98395d6cb3913c13f", + "b4922a4778f44f3b85e1ab7d3b4cdff9", + "71755ee5f68c4207939d235fda3ad3ac", + "1c9994f2ed8949899e76ceb15bcd9a5a", + "fefd9f594449493d9709ae967a3279d3", + "b6c4e16052534e87b219593b249776c4", + "336f67a49c42472a852dc4eeec6477c0", + "f98eb3b8720a4e2abc0a22bcc7287c39", + "bcc640c66bdd47f98a234b76d8f56c26", + "4caa75ec8e544e9a98f777d1b326d1df", + "35cfab96be144f82b753196bd815af55", + "d6630f4f468647558217d67912b14a45", + "9b25220693784825925c42f063ab9b43", + "dc73e0be74824b64a0b6ebf49bc52f3a", + "32605dd6a47a4c15a421957f9f08a0ab", + "3f4b096bcf0648a4948a4e134e9481f1", + "d19d1e1bdb644dc9a42e9f58b1cb8582", + "a1e0c4c8ec4241afac623f43d4ad0995" + ] + }, + "id": "sUPmTIKIOdNE", + "outputId": "4dc8233e-931d-45e6-d989-13bac277c780" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", + "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", + "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "config.json: 0%| | 0.00/768 [00:00=0.11.0 in /Users/joaorochaemelo/code/IH/venv_ironhack/lib/python3.11/site-packages (from transformers) (0.23.3)\n", - "Requirement already satisfied: numpy>=1.17 in /Users/joaorochaemelo/code/IH/venv_ironhack/lib/python3.11/site-packages (from transformers) (1.26.4)\n", - "Requirement already satisfied: packaging>=20.0 in /Users/joaorochaemelo/code/IH/venv_ironhack/lib/python3.11/site-packages (from 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] - } - ], - "source": [ - "!pip install transformers # The Transformers library from Hugging Face\n", - "!pip install sentencepiece\n", - "!pip install wikipedia\n", - "!pip install accelerate\n", - "!pip install tf-keras\n", - "!pip install torch\n", - "\n", - "# NOTE: you might need to restart you jupyter kernel after installing the libraries" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "D5FYbEIBEzEu" - }, - "source": [ - "**The Hugging Face Pipeline**\n", - "* Makes automation of many NLP tasks possible in 3 lines of code\n", - "* Detailed documentation is available [here](https://huggingface.co/transformers/main_classes/pipelines.html)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "0U1iHK0ETBXf" - }, - "outputs": [], - "source": [ - "from transformers import pipeline\n", - "import pandas as pd\n", - "import numpy as np\n", - "from pprint import pprint" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note : You might need more libraries or updates to run the cells below, if that is the case, follow the error messages and pip install accordingly. Chat gpt can help you if given the error messages." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "rKt3KNCBQ2fe" - }, - "source": [ - "### 2.1 Many models tailored to specific tasks\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "GbZWdV3LWP2l" - }, - "source": [ - "#### 2.1.1 Text classification" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "390ar7USTP30" - }, - "source": [ - "Let's select a popular text classification model in the [HF model hub](https://huggingface.co/models?pipeline_tag=text-classification&sort=downloads).\n", - "\n", - "Here we chose \"cardiffnlp/twitter-roberta-base-irony\".\n", - "\n", - "We will classify text into ironic or non ironic." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 177, - "referenced_widgets": [ - "d310190e2ee541c3961c7cb7dcb66aa7", - "f5aa52a8edd14db0b0894e9eebec0ddf", - "ddbd3e0df1e64155b50bab5a865119f2", - "99d81a4a65154dd988e09dacbcb29237", - "a687a8fd93a646239e9e53a535094d31", - "e0575adfffae4642aaf9d1e70718b106", - "84b92cd7d450408ca4eab85484503d0f", - "55a21e2f6e8843adb9950a1dfee90508", - "0b50d006cae94ec099bfe50b294c7129", - "544b2e9b1d8746598cc4acea830537e8", - "e2cfbf57fa54492fa60b5ebf08664d94", - "b0ae591ccb644b0eb14a54681ef4a211", - "f8effa793db54510abb2de1a94f38795", - "d182219f763d48c2ae8b22fbe3b50529", - "eb4be888afb6420b85d39bbab45838ea", - "252dfd5d19cf46f8874fd7c76a93fbd9", - "953b51706e2e4f0f9f5f30b105577059", - "a2db62c8a7504c88950d43751e6dacc7", - "b4e9a86e55cf464a80158fa1ad126eb0", - "7951ad7e06584d26ac73348b96179fd3", - "96cf560149c44a0d897b8515300742e1", - "a1b5654ea93f4c8c837daf59a25a1f5e", - "ee40a38cb9bc4337879c3c7a972d6c4b", - "25163f8eb27040dd8459df6b24a39e7c", - "a20dbdefe9004e00a8b8d3d6b26c8655", - "0396d608c7ce42dd8748ec13f19656e5", - "7c06a1b311be467ebb38f2957fe73594", - "768f995a5b4c4980a5a47e81d8e457d3", - "77f76464643c43ab93a3e2e7ff33a2e8", - "c2a771203d26401bbe3abdcc2f38d00e", - "6bf758c87eba4d0593f9b18b3ef8dfd2", - "eb5afb4bf22f411180753739e668ab23", - "94cbdf6c2b214ef7b3251192be0572c8", - "fe16ba2d7702410c8d12b238975985e5", - "55615a8cbf914b98be1fc382b3f7468b", - "b11597e5e82243e18304c093ab6ebe25", - "c8b5ed560c554bf2b5e18b0f400f852f", - "0b4a841175384c4e8a1ecccfa22cea35", - "9b96b41463ca49cb8764a3e7313c5a74", - "3f4aa56b130b4fa3a54ab8fe1964b53d", - "d94e88f88cb14d1689b1dc41843819ca", - "c371ed0f14ba4f5ca7a7a470ec70937b", - "61c628d441cc45b1bd2ce253efd4db2d", - "1934654ceeff40f5865c76451c16e713", - "faa0a9a04b7f4946a3ca086397bc7491", - "788c98ad384346cfa5aaf595d8becbe7", - "073146f7610943668a95c06216de2244", - "db773d2698974aaf8d9c033e5f365139", - "e9e84713985a461e9d30b3bb49a053df", - "dd3fc30285c54f978773408300b8a897", - "21d1d94e7918434f901fb020f4235db3", - "64dd7a4dd56f4ccd8dd7bee820fd8e64", - "3daf5dac75a04df190ff020e43681e98", - "21b611c2ca494a0392035a44f7e90699", - "9047ec28829141f8a756e6609f8d8e01" - ] - }, - "id": "cCf4vsXrbczm", - "outputId": "57cf0922-664b-4c97-98d7-d0670299a9a4" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From C:\\Users\\drago\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\tf_keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n", - "\n" - ] - } - ], - "source": [ - "pipeline_classification = pipeline(\"text-classification\", model=\"cardiffnlp/twitter-roberta-base-irony\") # cardiffnlp/twitter-roberta-base-irony, SamLowe/roberta-base-go_emotions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have the model we can pass it a string and have it give us a classification.\n", - "\n", - "Feel free to experiment with different sentences by changing the contents of the variable text" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Uhf7MYboRdEy", - "outputId": "50c58e9b-63a0-41e1-93f9-490ebc249306" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[{'label': 'irony', 'score': 0.9424387812614441}, {'label': 'non_irony', 'score': 0.057561274617910385}]\n" - ] - } - ], - "source": [ - "text = \"Well that workshop was totally worth my time...\" # \"Well that workshop was totally worth my time...\" \"This smells weird, I'm not sure if I should eat this ... Yikes, it tasted like old socks!\"\n", - "output = pipeline_classification(text, top_k=10)\n", - "print(output)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's make the output a little cleaner" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "zwtj5Lm8cdh8", - "outputId": "b7d92ba0-4559-411a-a6a6-53ab58570a45" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " label score\n", - "0 irony 0.942439\n", - "1 non_irony 0.057561\n" - ] - } - ], - "source": [ - "# make output a bit cleaner\n", - "df_output = pd.DataFrame(output)\n", - "print(df_output)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As you can see, in a few lines of code and by leveraging an existing model we can classify text as ironic or non ironic. Now you have one more tool in your machine learning toolbox.\n", - "\n", - "Remember that : 'when you only have a hammer everything is a nail'. But if we want to build a house (perform machine learning the right way), we need to use the right tool for the right job." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "HIOYqJZ9kOtk" - }, - "source": [ - "#### 2.1.2 Machine Translation" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Vk69-EpwvlbH" - }, - "source": [ - "* Open source machine translation (MT) models enable you to translate between many different languages without Google Translate.\n", - "* [University of Helsinki](https://huggingface.co/Helsinki-NLP) uploaded models for more than 1000 language pairs to the Hugging Face hub\n", - "* [Facebook AI](https://huggingface.co/models?search=facebook+m2m) open-sourced several multi-lingual models\n", - "* The [EasyNMT library](https://github.com/UKPLab/EasyNMT), provides an easy wrapper for all these models\n", - "* Most machine translation models translate between two languages in one direction (e.g. German to English, but not English to German), some can translate in multiple directions.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 241, - "referenced_widgets": [ - "2f5c8e94dfe44bcebb75a1197b3a9bfa", - "a57cf97a54e94dd99dd8e226bff56c2f", - "3f537cd55b94435aa32e3d038397ad2b", - "5aed25c268754384839b12837f5e85b5", - "391a5c22b3904dfe89bb0effcf01241c", - "28b6e0e3d01e4556aa3041bf4cd73ed1", - "68b9a4dd734f4a4280e5e9bd30e24551", - "440bb6e332e14272b1dc95c3af838b48", - "6e302ad5ebf44174af7e7506182811b7", - "91de6e5d78144aa5b3d119f03133e00a", - "d037896d028448769eba5ab37e218b1d", - "6aebd2f267814a0faecf37c1617ceaeb", - "67fde9f7a4d848c2a576f592536b1ceb", - "ab53ecee8c6448ceab8f67a34160e23e", - "4cbc6d57ce014b6d84705bb964934436", - "85ec6ca28a464beca85d16d5de8ff0d3", - "29ac6b86ee4e4517b81408fb291a183a", - "518ef0bf5a86406da3c173ab8fa8079f", - "68bdad9fd1e84cc3aa9ab615341a5f96", - "912d3c4e56b24b17af9205c1fe80c715", - "04ca56e052f34b7cb07189e1689c224e", - "701e50dd97f24f68a9743ab561326d04", - "d38e6ddb21914797ba6cac510b8a229b", - "f0f70053ac464064955a3297d49a5bec", - "c3e6a161e7d4467fb637c1412a0c0643", - "3ded4c4a353b4390b968c634abdedd8d", - "fda53efa67a74043a2f8a3a11ef65e7b", - "855e15bc89754006b47c965668f54205", - "2602a4b8ea3c4de895c2b2db2229cd78", - "acfe04db9fb54aeebf786ebcf4375e8b", - "4d3b697c0478481c98e9f973ded10687", - "cad10104c1904c4c8ee51db156126f5f", - "ca2c260b9e6e4c55af080d718850b287", - "b1151ea3a329493588a553e7e03a4a02", - "53046705c369469bb5601f37e67832f6", - "d44fea2d74464ac8a371daedc3c310d3", - "d8a6b3fb619b46eba071ac684470783f", - "25e7cf64940c4b38aaf29629881aa2bf", - "8e1181eb402643bd9732576f25766b1b", - "aec13b83dd214c9092223ec66381f801", - "bc67c1fc111543f2817ad3fa2d741b5e", - "e1947d1e1928436ab8f2f716d35a57a3", - "65e84bbdcc954b14bd60083d46b0a1bf", - "729f20b03e4b4dd4b8b4f54cb676a23d", - "afee0c08cf544df6ba138383a0ff5e49", - "a51c7d9053a544f68d4438d1423fc991", - "eefc4673f6e94148b7fe0efb51fd0d1b", - "a6f863a7b035488abedb19e42b442be7", - "de393632239344b8946d41322c3ce094", - "e3d522f5dc304826932b86ae77fd3421", - "4a1f8c64bad74023a0b8f8db4f030ca2", - "b75cd004712140f5b634b989a91fe116", - "7502e4ec759c482f87ac8e027a555dfb", - "c9e3ee3234394f3698be0dca9b018b23", - "e248f61759554536ab45eb0299e14ace", - "d0f64bb328124b4e9f5e07b61063ea3e", - "ebd8199ffad448e4a270c1c013444ed9", - "6ef4ed488e514bc19363c5d2bac79dc2", - "1842a963f5624e4e8b0cb48c81ea620e", - "92019a375b2f4dd390466a57cb4c6a2a", - "4e38260d5a804f0496b8a7ac275b86f9", - "fa184e9da52e43acb21b7078fc0a7472", - "3c8b928c017d489e946701e3a133a848", - "78e0617aecd740f48126f4f16ac8e1ed", - "2c52df20fe8a485c81dc412a2e4aa5f5", - "a88aea175f2844af85e1d18c572a367b", - "ef296c595f254ad0829cfe1bb6e3236b", - "562dec60b33e44a682af4a3f1573c97c", - "414f937dc66e4bada91ce82e41fb6b03", - "5ee3d15794cc4e1292a6b7c05781a4be", - "c3514b0ab7664283b1f526206ce085ea", - "721574ef512f4660a7612cb37f62d37b", - "29e97a481d614f9a964cae3d93b6815d", - "313983f79c4b4ff7b1f4ac62361ebe0e", - "eb2a3c8c31194102b79eb55c2df07dde", - "090338f4d17e4b489212323a951c8e30", - "52d305e72ed04bdeac82b12caf3d1e7b" - ] - }, - "id": "hKtdnG1FkRQt", - "outputId": "6bde9a22-0683-447c-f614-10ef5e298c91" - }, - "outputs": [], - "source": [ - "# translation pipeline docs: https://huggingface.co/transformers/main_classes/pipelines.html#transformers.TranslationPipeline\n", - "pipeline_translate = pipeline(\"translation\", model=\"facebook/m2m100_418M\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation.\n", - "\n", - "The model that can directly translate between the 9,900 directions of 100 languages.\n", - "\n", - "Here we specify to translate from German 'de' to English 'en'" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "p7pra41KlFwf", - "outputId": "d5513401-2879-42ca-d2c7-31d006e16ffe" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'translation_text': 'I am a fish'}]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "text = \"Ich bin ein Fisch\"\n", - "pipeline_translate(text, src_lang=\"de\", tgt_lang=\"en\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's do the same but with and entire wikipedia page in german." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "v7w6BRcOsjt7", - "outputId": "9d4e075b-41b4-4032-af44-b980705b3156" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Original text:\n", - "Donald John Trump [ˈdɑn.əld dʒɑn tɹɐmp] (* 14. Juni 1946 in New York City) ist ein US-amerikanischer Unternehmer, Entertainer und Politiker der Republikanischen Partei. Von 2017 bis 2021 war er der 45. Präsident der Vereinigten Staaten. Er gilt als einer der umstrittensten Politiker der US-Geschichte und ist der erst\n", - "\n", - "Translated text:\n", - "Donald John Trump [ˈdɑn.əld dʒɑn tɔmp] (* 14 June 1946 in New York City) is an American entrepreneur, entertainer and politician of the Republican Party. From 2017 to 2021 he was the 45th President of the United States.\n" - ] - } - ], - "source": [ - "# download any text from wikipedia, via https://pypi.org/project/wikipedia/\n", - "import wikipedia\n", - "wikipedia.set_lang(\"de\")\n", - "\n", - "text = wikipedia.summary(\"Donald Trump\").replace('\\n', ' ')[:318]\n", - "print(f\"Original text:\\n{text}\\n\")\n", - "\n", - "# translate the text from wikipedia\n", - "text_translated = pipeline_translate(text, src_lang=\"de\", tgt_lang=\"en\")\n", - "print(f\"Translated text:\\n{text_translated[0]['translation_text']}\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "YK9-BIv-WdyR" - }, - "source": [ - "#### 2.1.3 Text Summarization" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 177, - "referenced_widgets": [ - "068e468f6e0f4d5c98b9c414ce729aee", - "919d20a873f24792b1883b391c39194a", - "753a3586c0714a52a6aedf84313242cf", - 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entity_groupscorewordstartend
0PER0.989843donald john trump017
1MISC0.991848american4553
2LOC0.992675united states141154
3PER0.989312trump174179
4ORG0.697325university of pennsylvania233259
5PER0.969404trump337342
6ORG0.712140trump organization358376
7PER0.913634trump597602
8MISC0.953361the apprentice684698
9PER0.992383trump828833
10MISC0.873182republican876886
11MISC0.978373democratic909919
12PER0.994271hillary clinton936951
13LOC0.997165russia10321038
14PER0.982982trump10791084
15LOC0.849478u. s.12741278
16PER0.986752trump13381343
17MISC0.994762american14851493
18PER0.980122trump16221627
19MISC0.987600muslim16741680
20LOC0.977429u. s.17571761
21LOC0.997740mexico17621768
22MISC0.891977tax cuts and jobs act of19041928
23MISC0.960363affordable care act20212040
24PER0.996589neil gorsuch20552067
25PER0.997994brett kavanaugh20692084
26PER0.997711amy coney barrett20902107
27LOC0.901173u. s.21152119
28ORG0.821682supreme court21202133
29MISC0.937786covid - 1921602168
30PER0.980800trump23592364
31LOC0.996960china23922397
32LOC0.991840u. s24152418
33MISC0.908778trans - pacific partnership24382463
34MISC0.889306paris agreement24852500
35MISC0.697912climate change25042518
36LOC0.906911iran25282532
37MISC0.987541north korean25592571
38PER0.993055kim jong un25792590
39PER0.989296trump26452650
40LOC0.874581u. s.26632667
\n", - "" - ], - "text/plain": [ - " entity_group score word start end\n", - "0 PER 0.989843 donald john trump 0 17\n", - "1 MISC 0.991848 american 45 53\n", - "2 LOC 0.992675 united states 141 154\n", - "3 PER 0.989312 trump 174 179\n", - "4 ORG 0.697325 university of pennsylvania 233 259\n", - "5 PER 0.969404 trump 337 342\n", - "6 ORG 0.712140 trump organization 358 376\n", - "7 PER 0.913634 trump 597 602\n", - "8 MISC 0.953361 the apprentice 684 698\n", - "9 PER 0.992383 trump 828 833\n", - "10 MISC 0.873182 republican 876 886\n", - "11 MISC 0.978373 democratic 909 919\n", - "12 PER 0.994271 hillary clinton 936 951\n", - "13 LOC 0.997165 russia 1032 1038\n", - "14 PER 0.982982 trump 1079 1084\n", - "15 LOC 0.849478 u. s. 1274 1278\n", - "16 PER 0.986752 trump 1338 1343\n", - "17 MISC 0.994762 american 1485 1493\n", - "18 PER 0.980122 trump 1622 1627\n", - "19 MISC 0.987600 muslim 1674 1680\n", - "20 LOC 0.977429 u. s. 1757 1761\n", - "21 LOC 0.997740 mexico 1762 1768\n", - "22 MISC 0.891977 tax cuts and jobs act of 1904 1928\n", - "23 MISC 0.960363 affordable care act 2021 2040\n", - "24 PER 0.996589 neil gorsuch 2055 2067\n", - "25 PER 0.997994 brett kavanaugh 2069 2084\n", - "26 PER 0.997711 amy coney barrett 2090 2107\n", - "27 LOC 0.901173 u. s. 2115 2119\n", - "28 ORG 0.821682 supreme court 2120 2133\n", - "29 MISC 0.937786 covid - 19 2160 2168\n", - "30 PER 0.980800 trump 2359 2364\n", - "31 LOC 0.996960 china 2392 2397\n", - "32 LOC 0.991840 u. s 2415 2418\n", - "33 MISC 0.908778 trans - pacific partnership 2438 2463\n", - "34 MISC 0.889306 paris agreement 2485 2500\n", - "35 MISC 0.697912 climate change 2504 2518\n", - "36 LOC 0.906911 iran 2528 2532\n", - "37 MISC 0.987541 north korean 2559 2571\n", - "38 PER 0.993055 kim jong un 2579 2590\n", - "39 PER 0.989296 trump 2645 2650\n", - "40 LOC 0.874581 u. s. 2663 2667" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import wikipedia\n", - "wikipedia.set_lang(\"en\")\n", - "\n", - "text_long = wikipedia.summary(\"Donald Trump\").replace('\\n', ' ')\n", - "\n", - "output = pipeline_ner(text_long)\n", - "\n", - "pd.DataFrame(output)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "TXPa9hHmP8vJ" - }, - "source": [ - "### 2.2. Universal models" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "lEGnV9KabQg4" - }, - "source": [ - "The models mentioned above are designed to excel at a single specific task on a particular dataset. The key advantage of these models is their high performance and accuracy on that specific task and dataset. \n", - "\n", - "However, in real-world applications, the problems you'll face often require solving slightly different tasks, possibly with varied category definitions or applied to different types of texts.\n", - "\n", - "Universal models can help address this challenge. Although they also focus on one task, the task is general or universal enough that many other tasks can be reformulated into it. Two examples of universal tasks are:\n", - "\n", - "- Natural Language Inference (NLI): A task that can effectively solve a wide range of classification tasks by determining whether a given premise supports, contradicts, or is neutral with respect to a hypothesis.\n", - "\n", - "- Token Generation: An even more universal task that can be applied to solve virtually any text-related task, including translation, summarization, and text completion.\n", - "\n", - "These universal tasks enable the models to be versatile and adaptable to various problems beyond the specific ones they were initially trained on." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ocJsfbLcQPpO" - }, - "source": [ - "#### Zero-shot classification\n", - "\n", - "\n", - "Zero-shot classification is a technique where a model can categorize data into classes it has never seen before. \n", - "\n", - "Instead of relying on labeled examples for each class, the model understands the relationship between the input and the class descriptions, allowing it to make accurate predictions without needing specific training on those classes." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 265, - "referenced_widgets": [ - "e5ef7ba446bd41978c0cee3b31705b80", - "871968923aa54eaca0eadf0dd248b3dd", - "9cd1788b3fa04c868922c36426dee402", - "67f3625d56a84093896df4668e88477d", - "8b78ca4c4c4942debb95b2183eabf650", - "cef8174b4c614e7e91ac179bb94ae1cb", - "3306fcb3d7834eff9a9686b5e61514b9", - "49c2def2f2204294a2ab449705a93b6e", - "06b33973b2814e71af98e08bcec341c0", - "95d4a8d3562e46f890e7e19199f87195", - "609dd568b69741f897cf6fb218745753", - "cadb35b6786a4fc7875806d00d8c6bc1", - "9ad38d4d5ca940f9b317f51e1a8bc42a", - "c69d9e1389264df2b7307ca5e7cc2fc3", - "80fb8979b79241feb13b7d6bd9aa9f15", - "7a0f714ac21447ec9f4b4109b14f5d6a", - "af778561c5a642ffba66c022e16227e5", - "13336ece6361426dac4aa491842a9458", - "30d569b20ae24ca993559b472f3be6f0", - "f69b70a344194acbb4150b96c0ffd349", - "fd310949de2c488390ed73af93dda4bb", - "f885bcf2f4c241318422547a892a9346", - "bc21dbe2f48d4302a230a2e20a9a93f5", - "f90ae50de0df4c6199281e1b26218261", - "04aa9fb0ae534562a228c115a3b46a28", - "c295c2fbf8ed42e0b703f2982ecb59f8", - "6a938b4d73dc4440b2576d479c6cacb7", - "ac04e01423c5423a8c7f62db04a0b391", - "765f6ebecab64d9589da0471b538240d", - "4a07f8bda79b4ae5914f1e141f5afe48", - "d02cac7ca7a547b6a057910105133733", - "ff968b87851d49ddbd05401ecba0baf7", - "f5a963c659ad4e2999d23d8edc49e282", - "b0196114957947ba90e002407357cc5f", - "3e446780420d4afa8f9cfba8075183ee", - "0d20a336e60a4170bfabfe06023d78f6", - "6230c54ece324f43818dfbeb28593daf", - "55c73137fb20479c93458b56e318b2e3", - "86a60990aa9b4575ba361be6ae45f380", - "fe011bfc4c104ee8b341374c14a47de6", - "83955802abd64bcb839e9b23785e4039", - "670a75756a294c2685d31ef7a7cae7e6", - "efcf2c2e90114ae8adf2febc6beea6b9", - "7a22215f5939403ba06ca1a87b338866", - "d4bf36adde4b4a7794d5f5551943eac2", - "f2717d41d37b45d8bee661e2361e73f2", - "9050276cb62a411eb2814c841f60fba7", - "1deb472ae9de40af9adeecc110e30887", - "c8111fe90ed641559c8724ecd2a459fd", - "f8904d6993cc446d9b89cbcaacc5e8d7", - "77ce371cb8ac4d79a5042527c204bf8b", - "d1e92c4801914a37ba53f6641e436afa", - "c7a07acf37cc4e159439711e13988ae8", - "33660a794c034cfcb7eae432d32ff221", - "26753ff273ee43c48f0b51b0565e3259", - "e89b45b543664e63b6fb2080ddc5c103", - "96c739b8fda64108a140b2828414489e", - "82bc7264609e40cca9350c3ea9e9753b", - "f086c21e256d4810bb5295f875972866", - "4a2831a6121f4541b650986be10cc951", - "ff0833fa0c124cfa882c3b5748f03b2f", - "5f9f3fadc605486f82da32d6f154abc7", - "41704b3eaea34c6783c34b5937beab77", - "f3bdfaf8f61649649022fd1903ac07e5", - "fe237cce8c4f40fe96eda0d5ed7425ea", - "d802d528db7241bb9ad84ba1c66e927a" - ] - }, - "id": "CQWu07esQPAF", - "outputId": "51eff463-8b26-4536-b6f5-2dcfbcc45a1e" - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "a2f5788e5bcd426e96d8d80cfda33741", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "config.json: 0%| | 0.00/1.07k [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
classprobability
0payment issues0.991133
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\n", - "" - ], - "text/plain": [ - " class probability\n", - "0 payment issues 0.991133\n", - "1 bug report 0.076115\n", - "2 travel advice 0.018696" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "text = \"Customer: I have not received my reimbursement yet. What the hell is going on?\"\n", - "classes = ['payment issues', 'travel advice', 'bug report'] # \"account opening\", \"customer complaint\"\n", - "\n", - "#text = \"I do not think the government is trustworthy anymore. We need to mobilize and resist!\"\n", - "#classes = [\"civil disobedience\", \"praise of the government\", \"travel advice\"] # \"collective action\"\n", - "\n", - "output = pipeline_zeroshot_classification(text, classes, multi_label=True)\n", - "\n", - "pd.DataFrame(data=[output[\"labels\"], output[\"scores\"]], index=[\"class\", \"probability\"]).T\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "JwM1gzfvA-Eb" - }, - "source": [ - "## Exercise\n", - "\n", - "Now it is your turn to go to the hugging face library https://huggingface.co/models?pipeline_tag=text-classification&sort=downloads\n", - "\n", - "(you can select on the left menu of the website the type of NLP tasks you want models to perform)\n", - "\n", - "- Find an NLP model that we have not used previously.\n", - "\n", - "- Get some data from wikipedia or elsewhere.\n", - "\n", - "- Perform inference with the model and print the result!\n", - "\n", - "- Comment your code along the way, describe what your model does and what your end goal is from input to output.\n", - 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