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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "8JqpxyBueqTH",
"outputId": "6c2c3908-9067-496c-ad64-74f21895232a"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Building wheel for flashtext (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting git+https://github.com/boudinfl/pke.git\n",
" Cloning https://github.com/boudinfl/pke.git to /tmp/pip-req-build-s0vst_dk\n",
" Running command git clone -q https://github.com/boudinfl/pke.git /tmp/pip-req-build-s0vst_dk\n",
"Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (from pke==2.0.0) (3.7)\n",
"Requirement already satisfied: networkx in /usr/local/lib/python3.7/dist-packages (from pke==2.0.0) (2.6.3)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from pke==2.0.0) (1.21.6)\n",
"Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from pke==2.0.0) (1.7.3)\n",
"Collecting sklearn\n",
" Downloading sklearn-0.0.post1.tar.gz (3.6 kB)\n",
"Collecting unidecode\n",
" Downloading Unidecode-1.3.6-py3-none-any.whl (235 kB)\n",
"\u001b[K |████████████████████████████████| 235 kB 6.2 MB/s \n",
"\u001b[?25hRequirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from pke==2.0.0) (0.16.0)\n",
"Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from pke==2.0.0) (1.2.0)\n",
"Requirement already satisfied: spacy>=3.2.3 in /usr/local/lib/python3.7/dist-packages (from pke==2.0.0) (3.4.3)\n",
"Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (2.0.7)\n",
"Requirement already satisfied: typing-extensions<4.2.0,>=3.7.4 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (4.1.1)\n",
"Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (1.0.3)\n",
"Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (57.4.0)\n",
"Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.10 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (3.0.10)\n",
"Requirement already satisfied: wasabi<1.1.0,>=0.9.1 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (0.10.1)\n",
"Requirement already satisfied: typer<0.8.0,>=0.3.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (0.7.0)\n",
"Requirement already satisfied: thinc<8.2.0,>=8.1.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (8.1.5)\n",
"Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (2.4.5)\n",
"Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (3.0.8)\n",
"Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (4.64.1)\n",
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (21.3)\n",
"Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (1.0.9)\n",
"Requirement already satisfied: pathy>=0.3.5 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (0.8.1)\n",
"Requirement already satisfied: pydantic!=1.8,!=1.8.1,<1.11.0,>=1.7.4 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (1.10.2)\n",
"Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (2.23.0)\n",
"Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (3.3.0)\n",
"Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (2.0.8)\n",
"Requirement already satisfied: jinja2 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (2.11.3)\n",
"Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from catalogue<2.1.0,>=2.0.6->spacy>=3.2.3->pke==2.0.0) (3.10.0)\n",
"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->spacy>=3.2.3->pke==2.0.0) (3.0.9)\n",
"Requirement already satisfied: smart-open<6.0.0,>=5.2.1 in /usr/local/lib/python3.7/dist-packages (from pathy>=0.3.5->spacy>=3.2.3->pke==2.0.0) (5.2.1)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=3.2.3->pke==2.0.0) (2.10)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=3.2.3->pke==2.0.0) (2022.9.24)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=3.2.3->pke==2.0.0) (3.0.4)\n",
"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<3.0.0,>=2.13.0->spacy>=3.2.3->pke==2.0.0) (1.24.3)\n",
"Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.7/dist-packages (from thinc<8.2.0,>=8.1.0->spacy>=3.2.3->pke==2.0.0) (0.0.3)\n",
"Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.7/dist-packages (from thinc<8.2.0,>=8.1.0->spacy>=3.2.3->pke==2.0.0) (0.7.9)\n",
"Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.7/dist-packages (from typer<0.8.0,>=0.3.0->spacy>=3.2.3->pke==2.0.0) (7.1.2)\n",
"Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->spacy>=3.2.3->pke==2.0.0) (2.0.1)\n",
"Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.7/dist-packages (from nltk->pke==2.0.0) (2022.6.2)\n",
"Building wheels for collected packages: pke, sklearn\n",
" Building wheel for pke (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for pke: filename=pke-2.0.0-py3-none-any.whl size=6160276 sha256=6967c9216d570e0bbc7bab2c16f5f1810ecd62dcc9fad636e26ff35edbab3a68\n",
" Stored in directory: /tmp/pip-ephem-wheel-cache-_mu5g7sn/wheels/fa/b3/09/612ee93bf3ee4164bcd5783e742942cdfc892a86039d3e0a33\n",
" Building wheel for sklearn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for sklearn: filename=sklearn-0.0.post1-py3-none-any.whl size=2344 sha256=47f5287c3e5d1518e0617e1db17d093069e553338d6c0e359aa70352e6c78d66\n",
" Stored in directory: /root/.cache/pip/wheels/42/56/cc/4a8bf86613aafd5b7f1b310477667c1fca5c51c3ae4124a003\n",
"Successfully built pke sklearn\n",
"Installing collected packages: unidecode, sklearn, pke\n",
"Successfully installed pke-2.0.0 sklearn-0.0.post1 unidecode-1.3.6\n"
]
}
],
"source": [
"!pip install --quiet flashtext==2.7\n",
"!pip install git+https://github.com/boudinfl/pke.git\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "am3XUlr5evYK"
},
"outputs": [],
"source": [
"!pip install --quiet transformers==4.8.1\n",
"!pip install --quiet sentencepiece==0.1.95\n",
"!pip install --quiet textwrap3==0.9.2\n",
"!pip install gradio"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "mhwpLyuBfFUK",
"outputId": "dc6f4900-429d-4815-c98c-b8625efcbe7b"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[?25l\r\u001b[K |███████▊ | 10 kB 27.7 MB/s eta 0:00:01\r\u001b[K |███████████████▌ | 20 kB 34.6 MB/s eta 0:00:01\r\u001b[K |███████████████████████▏ | 30 kB 15.4 MB/s eta 0:00:01\r\u001b[K |███████████████████████████████ | 40 kB 6.6 MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 42 kB 955 kB/s \n",
"\u001b[?25h"
]
}
],
"source": [
"!pip install --quiet strsim==0.0.3\n",
"!pip install --quiet sense2vec==2.0.0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "NcNXz17EfQLJ",
"outputId": "c90851f7-e320-48e3-d994-fcc5c174c636"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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0:00:01\r\u001b[K |████████████████████████████████| 1.6 MB 7.5 MB/s \n",
"\u001b[?25htime: 506 µs (started: 2022-11-24 06:06:09 +00:00)\n"
]
}
],
"source": [
"!pip install --quiet ipython-autotime\n",
"%load_ext autotime"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "Bijc_hfbfUwp",
"outputId": "54a7f895-8f08-452d-8f3a-8e5310a1aa6c"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[K |████████████████████████████████| 85 kB 3.9 MB/s \n",
"\u001b[K |████████████████████████████████| 182 kB 49.1 MB/s \n",
"\u001b[K |████████████████████████████████| 5.5 MB 54.9 MB/s \n",
"\u001b[K |████████████████████████████████| 7.6 MB 55.0 MB/s \n",
"\u001b[?25h Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
"time: 10.4 s (started: 2022-11-24 06:06:09 +00:00)\n"
]
}
],
"source": [
"!pip install --quiet sentence-transformers==2.2.2"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bmVx9L0yfgvR"
},
"source": [
"The below code restarts the colab notebook. Once it is restarted continue from next section and no need to run this section (installation) again."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "uPO9U__1fZWh",
"outputId": "31e8d745-2a88-4bd6-f136-55cd2147ee3f"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"time: 556 µs (started: 2022-11-24 06:06:20 +00:00)\n"
]
}
],
"source": [
"# import os\n",
"# os.kill(os.getpid(), 9)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "POh2_zvgrk0h"
},
"source": [
"## Example 1"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VJP4CDBBrnNY"
},
"source": [
"Text taken from: \n",
"https://gadgets.ndtv.com/internet/news/dogecoin-price-rally-surge-elon-musk-tweet-twitter-working-developers-improve-transaction-efficiency-2442120"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "P_jlw7MUfjOp",
"outputId": "fd3e08da-3595-445d-941f-2c8047e34f08"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Elon Musk has shown again he can influence the digital currency market with just his tweets. After saying that his electric vehicle-making company\n",
"Tesla will not accept payments in Bitcoin because of environmental concerns, he tweeted that he was working with developers of Dogecoin to improve\n",
"system transaction efficiency. Following the two distinct statements from him, the world's largest cryptocurrency hit a two-month low, while Dogecoin\n",
"rallied by about 20 percent. The SpaceX CEO has in recent months often tweeted in support of Dogecoin, but rarely for Bitcoin. In a recent tweet,\n",
"Musk put out a statement from Tesla that it was “concerned” about the rapidly increasing use of fossil fuels for Bitcoin (price in India) mining and\n",
"transaction, and hence was suspending vehicle purchases using the cryptocurrency. A day later he again tweeted saying, “To be clear, I strongly\n",
"believe in crypto, but it can't drive a massive increase in fossil fuel use, especially coal”. It triggered a downward spiral for Bitcoin value but\n",
"the cryptocurrency has stabilised since. A number of Twitter users welcomed Musk's statement. One of them said it's time people started realising\n",
"that Dogecoin “is here to stay” and another referred to Musk's previous assertion that crypto could become the world's future currency.\n",
"\n",
"\n",
"time: 18.8 ms (started: 2022-11-24 06:06:20 +00:00)\n"
]
}
],
"source": [
"from textwrap3 import wrap\n",
"\n",
"text = \"\"\"Elon Musk has shown again he can influence the digital currency market with just his tweets. After saying that his electric vehicle-making company\n",
"Tesla will not accept payments in Bitcoin because of environmental concerns, he tweeted that he was working with developers of Dogecoin to improve\n",
"system transaction efficiency. Following the two distinct statements from him, the world's largest cryptocurrency hit a two-month low, while Dogecoin\n",
"rallied by about 20 percent. The SpaceX CEO has in recent months often tweeted in support of Dogecoin, but rarely for Bitcoin. In a recent tweet,\n",
"Musk put out a statement from Tesla that it was “concerned” about the rapidly increasing use of fossil fuels for Bitcoin (price in India) mining and\n",
"transaction, and hence was suspending vehicle purchases using the cryptocurrency. A day later he again tweeted saying, “To be clear, I strongly\n",
"believe in crypto, but it can't drive a massive increase in fossil fuel use, especially coal”. It triggered a downward spiral for Bitcoin value but\n",
"the cryptocurrency has stabilised since. A number of Twitter users welcomed Musk's statement. One of them said it's time people started realising\n",
"that Dogecoin “is here to stay” and another referred to Musk's previous assertion that crypto could become the world's future currency.\"\"\"\n",
"\n",
"for wrp in wrap(text, 150):\n",
" print (wrp)\n",
"print (\"\\n\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ShPNEZz8u7s6"
},
"source": [
"# **Summarization with T5**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true,
"referenced_widgets": [
"c9c2e5d5824345f780befcf11d6ff946",
"c39b4e7e424d4f64a8fb25495f8c7026",
"543714c7a41a4429a57a069bc2eca1dc"
]
},
"id": "H1eIU521rrn5",
"outputId": "d3bb1402-1cba-4881-b05f-b8e24bb19278"
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c9c2e5d5824345f780befcf11d6ff946",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading: 0%| | 0.00/1.20k [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c39b4e7e424d4f64a8fb25495f8c7026",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading: 0%| | 0.00/892M [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "543714c7a41a4429a57a069bc2eca1dc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading: 0%| | 0.00/792k [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/transformers/models/t5/tokenization_t5.py:174: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.\n",
"For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.\n",
"- Be aware that you SHOULD NOT rely on t5-base automatically truncating your input to 512 when padding/encoding.\n",
"- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.\n",
"- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.\n",
" FutureWarning,\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"time: 30.6 s (started: 2022-11-24 06:06:20 +00:00)\n"
]
}
],
"source": [
"import torch\n",
"from transformers import T5ForConditionalGeneration,T5Tokenizer\n",
"summary_model = T5ForConditionalGeneration.from_pretrained('t5-base')\n",
"summary_tokenizer = T5Tokenizer.from_pretrained('t5-base')\n",
"\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"summary_model = summary_model.to(device)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "8mVsjMPTu-bj",
"outputId": "e0ac198d-4625-4f8f-a2fd-9968c0a5a72d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"time: 1.03 ms (started: 2022-11-24 06:06:50 +00:00)\n"
]
}
],
"source": [
"import random\n",
"import numpy as np\n",
"\n",
"def set_seed(seed: int):\n",
" random.seed(seed)\n",
" np.random.seed(seed)\n",
" torch.manual_seed(seed)\n",
" torch.cuda.manual_seed_all(seed)\n",
"\n",
"set_seed(42)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "Gh2Xc5JRvQDp",
"outputId": "c1198166-2a2b-4571-b831-3ed1a8705c9e"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package punkt to /root/nltk_data...\n",
"[nltk_data] Unzipping tokenizers/punkt.zip.\n",
"[nltk_data] Downloading package brown to /root/nltk_data...\n",
"[nltk_data] Unzipping corpora/brown.zip.\n",
"[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"original Text >>\n",
"Elon Musk has shown again he can influence the digital currency market with just his tweets. After saying that his electric vehicle-making company\n",
"Tesla will not accept payments in Bitcoin because of environmental concerns, he tweeted that he was working with developers of Dogecoin to improve\n",
"system transaction efficiency. Following the two distinct statements from him, the world's largest cryptocurrency hit a two-month low, while Dogecoin\n",
"rallied by about 20 percent. The SpaceX CEO has in recent months often tweeted in support of Dogecoin, but rarely for Bitcoin. In a recent tweet,\n",
"Musk put out a statement from Tesla that it was “concerned” about the rapidly increasing use of fossil fuels for Bitcoin (price in India) mining and\n",
"transaction, and hence was suspending vehicle purchases using the cryptocurrency. A day later he again tweeted saying, “To be clear, I strongly\n",
"believe in crypto, but it can't drive a massive increase in fossil fuel use, especially coal”. It triggered a downward spiral for Bitcoin value but\n",
"the cryptocurrency has stabilised since. A number of Twitter users welcomed Musk's statement. One of them said it's time people started realising\n",
"that Dogecoin “is here to stay” and another referred to Musk's previous assertion that crypto could become the world's future currency.\n",
"\n",
"\n",
"Summarized Text >>\n",
"Musk tweeted that his electric vehicle-making company tesla will not accept payments in bitcoin because of environmental concerns. He also said that\n",
"the company was working with developers of dogecoin to improve system transaction efficiency. The world's largest cryptocurrency hit a two-month low,\n",
"while doge coin rallied by about 20 percent. Musk has in recent months often tweeted in support of crypto, but rarely for bitcoin.\n",
"\n",
"\n",
"time: 6.14 s (started: 2022-11-24 06:06:50 +00:00)\n"
]
}
],
"source": [
"import nltk\n",
"nltk.download('punkt')\n",
"nltk.download('brown')\n",
"nltk.download('wordnet')\n",
"from nltk.corpus import wordnet as wn\n",
"from nltk.tokenize import sent_tokenize\n",
"\n",
"def postprocesstext (content):\n",
" final=\"\"\n",
" for sent in sent_tokenize(content):\n",
" sent = sent.capitalize()\n",
" final = final +\" \"+sent\n",
" return final\n",
"\n",
"\n",
"def summarizer(text,model,tokenizer):\n",
" text = text.strip().replace(\"\\n\",\" \")\n",
" text = \"summarize: \"+text\n",
" # print (text)\n",
" max_len = 512\n",
" encoding = tokenizer.encode_plus(text,max_length=max_len, pad_to_max_length=False,truncation=True, return_tensors=\"pt\").to(device)\n",
"\n",
" input_ids, attention_mask = encoding[\"input_ids\"], encoding[\"attention_mask\"]\n",
"\n",
" outs = model.generate(input_ids=input_ids,\n",
" attention_mask=attention_mask,\n",
" early_stopping=True,\n",
" num_beams=3,\n",
" num_return_sequences=1,\n",
" no_repeat_ngram_size=2,\n",
" min_length = 75,\n",
" max_length=300)\n",
"\n",
"\n",
" dec = [tokenizer.decode(ids,skip_special_tokens=True) for ids in outs]\n",
" summary = dec[0]\n",
" summary = postprocesstext(summary)\n",
" summary= summary.strip()\n",
"\n",
" return summary\n",
"\n",
"\n",
"summarized_text = summarizer(text,summary_model,summary_tokenizer)\n",
"\n",
"\n",
"print (\"\\noriginal Text >>\")\n",
"for wrp in wrap(text, 150):\n",
" print (wrp)\n",
"print (\"\\n\")\n",
"print (\"Summarized Text >>\")\n",
"for wrp in wrap(summarized_text, 150):\n",
" print (wrp)\n",
"print (\"\\n\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JvBHu5eXv_wp"
},
"source": [
"# **Answer Span Extraction (Keywords and Noun Phrases)**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "84DxJGFn4MfD",
"outputId": "27c39b58-dcaa-4b92-ff9e-0da292be34d9"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
"[nltk_data] Unzipping corpora/stopwords.zip.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"time: 8.23 s (started: 2022-11-24 06:06:56 +00:00)\n"
]
}
],
"source": [
"import nltk\n",
"nltk.download('stopwords')\n",
"from nltk.corpus import stopwords\n",
"import string\n",
"import pke\n",
"import traceback\n",
"\n",
"def get_nouns_multipartite(content):\n",
" out=[]\n",
" try:\n",
" extractor = pke.unsupervised.MultipartiteRank()\n",
" extractor.load_document(input=content,language='en')\n",
" # not contain punctuation marks or stopwords as candidates.\n",
" pos = {'PROPN','NOUN'}\n",
" #pos = {'PROPN','NOUN'}\n",
" stoplist = list(string.punctuation)\n",
" stoplist += ['-lrb-', '-rrb-', '-lcb-', '-rcb-', '-lsb-', '-rsb-']\n",
" stoplist += stopwords.words('english')\n",
" # extractor.candidate_selection(pos=pos, stoplist=stoplist)\n",
" extractor.candidate_selection(pos=pos)\n",
" # 4. build the Multipartite graph and rank candidates using random walk,\n",
" # alpha controls the weight adjustment mechanism, see TopicRank for\n",
" # threshold/method parameters.\n",
" extractor.candidate_weighting(alpha=1.1,\n",
" threshold=0.75,\n",
" method='average')\n",
" keyphrases = extractor.get_n_best(n=15)\n",
" \n",
"\n",
" for val in keyphrases:\n",
" out.append(val[0])\n",
" except:\n",
" out = []\n",
" traceback.print_exc()\n",
"\n",
" return out"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "E8LNRzDVwDbp",
"outputId": "c2ae2bda-8250-4e82-ed71-d10568251e68"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"keywords unsummarized: ['elon musk', 'dogecoin', 'bitcoin', 'statements', 'use', 'cryptocurrency', 'tesla', 'tweets', 'musk', 'system transaction efficiency', 'currency market', 'world', 'price', 'payments', 'company']\n",
"keywords_found in summarized: ['world', 'dogecoin', 'musk', 'cryptocurrency', 'system transaction efficiency', 'payments', 'company', 'bitcoin', 'tesla']\n",
"['dogecoin', 'bitcoin', 'cryptocurrency', 'tesla', 'musk', 'system transaction efficiency', 'world', 'payments', 'company']\n",
"time: 785 ms (started: 2022-11-24 06:07:05 +00:00)\n"
]
}
],
"source": [
"from flashtext import KeywordProcessor\n",
"\n",
"\n",
"def get_keywords(originaltext,summarytext):\n",
" keywords = get_nouns_multipartite(originaltext)\n",
" print (\"keywords unsummarized: \",keywords)\n",
" keyword_processor = KeywordProcessor()\n",
" for keyword in keywords:\n",
" keyword_processor.add_keyword(keyword)\n",
"\n",
" keywords_found = keyword_processor.extract_keywords(summarytext)\n",
" keywords_found = list(set(keywords_found))\n",
" print (\"keywords_found in summarized: \",keywords_found)\n",
"\n",
" important_keywords =[]\n",
" for keyword in keywords:\n",
" if keyword in keywords_found:\n",
" important_keywords.append(keyword)\n",
"\n",
" return important_keywords[:10]\n",
"\n",
"\n",
"imp_keywords = get_keywords(text,summarized_text)\n",
"print (imp_keywords)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true,
"referenced_widgets": [
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"id": "m44RM44OwGzR",
"outputId": "ca45cae8-a813-4425-9adc-3d8e0f886324"
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"output_type": "stream",
"text": [
"time: 35.2 s (started: 2022-11-24 06:07:05 +00:00)\n"
]
}
],
"source": [
"question_model = T5ForConditionalGeneration.from_pretrained('ramsrigouthamg/t5_squad_v1')\n",
"question_tokenizer = T5Tokenizer.from_pretrained('ramsrigouthamg/t5_squad_v1')\n",
"question_model = question_model.to(device)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "1usLabLu5DUB",
"outputId": "69d364b6-ee46-46d2-ee22-19b1fe5b2411"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Musk tweeted that his electric vehicle-making company tesla will not accept payments in bitcoin because of environmental concerns. He also said that\n",
"the company was working with developers of dogecoin to improve system transaction efficiency. The world's largest cryptocurrency hit a two-month low,\n",
"while doge coin rallied by about 20 percent. Musk has in recent months often tweeted in support of crypto, but rarely for bitcoin.\n",
"\n",
"\n",
"What did Musk say he was working with to improve system transaction efficiency?\n",
"Dogecoin\n",
"\n",
"\n",
"What cryptocurrency did Musk rarely tweet about?\n",
"Bitcoin\n",
"\n",
"\n",
"What has Musk often tweeted in support of?\n",
"Cryptocurrency\n",
"\n",
"\n",
"What company did Musk say would not accept bitcoin payments?\n",
"Tesla\n",
"\n",
"\n",
"Who said tesla would not accept bitcoin payments?\n",
"Musk\n",
"\n",
"\n",
"What did Musk want to improve with dogecoin?\n",
"System transaction efficiency\n",
"\n",
"\n",
"What is the largest cryptocurrency?\n",
"World\n",
"\n",
"\n",
"What did Musk say his company would not accept in bitcoin?\n",
"Payments\n",
"\n",
"\n",
"What did Musk say was working with dogecoin developers?\n",
"Company\n",
"\n",
"\n",
"time: 2.78 s (started: 2022-11-24 06:07:41 +00:00)\n"
]
}
],
"source": [
"def get_question(context,answer,model,tokenizer):\n",
" text = \"context: {} answer: {}\".format(context,answer)\n",
" encoding = tokenizer.encode_plus(text,max_length=384, pad_to_max_length=False,truncation=True, return_tensors=\"pt\").to(device)\n",
" input_ids, attention_mask = encoding[\"input_ids\"], encoding[\"attention_mask\"]\n",
"\n",
" outs = model.generate(input_ids=input_ids,\n",
" attention_mask=attention_mask,\n",
" early_stopping=True,\n",
" num_beams=5,\n",
" num_return_sequences=1,\n",
" no_repeat_ngram_size=2,\n",
" max_length=72)\n",
"\n",
"\n",
" dec = [tokenizer.decode(ids,skip_special_tokens=True) for ids in outs]\n",
"\n",
"\n",
" Question = dec[0].replace(\"question:\",\"\")\n",
" Question= Question.strip()\n",
" return Question\n",
"\n",
"\n",
"\n",
"for wrp in wrap(summarized_text, 150):\n",
" print (wrp)\n",
"print (\"\\n\")\n",
"\n",
"for answer in imp_keywords:\n",
" ques = get_question(summarized_text,answer,question_model,question_tokenizer)\n",
" print (ques)\n",
" print (answer.capitalize())\n",
" print (\"\\n\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "4kEuH__G6oDK",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 740
},
"outputId": "8a8b7911-1e79-403e-9601-6f7221fc8bd7"
},
"outputs": [
{
"metadata": {
"tags": null
},
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/gradio/inputs.py:27: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
" \"Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\",\n",
"/usr/local/lib/python3.7/dist-packages/gradio/deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
" warnings.warn(value)\n",
"/usr/local/lib/python3.7/dist-packages/gradio/deprecation.py:40: UserWarning: `numeric` parameter is deprecated, and it has no effect\n",
" warnings.warn(value)\n"
]
},
{
"metadata": {
"tags": null
},
"name": "stdout",
"output_type": "stream",
"text": [
"Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
"Note: opening Chrome Inspector may crash demo inside Colab notebooks.\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"application/javascript": [
"(async (port, path, width, height, cache, element) => {\n",
" if (!google.colab.kernel.accessAllowed && !cache) {\n",
" return;\n",
" }\n",
" element.appendChild(document.createTextNode(''));\n",
" const url = await google.colab.kernel.proxyPort(port, {cache});\n",
"\n",
" const external_link = document.createElement('div');\n",
" external_link.innerHTML = `\n",
" <div style=\"font-family: monospace; margin-bottom: 0.5rem\">\n",
" Running on <a href=${new URL(path, url).toString()} target=\"_blank\">\n",
" https://localhost:${port}${path}\n",
" </a>\n",
" </div>\n",
" `;\n",
" element.appendChild(external_link);\n",
"\n",
" const iframe = document.createElement('iframe');\n",
" iframe.src = new URL(path, url).toString();\n",
" iframe.height = height;\n",
" iframe.allow = \"autoplay; camera; microphone; clipboard-read; clipboard-write;\"\n",
" iframe.width = width;\n",
" iframe.style.border = 0;\n",
" element.appendChild(iframe);\n",
" })(7860, \"/\", \"100%\", 500, false, window.element)"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import gradio as gr\n",
"\n",
"context = gr.inputs.Textbox(lines=10, placeholder=\"Enter paragraph/content here...\")\n",
"output = gr.outputs.HTML( label=\"Question and Answers\")\n",
"\n",
"\n",
"def generate_question(context):\n",
" summary_text = summarizer(context,summary_model,summary_tokenizer)\n",
" for wrp in wrap(summary_text, 150):\n",
" print (wrp)\n",
" np = get_keywords(context,summary_text)\n",
" print (\"\\n\\nNoun phrases\",np)\n",
" output=\"\"\n",
" for answer in np:\n",
" ques = get_question(summary_text,answer,question_model,question_tokenizer)\n",
" # output= output + ques + \"\\n\" + \"Ans: \"+answer.capitalize() + \"\\n\\n\"\n",
" output = output + \"<b style='color:blue;'>\" + ques + \"</b>\"\n",
" output = output + \"<br>\"\n",
" output = output + \"<b style='color:green;'>\" + \"Ans: \" +answer.capitalize()+ \"</b>\"\n",
" output = output + \"<br>\"\n",
"\n",
" summary =\"Summary: \"+ summary_text\n",
" for answer in np:\n",
" summary = summary.replace(answer,\"<b>\"+answer+\"</b>\")\n",
" summary = summary.replace(answer.capitalize(),\"<b>\"+answer.capitalize()+\"</b>\")\n",
" output = output + \"<p>\"+summary+\"</p>\"\n",
" \n",
" return output\n",
"\n",
"iface = gr.Interface(\n",
" fn=generate_question, \n",
" inputs=context, \n",
" outputs=output)\n",
"iface.launch(debug=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dNmJx7QNfLcy"
},
"source": [
"# **Filter keywords with Maximum marginal Relevance**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "zPBj-IUL7L8x"
},
"outputs": [],
"source": [
"!wget https://github.com/explosion/sense2vec/releases/download/v1.0.0/s2v_reddit_2015_md.tar.gz\n",
"!tar -xvf s2v_reddit_2015_md.tar.gz"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "s5RI3fk9fOOz"
},
"outputs": [],
"source": [
"import numpy as np\n",
"from sense2vec import Sense2Vec\n",
"s2v = Sense2Vec().from_disk('s2v_old')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "J2y3unpvfo1y"
},
"outputs": [],
"source": [
"from sentence_transformers import SentenceTransformer\n",
"# paraphrase-distilroberta-base-v1\n",
"sentence_transformer_model = SentenceTransformer('msmarco-distilbert-base-v3')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "pvfmhuWVfsJb"
},
"outputs": [],
"source": [
"from similarity.normalized_levenshtein import NormalizedLevenshtein\n",
"normalized_levenshtein = NormalizedLevenshtein()\n",
"\n",
"def filter_same_sense_words(original,wordlist):\n",
" filtered_words=[]\n",
" base_sense =original.split('|')[1] \n",
" print (base_sense)\n",
" for eachword in wordlist:\n",
" if eachword[0].split('|')[1] == base_sense:\n",
" filtered_words.append(eachword[0].split('|')[0].replace(\"_\", \" \").title().strip())\n",
" return filtered_words\n",
"\n",
"def get_highest_similarity_score(wordlist,wrd):\n",
" score=[]\n",
" for each in wordlist:\n",
" score.append(normalized_levenshtein.similarity(each.lower(),wrd.lower()))\n",
" return max(score)\n",
"\n",
"def sense2vec_get_words(word,s2v,topn,question):\n",
" output = []\n",
" print (\"word \",word)\n",
" try:\n",
" sense = s2v.get_best_sense(word, senses= [\"NOUN\", \"PERSON\",\"PRODUCT\",\"LOC\",\"ORG\",\"EVENT\",\"NORP\",\"WORK OF ART\",\"FAC\",\"GPE\",\"NUM\",\"FACILITY\"])\n",
" most_similar = s2v.most_similar(sense, n=topn)\n",
" # print (most_similar)\n",
" output = filter_same_sense_words(sense,most_similar)\n",
" print (\"Similar \",output)\n",
" except:\n",
" output =[]\n",
"\n",
" threshold = 0.6\n",
" final=[word]\n",
" checklist =question.split()\n",
" for x in output:\n",
" if get_highest_similarity_score(final,x)<threshold and x not in final and x not in checklist:\n",
" final.append(x)\n",
" \n",
" return final[1:]\n",
"\n",
"def mmr(doc_embedding, word_embeddings, words, top_n, lambda_param):\n",
"\n",
" # Extract similarity within words, and between words and the document\n",
" word_doc_similarity = cosine_similarity(word_embeddings, doc_embedding)\n",
" word_similarity = cosine_similarity(word_embeddings)\n",
"\n",
" # Initialize candidates and already choose best keyword/keyphrase\n",
" keywords_idx = [np.argmax(word_doc_similarity)]\n",
" candidates_idx = [i for i in range(len(words)) if i != keywords_idx[0]]\n",
"\n",
" for _ in range(top_n - 1):\n",
" # Extract similarities within candidates and\n",
" # between candidates and selected keywords/phrases\n",
" candidate_similarities = word_doc_similarity[candidates_idx, :]\n",
" target_similarities = np.max(word_similarity[candidates_idx][:, keywords_idx], axis=1)\n",
"\n",
" # Calculate MMR\n",
" mmr = (lambda_param) * candidate_similarities - (1-lambda_param) * target_similarities.reshape(-1, 1)\n",
" mmr_idx = candidates_idx[np.argmax(mmr)]\n",
"\n",
" # Update keywords & candidates\n",
" keywords_idx.append(mmr_idx)\n",
" candidates_idx.remove(mmr_idx)\n",
"\n",
" return [words[idx] for idx in keywords_idx]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "UCN0-kXEfxwy"
},
"outputs": [],
"source": [
"from collections import OrderedDict\n",
"from sklearn.metrics.pairwise import cosine_similarity\n",
"import nltk\n",
"nltk.download('omw-1.4')\n",
"\n",
"def get_distractors_wordnet(word):\n",
" distractors=[]\n",
" try:\n",
" syn = wn.synsets(word,'n')[0]\n",
" \n",
" word= word.lower()\n",
" orig_word = word\n",
" if len(word.split())>0:\n",
" word = word.replace(\" \",\"_\")\n",
" hypernym = syn.hypernyms()\n",
" if len(hypernym) == 0: \n",
" return distractors\n",
" for item in hypernym[0].hyponyms():\n",
" name = item.lemmas()[0].name()\n",
" #print (\"name \",name, \" word\",orig_word)\n",
" if name == orig_word:\n",
" continue\n",
" name = name.replace(\"_\",\" \")\n",
" name = \" \".join(w.capitalize() for w in name.split())\n",
" if name is not None and name not in distractors:\n",
" distractors.append(name)\n",
" except:\n",
" print (\"Wordnet distractors not found\")\n",
" return distractors\n",
"\n",
"def get_distractors (word,origsentence,sense2vecmodel,sentencemodel,top_n,lambdaval):\n",
" distractors = sense2vec_get_words(word,sense2vecmodel,top_n,origsentence)\n",
" print (\"distractors \",distractors)\n",
" if len(distractors) ==0:\n",
" return distractors\n",
" distractors_new = [word.capitalize()]\n",
" distractors_new.extend(distractors)\n",
" # print (\"distractors_new .. \",distractors_new)\n",
"\n",
" embedding_sentence = origsentence+ \" \"+word.capitalize()\n",
" # embedding_sentence = word\n",
" keyword_embedding = sentencemodel.encode([embedding_sentence])\n",
" distractor_embeddings = sentencemodel.encode(distractors_new)\n",
"\n",
" # filtered_keywords = mmr(keyword_embedding, distractor_embeddings,distractors,4,0.7)\n",
" max_keywords = min(len(distractors_new),5)\n",
" filtered_keywords = mmr(keyword_embedding, distractor_embeddings,distractors_new,max_keywords,lambdaval)\n",
" # filtered_keywords = filtered_keywords[1:]\n",
" final = [word.capitalize()]\n",
" for wrd in filtered_keywords:\n",
" if wrd.lower() !=word.lower():\n",
" final.append(wrd.capitalize())\n",
" final = final[1:]\n",
" return final\n",
"\n",
"sent = \"What cryptocurrency did Musk rarely tweet about?\"\n",
"keyword = \"Bitcoin\"\n",
"\n",
"# sent = \"What did Musk say he was working with to improve system transaction efficiency?\"\n",
"# keyword= \"Dogecoin\"\n",
"\n",
"\n",
"# sent = \"What company did Musk say would not accept bitcoin payments?\"\n",
"# keyword= \"Tesla\"\n",
"\n",
"\n",
"# sent = \"What has Musk often tweeted in support of?\"\n",
"# keyword = \"Cryptocurrency\"\n",
"\n",
"print (get_distractors(keyword,sent,s2v,sentence_transformer_model,40,0.2))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "s2FX-mGdf08p"
},
"outputs": [],
"source": [
"get_distractors_wordnet('lion')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "vgvffLecf4Cq"
},
"outputs": [],
"source": [
"import gradio as gr\n",
"\n",
"context = gr.inputs.Textbox(lines=10, placeholder=\"Enter paragraph/content here...\")\n",
"output = gr.outputs.HTML( label=\"Question and Answers\")\n",
"radiobutton = gr.inputs.Radio([\"Wordnet\", \"Sense2Vec\"])\n",
"\n",
"def generate_question(context,radiobutton):\n",
" summary_text = summarizer(context,summary_model,summary_tokenizer)\n",
" for wrp in wrap(summary_text, 100):\n",
" print (wrp)\n",
" # np = getnounphrases(summary_text,sentence_transformer_model,3)\n",
" np = get_keywords(context,summary_text)\n",
" print (\"\\n\\nNoun phrases\",np)\n",
" output=\"\"\n",
" for answer in np:\n",
" ques = get_question(summary_text,answer,question_model,question_tokenizer)\n",
" if radiobutton==\"Wordnet\":\n",
" distractors = get_distractors_wordnet(answer)\n",
" else:\n",
" distractors = get_distractors(answer.capitalize(),ques,s2v,sentence_transformer_model,40,0.2)\n",
" # output= output + ques + \"\\n\" + \"Ans: \"+answer.capitalize() + \"\\n\\n\"\n",
" output = output + \"<b style='color:blue;'>\" + ques + \"</b>\"\n",
" output = output + \"<br>\"\n",
" output = output + \"<b style='color:green;'>\" + \"Ans: \" +answer.capitalize()+ \"</b>\"+\"<br>\"\n",
" if len(distractors)>0:\n",
" for distractor in distractors[:4]:\n",
" output = output + \"<b style='color:brown;'>\" + distractor+ \"</b>\"+\"<br>\"\n",
" output = output + \"<br>\"\n",
"\n",
" summary =\"Summary: \"+ summary_text\n",
" for answer in np:\n",
" summary = summary.replace(answer,\"<b>\"+answer+\"</b>\" + \"<br>\")\n",
" summary = summary.replace(answer.capitalize(),\"<b>\"+answer.capitalize()+\"</b>\")\n",
" output = output + \"<p>\"+summary+\"</p>\"\n",
" output = output + \"<br>\"\n",
" return output\n",
"\n",
"\n",
"iface = gr.Interface(\n",
" fn=generate_question, \n",
" inputs=[context,radiobutton], \n",
" outputs=output)\n",
"iface.launch(debug=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "EhKGhA1ff7Hi"
},
"outputs": [],
"source": [
"import requests\n",
"\n",
"url = \"https://question-answer.p.rapidapi.com/question-answer\"\n",
"\n",
"querystring = {\"question\":\"What are some tips to starting up your own small business?\"}\n",
"\n",
"headers = {\n",
"\t\"X-RapidAPI-Key\": \"SIGN-UP-FOR-KEY\",\n",
"\t\"X-RapidAPI-Host\": \"question-answer.p.rapidapi.com\"\n",
"}\n",
"\n",
"response = requests.request(\"GET\", url, headers=headers, params=querystring)\n",
"\n",
"print(response.text)"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"provenance": []
},
"gpuClass": "standard",
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
} |