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| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/cstorm125/1db00f0540d206df687bdbc2f19c9c66/fake_berthai.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "BS1LFhUDn5Q8" | |
| }, | |
| "source": [ | |
| "# Fake BERT in Thai with Machine Translation" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "iWUHdwhGHA6d" | |
| }, | |
| "source": [ | |
| "RNN-based models have performed [reasonably well (sometimes better than transformers)](https://github.com/PyThaiNLP/classification-benchmarks) for simpler tasks like sequence classification (sentiment analysis, review classification, intent classification). However, with the lack of a large pretrained BERT in Thai, more complex tasks like question answering, summarization and language generation (with \"smoother\" models like GPT-2) have not been tried in Thai. \n", | |
| "\n", | |
| "With the advent of `scb-mt-en-th-2020` machine translation models based on `transformers-base` ([current state-of-the-art on IWSLT15](https://arxiv.org/abs/2007.03541)), we see if we can replicate the performance of pretrained English models by translating inputs and outputs to Thai before and after the models." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "hox86w2UoBXF" | |
| }, | |
| "source": [ | |
| "## Setups" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "qSIF-2WYYqMl" | |
| }, | |
| "source": [ | |
| "Run this to set things up." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "GKzs7fTqnzzH", | |
| "outputId": "61124fe9-a745-4947-d8a6-412ab4969826", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 692 | |
| } | |
| }, | |
| "source": [ | |
| "#install libraries\n", | |
| "!pip install -q torch pythainlp==2.1.4 sentencepiece transformers\n", | |
| "!pip install git+https://github.com/pytorch/fairseq@6f6461b;" | |
| ], | |
| "execution_count": 1, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\u001b[K |████████████████████████████████| 11.1MB 223kB/s \n", | |
| "\u001b[K |████████████████████████████████| 1.1MB 58.5MB/s \n", | |
| "\u001b[K |████████████████████████████████| 1.1MB 58.6MB/s \n", | |
| "\u001b[K |████████████████████████████████| 1.4MB 57.8MB/s \n", | |
| "\u001b[K |████████████████████████████████| 3.0MB 47.6MB/s \n", | |
| "\u001b[K |████████████████████████████████| 890kB 56.5MB/s \n", | |
| "\u001b[?25h Building wheel for nltk (setup.py) ... \u001b[?25l\u001b[?25hdone\n", | |
| " Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n", | |
| "Collecting git+https://github.com/pytorch/fairseq@6f6461b\n", | |
| " Cloning https://github.com/pytorch/fairseq (to revision 6f6461b) to /tmp/pip-req-build-4nt9sfda\n", | |
| " Running command git clone -q https://github.com/pytorch/fairseq /tmp/pip-req-build-4nt9sfda\n", | |
| "\u001b[33m WARNING: Did not find branch or tag '6f6461b', assuming revision or ref.\u001b[0m\n", | |
| " Running command git checkout -q 6f6461b\n", | |
| " Running command git submodule update --init --recursive -q\n", | |
| " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", | |
| " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", | |
| " Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n", | |
| " Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n", | |
| "Requirement already satisfied: torch in /usr/local/lib/python3.6/dist-packages (from fairseq==0.9.0) (1.6.0+cu101)\n", | |
| "Collecting sacrebleu\n", | |
| "\u001b[?25l Downloading https://files.pythonhosted.org/packages/a3/c4/8e948f601a4f9609e8b2b58f31966cb13cf17b940b82aa3e767f01c42c52/sacrebleu-1.4.14-py3-none-any.whl (64kB)\n", | |
| "\u001b[K |████████████████████████████████| 71kB 3.7MB/s \n", | |
| "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from fairseq==0.9.0) (1.18.5)\n", | |
| "Requirement already satisfied: regex in /usr/local/lib/python3.6/dist-packages (from fairseq==0.9.0) (2019.12.20)\n", | |
| "Requirement already satisfied: cython in /usr/local/lib/python3.6/dist-packages (from fairseq==0.9.0) (0.29.21)\n", | |
| "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from fairseq==0.9.0) (4.41.1)\n", | |
| "Requirement already satisfied: cffi in /usr/local/lib/python3.6/dist-packages (from fairseq==0.9.0) (1.14.3)\n", | |
| "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from torch->fairseq==0.9.0) (0.16.0)\n", | |
| "Collecting portalocker\n", | |
| " Downloading https://files.pythonhosted.org/packages/89/a6/3814b7107e0788040870e8825eebf214d72166adf656ba7d4bf14759a06a/portalocker-2.0.0-py2.py3-none-any.whl\n", | |
| "Requirement already satisfied: pycparser in /usr/local/lib/python3.6/dist-packages (from cffi->fairseq==0.9.0) (2.20)\n", | |
| "Building wheels for collected packages: fairseq\n", | |
| " Building wheel for fairseq (PEP 517) ... \u001b[?25l\u001b[?25hdone\n", | |
| " Created wheel for fairseq: filename=fairseq-0.9.0-cp36-cp36m-linux_x86_64.whl size=2203320 sha256=75a902ab11ef6c8476b66254f2ea8d98b038450e7181c5e561a4cf5c793a74b8\n", | |
| " Stored in directory: /tmp/pip-ephem-wheel-cache-3mdfo7xn/wheels/fe/76/a0/094fc6e2fbd71b397f081787ec56944f4e7abf58436110f5fa\n", | |
| "Successfully built fairseq\n", | |
| "Installing collected packages: portalocker, sacrebleu, fairseq\n", | |
| "Successfully installed fairseq-0.9.0 portalocker-2.0.0 sacrebleu-1.4.14\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "PNXRDg9pndz7", | |
| "outputId": "ded7667a-f86e-4f6f-f369-91acfa55d158", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 997 | |
| } | |
| }, | |
| "source": [ | |
| "#download weights\n", | |
| "!wget https://github.com/vistec-AI/model-releases/releases/download/SCB_1M%2BTBASE_v1.0/SCB_1M+TBASE_th-en_spm-spm_32000-joined_v1.0.tar.gz\n", | |
| "!wget https://github.com/vistec-AI/model-releases/releases/download/SCB_1M%2BTBASE_v1.0/SCB_1M+TBASE_en-th_spm-spm_32000-joined_v1.0.tar.gz\n", | |
| "!mkdir -p ./mt\n", | |
| "!tar -C ./mt -xvzf SCB_1M+TBASE_th-en_spm-spm_32000-joined_v1.0.tar.gz\n", | |
| "!tar -C ./mt -xvzf SCB_1M+TBASE_en-th_spm-spm_32000-joined_v1.0.tar.gz" | |
| ], | |
| "execution_count": 2, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "--2020-10-14 15:17:11-- https://github.com/vistec-AI/model-releases/releases/download/SCB_1M%2BTBASE_v1.0/SCB_1M+TBASE_th-en_spm-spm_32000-joined_v1.0.tar.gz\n", | |
| "Resolving github.com (github.com)... 192.30.255.112\n", | |
| "Connecting to github.com (github.com)|192.30.255.112|:443... connected.\n", | |
| "HTTP request sent, awaiting response... 302 Found\n", | |
| "Location: https://github-production-release-asset-2e65be.s3.amazonaws.com/272403533/14416180-b4b9-11ea-81ab-f85e212bf35b?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20201014%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201014T151711Z&X-Amz-Expires=300&X-Amz-Signature=3be50f3f0cef6aa4893833a9f5904566e5f35d5a9fce09a216ff13e6bc1b6bff&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=272403533&response-content-disposition=attachment%3B%20filename%3DSCB_1M%2BTBASE_th-en_spm-spm_32000-joined_v1.0.tar.gz&response-content-type=application%2Foctet-stream [following]\n", | |
| "--2020-10-14 15:17:11-- https://github-production-release-asset-2e65be.s3.amazonaws.com/272403533/14416180-b4b9-11ea-81ab-f85e212bf35b?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20201014%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201014T151711Z&X-Amz-Expires=300&X-Amz-Signature=3be50f3f0cef6aa4893833a9f5904566e5f35d5a9fce09a216ff13e6bc1b6bff&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=272403533&response-content-disposition=attachment%3B%20filename%3DSCB_1M%2BTBASE_th-en_spm-spm_32000-joined_v1.0.tar.gz&response-content-type=application%2Foctet-stream\n", | |
| "Resolving github-production-release-asset-2e65be.s3.amazonaws.com (github-production-release-asset-2e65be.s3.amazonaws.com)... 52.216.97.219\n", | |
| "Connecting to github-production-release-asset-2e65be.s3.amazonaws.com (github-production-release-asset-2e65be.s3.amazonaws.com)|52.216.97.219|:443... connected.\n", | |
| "HTTP request sent, awaiting response... 200 OK\n", | |
| "Length: 699200099 (667M) [application/octet-stream]\n", | |
| "Saving to: ‘SCB_1M+TBASE_th-en_spm-spm_32000-joined_v1.0.tar.gz’\n", | |
| "\n", | |
| "SCB_1M+TBASE_th-en_ 100%[===================>] 666.81M 38.5MB/s in 15s \n", | |
| "\n", | |
| "2020-10-14 15:17:27 (43.6 MB/s) - ‘SCB_1M+TBASE_th-en_spm-spm_32000-joined_v1.0.tar.gz’ saved [699200099/699200099]\n", | |
| "\n", | |
| "--2020-10-14 15:17:27-- https://github.com/vistec-AI/model-releases/releases/download/SCB_1M%2BTBASE_v1.0/SCB_1M+TBASE_en-th_spm-spm_32000-joined_v1.0.tar.gz\n", | |
| "Resolving github.com (github.com)... 192.30.255.112\n", | |
| "Connecting to github.com (github.com)|192.30.255.112|:443... connected.\n", | |
| "HTTP request sent, awaiting response... 302 Found\n", | |
| "Location: https://github-production-release-asset-2e65be.s3.amazonaws.com/272403533/c88cb900-b4b4-11ea-9b7f-294881c2ca03?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20201014%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201014T151727Z&X-Amz-Expires=300&X-Amz-Signature=5c3e61f98f6a21f7c4ab8136a3f16e300ae2c55e6cf6fa31edb5036a72e2fff6&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=272403533&response-content-disposition=attachment%3B%20filename%3DSCB_1M%2BTBASE_en-th_spm-spm_32000-joined_v1.0.tar.gz&response-content-type=application%2Foctet-stream [following]\n", | |
| "--2020-10-14 15:17:27-- https://github-production-release-asset-2e65be.s3.amazonaws.com/272403533/c88cb900-b4b4-11ea-9b7f-294881c2ca03?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20201014%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201014T151727Z&X-Amz-Expires=300&X-Amz-Signature=5c3e61f98f6a21f7c4ab8136a3f16e300ae2c55e6cf6fa31edb5036a72e2fff6&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=272403533&response-content-disposition=attachment%3B%20filename%3DSCB_1M%2BTBASE_en-th_spm-spm_32000-joined_v1.0.tar.gz&response-content-type=application%2Foctet-stream\n", | |
| "Resolving github-production-release-asset-2e65be.s3.amazonaws.com (github-production-release-asset-2e65be.s3.amazonaws.com)... 52.216.109.115\n", | |
| "Connecting to github-production-release-asset-2e65be.s3.amazonaws.com (github-production-release-asset-2e65be.s3.amazonaws.com)|52.216.109.115|:443... connected.\n", | |
| "HTTP request sent, awaiting response... 200 OK\n", | |
| "Length: 699080820 (667M) [application/octet-stream]\n", | |
| "Saving to: ‘SCB_1M+TBASE_en-th_spm-spm_32000-joined_v1.0.tar.gz’\n", | |
| "\n", | |
| "SCB_1M+TBASE_en-th_ 100%[===================>] 666.70M 47.0MB/s in 15s \n", | |
| "\n", | |
| "2020-10-14 15:17:42 (45.1 MB/s) - ‘SCB_1M+TBASE_en-th_spm-spm_32000-joined_v1.0.tar.gz’ saved [699080820/699080820]\n", | |
| "\n", | |
| "SCB_1M+TBASE_th-en_spm-spm_32000-joined_v1.0/\n", | |
| "SCB_1M+TBASE_th-en_spm-spm_32000-joined_v1.0/bpe/\n", | |
| "SCB_1M+TBASE_th-en_spm-spm_32000-joined_v1.0/models/\n", | |
| "SCB_1M+TBASE_th-en_spm-spm_32000-joined_v1.0/vocab/\n", | |
| "SCB_1M+TBASE_th-en_spm-spm_32000-joined_v1.0/vocab/dict.th.txt\n", | |
| "SCB_1M+TBASE_th-en_spm-spm_32000-joined_v1.0/vocab/dict.en.txt\n", | |
| "SCB_1M+TBASE_th-en_spm-spm_32000-joined_v1.0/models/checkpoint.pt\n", | |
| "SCB_1M+TBASE_th-en_spm-spm_32000-joined_v1.0/bpe/spm.th.vocab\n", | |
| "SCB_1M+TBASE_th-en_spm-spm_32000-joined_v1.0/bpe/spm.en.model\n", | |
| "SCB_1M+TBASE_th-en_spm-spm_32000-joined_v1.0/bpe/spm.th.model\n", | |
| "SCB_1M+TBASE_th-en_spm-spm_32000-joined_v1.0/bpe/spm.en.vocab\n", | |
| "SCB_1M+TBASE_en-th_spm-spm_32000-joined_v1.0/\n", | |
| "SCB_1M+TBASE_en-th_spm-spm_32000-joined_v1.0/bpe/\n", | |
| "SCB_1M+TBASE_en-th_spm-spm_32000-joined_v1.0/models/\n", | |
| "SCB_1M+TBASE_en-th_spm-spm_32000-joined_v1.0/vocab/\n", | |
| "SCB_1M+TBASE_en-th_spm-spm_32000-joined_v1.0/vocab/dict.th.txt\n", | |
| "SCB_1M+TBASE_en-th_spm-spm_32000-joined_v1.0/vocab/dict.en.txt\n", | |
| "SCB_1M+TBASE_en-th_spm-spm_32000-joined_v1.0/models/checkpoint.pt\n", | |
| "SCB_1M+TBASE_en-th_spm-spm_32000-joined_v1.0/bpe/spm.th.vocab\n", | |
| "SCB_1M+TBASE_en-th_spm-spm_32000-joined_v1.0/bpe/spm.en.model\n", | |
| "SCB_1M+TBASE_en-th_spm-spm_32000-joined_v1.0/bpe/spm.th.model\n", | |
| "SCB_1M+TBASE_en-th_spm-spm_32000-joined_v1.0/bpe/spm.en.vocab\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "RuaOjlUbnibp" | |
| }, | |
| "source": [ | |
| "from __future__ import print_function\n", | |
| "from ipywidgets import interact, interactive, fixed, interact_manual\n", | |
| "import ipywidgets as widgets\n", | |
| "import textwrap\n", | |
| "\n", | |
| "import torch\n", | |
| "from fairseq.models.transformer import TransformerModel" | |
| ], | |
| "execution_count": 3, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "psuL88nuoanq" | |
| }, | |
| "source": [ | |
| "## `scb-mt-en-th-2020` Machine Translation Models" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "RpKwC5JUYtGi" | |
| }, | |
| "source": [ | |
| "Try out the machine translation models. This is not the best version but they will do reasonably well." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "rYdLExGdmjOn" | |
| }, | |
| "source": [ | |
| "en2th_spm = TransformerModel.from_pretrained(\n", | |
| " model_name_or_path='mt/SCB_1M+TBASE_en-th_spm-spm_32000-joined_v1.0/models/',\n", | |
| " checkpoint_file='checkpoint.pt',\n", | |
| " data_name_or_path='mt/SCB_1M+TBASE_en-th_spm-spm_32000-joined_v1.0/vocab/',\n", | |
| " bpe='sentencepiece',\n", | |
| " sentencepiece_vocab='mt/SCB_1M+TBASE_en-th_spm-spm_32000-joined_v1.0/bpe/spm.en.model'\n", | |
| ")\n", | |
| "\n", | |
| "th2en_spm = TransformerModel.from_pretrained(\n", | |
| " model_name_or_path='mt/SCB_1M+TBASE_th-en_spm-spm_32000-joined_v1.0/models/',\n", | |
| " checkpoint_file='checkpoint.pt',\n", | |
| " data_name_or_path='mt/SCB_1M+TBASE_th-en_spm-spm_32000-joined_v1.0/vocab/',\n", | |
| " bpe='sentencepiece',\n", | |
| " sentencepiece_vocab='mt/SCB_1M+TBASE_th-en_spm-spm_32000-joined_v1.0/bpe/spm.th.model'\n", | |
| " )" | |
| ], | |
| "execution_count": 4, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "ICPdoTM6zcvl", | |
| "cellView": "form", | |
| "outputId": "d7d327d7-3e16-47df-d5ae-e3c85d5f79f0", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 52 | |
| } | |
| }, | |
| "source": [ | |
| "#@title English to Thai Translation\n", | |
| "input_sentence = 'Covid-19 has infected more than 3 million people and killed at least 210,000 worldwide, according to Johns Hopkins University.' #@param {type:\"string\"}\n", | |
| "\n", | |
| "hypothesis = en2th_spm.translate(input_sentence)\n", | |
| "\n", | |
| "print('input_sentence:', input_sentence)\n", | |
| "print('translation :', hypothesis)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "input_sentence: Covid-19 has infected more than 3 million people and killed at least 210,000 worldwide, according to Johns Hopkins University.\n", | |
| "translation : โควิด-19 ได้ติดเชื้อมากกว่า 3 ล้านคนและสังหารอย่างน้อย 210,000 คนทั่วโลก จากการรายงานของมหาวิทยาลัยจอห์นส์ฮอปกินส์\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "zAHi6NcQnT9h", | |
| "cellView": "form", | |
| "outputId": "f3a2b2e2-1f15-42f9-8d40-900882c1f3d1", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 52 | |
| } | |
| }, | |
| "source": [ | |
| "#@title English to Thai Translation\n", | |
| "input_sentence = 'ราคาเอทิลีนและโพรพิลีนในตลาดเอเชียไตรมาส 1 ปี 2560 เฉลี่ยอยู่ที่ 38.28 และ 32.81 บาท/กิโลกรัม ตามลำดับ' #@param {type:\"string\"}\n", | |
| "\n", | |
| "hypothesis = th2en_spm.translate(input_sentence)\n", | |
| "\n", | |
| "print('input_sentence:', input_sentence)\n", | |
| "print('translation :', hypothesis)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "input_sentence: ราคาเอทิลีนและโพรพิลีนในตลาดเอเชียไตรมาส 1 ปี 2560 เฉลี่ยอยู่ที่ 38.28 และ 32.81 บาท/กิโลกรัม ตามลำดับ\n", | |
| "translation : Ethylene and polypropylene prices in the first quarter of 2017 averaged 38.28 and 32.81 baht/kg, respectively.\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "HhpjXQIqppDQ" | |
| }, | |
| "source": [ | |
| "## BERT Whole-Word Masking Question Answering" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "PxQVJqzSYz1L" | |
| }, | |
| "source": [ | |
| "We first try question answering using BERT trained with whole-word masking. Put the context in `text_th` and then enter your questions in the cell below." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "RyoNKYK34JF6", | |
| "outputId": "0ec477e3-e02e-4822-f842-123210b91e3d", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 166, | |
| "referenced_widgets": [ | |
| "5763d9c666104798b9142f7930feb082", | |
| "bb5d731d4dd7459cb4cff26826981941", | |
| "361718c1529e4752ba39be680d113ea5", | |
| "887b453cff9047a7a3a3bd2ab770b706", | |
| "5665589f88e34f84a77a13dffe4b1c6d", | |
| "c17c3e07f0224529a97fef31bbdc11ad", | |
| "b693309e4f7b43f18ab854f862cccdb4", | |
| "9c46eaeee1fa4f1fb5dde626cdd04aa5", | |
| "f9edfdc8fb514f23ba55610df90314d2", | |
| "b8d20cc2a1dc419f91c17fc51cf3aa3d", | |
| "76900b67fe0448bc96ab847f01494eb7", | |
| "02226a523d904b00b438a51c2167280b", | |
| "f8fd175a2e0c41a2a75b93b50d14eb5e", | |
| "7ed384326c894a859f4a29aef801baf5", | |
| "2a7cd115b9604a118fa61064ffca081f", | |
| "169db29a65624951ae84c016ca077689", | |
| "3a85c7ca089048c481decb7f0fab19e8", | |
| "e0892ebb37cf4972b5b304303bf3848b", | |
| "47c174ee43094cea811a017228238278", | |
| "7d7fe817759a4034af8adafd25719010", | |
| "b1fd665f566f409a8c7e74e590034d7a", | |
| "a94ce761446b47e5bd55ece5435efa18", | |
| "01c732c5b9fb41d7b78a1b457711cd2d", | |
| "75f56137297445939d8fddfcf2e2209a" | |
| ] | |
| } | |
| }, | |
| "source": [ | |
| " from transformers import AutoTokenizer, AutoModelForQuestionAnswering\n", | |
| "\n", | |
| "tokenizer = AutoTokenizer.from_pretrained(\"bert-large-uncased-whole-word-masking-finetuned-squad\")\n", | |
| "model = AutoModelForQuestionAnswering.from_pretrained(\"bert-large-uncased-whole-word-masking-finetuned-squad\")" | |
| ], | |
| "execution_count": 5, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "5763d9c666104798b9142f7930feb082", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=443.0, style=ProgressStyle(description_…" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "f9edfdc8fb514f23ba55610df90314d2", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=231508.0, style=ProgressStyle(descripti…" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "3a85c7ca089048c481decb7f0fab19e8", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1340675298.0, style=ProgressStyle(descr…" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| } | |
| }, | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "bl-LuZdRFr5s" | |
| }, | |
| "source": [ | |
| "#context article\n", | |
| "text_th = r\"\"\" \n", | |
| "สโมสรฟุตบอลแมนเชสเตอร์ยูไนเต็ดเป็นสโมสรฟุตบอลตั้งอยู่ที่โอลด์แทรฟฟอร์ดในเกรเทอร์แมนเชสเตอร์ ประเทศอังกฤษ \n", | |
| "ปัจจุบันแข่งขันในพรีเมียร์ลีกซึ่งเป็นลีกสูงสุดของฟุตบอลอังกฤษ สโมสรมีฉายา \"ปีศาจแดง\" ก่อตั้งในชื่อสโมสรฟุตบอลนิวตันฮีตแอลวายอาร์ใน ค.ศ. 1878 \n", | |
| "ต่อมาเปลี่ยนชื่อเป็นแมนเชสเตอร์ยูไนเต็ดใน ค.ศ. 1902 และย้ายไปเล่นที่สนามเหย้าปัจจุบันอย่างโอลด์แทรฟฟอร์ดใน ค.ศ. 1910\n", | |
| "\"\"\" \n", | |
| "text_en = th2en_spm.translate(text_th)" | |
| ], | |
| "execution_count": 8, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "dI94wLAFnX65", | |
| "cellView": "form", | |
| "outputId": "a718ccfc-2b0d-4742-b1f0-9704feaf52b7", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 124 | |
| } | |
| }, | |
| "source": [ | |
| "#@title Question Answering\n", | |
| "question_th_1 = \"\\u0E41\\u0E21\\u0E19\\u0E40\\u0E0A\\u0E2A\\u0E40\\u0E15\\u0E2D\\u0E23\\u0E4C\\u0E22\\u0E39\\u0E44\\u0E19\\u0E40\\u0E15\\u0E47\\u0E14\\u0E04\\u0E37\\u0E2D\\u0E2D\\u0E30\\u0E44\\u0E23\" #@param {type:\"string\"}\n", | |
| "question_th_2 = \"\\u0E2A\\u0E19\\u0E32\\u0E21\\u0E40\\u0E2B\\u0E22\\u0E49\\u0E32\\u0E02\\u0E2D\\u0E07\\u0E41\\u0E21\\u0E19\\u0E22\\u0E39\\u0E2D\\u0E22\\u0E39\\u0E48\\u0E17\\u0E35\\u0E48\\u0E44\\u0E2B\\u0E19\" #@param {type:\"string\"}\n", | |
| "question_th_3 = \"\\u0E01\\u0E48\\u0E2D\\u0E19\\u0E1B\\u0E35 1902 \\u0E41\\u0E21\\u0E19\\u0E40\\u0E0A\\u0E2A\\u0E40\\u0E15\\u0E2D\\u0E23\\u0E4C\\u0E22\\u0E39\\u0E44\\u0E19\\u0E40\\u0E15\\u0E47\\u0E14\\u0E0A\\u0E37\\u0E48\\u0E2D\\u0E2D\\u0E30\\u0E44\\u0E23\" #@param {type:\"string\"}\n", | |
| "questions_th =[question_th_1,question_th_2,question_th_3]\n", | |
| "questions_en = [th2en_spm.translate(i) for i in questions_th]\n", | |
| "\n", | |
| "for i in range(len(questions_en)):\n", | |
| " question_en = questions_en[i]\n", | |
| " question_th = questions_th[i]\n", | |
| " inputs = tokenizer(question_en, text_en, add_special_tokens=True, return_tensors=\"pt\")\n", | |
| " input_ids = inputs[\"input_ids\"].tolist()[0]\n", | |
| " text_tokens = tokenizer.convert_ids_to_tokens(input_ids)\n", | |
| " answer_start_scores, answer_end_scores = model(**inputs)\n", | |
| " answer_start = torch.argmax(answer_start_scores) # Get the most likely beginning of answer with the argmax of the score\n", | |
| " answer_end = torch.argmax(answer_end_scores) + 1 # Get the most likely end of answer with the argmax of the score\n", | |
| " answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end]))\n", | |
| " # answer = f'{answer}.' if answer[-1]!='.' else answer\n", | |
| " print(f\"Question: {question_th}\")\n", | |
| " print(f\"Answer: {en2th_spm.translate(answer)} (from {answer_start} to {answer_end})\")" | |
| ], | |
| "execution_count": 12, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Question: แมนเชสเตอร์ยูไนเต็ดคืออะไร\n", | |
| "Answer: สโมสรฟุตบอล (from 10 to 13)\n", | |
| "Question: สนามเหย้าของแมนยูอยู่ที่ไหน\n", | |
| "Answer: โอลด์ แทรฟฟอร์ดค่ะ (from 20 to 22)\n", | |
| "Question: ก่อนปี 1902 แมนเชสเตอร์ยูไนเต็ดชื่ออะไร\n", | |
| "Answer: เรดเดวิลค่ะ (from 47 to 50)\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "D2ExOLGwtVRq" | |
| }, | |
| "source": [ | |
| "## T5 Summarization" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "SGjITDJVZA9q" | |
| }, | |
| "source": [ | |
| "You can also try summarization by entering the text to summarize in `article_th`. Tune the max length of the summarization in the cell below." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "rvB4p84SHSoR" | |
| }, | |
| "source": [ | |
| "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n", | |
| "\n", | |
| "model = AutoModelForSeq2SeqLM.from_pretrained(\"t5-base\")\n", | |
| "tokenizer = AutoTokenizer.from_pretrained(\"t5-base\")" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "BnZGJ6X4HekL" | |
| }, | |
| "source": [ | |
| "#text to summarize\n", | |
| "article_th = '''บริษัท สรรพสินค้าเซ็นทรัล จำกัด หรือ ห้างสรรพสินค้าเซ็นทรัล เป็นห้างสรรพสินค้าเก่าแก่แห่งหนึ่งของประเทศไทย \n", | |
| "ก่อตั้งโดย เตียง จิราธิวัฒน์ เริ่มดำเนินการเมื่อปี พ.ศ. 2490 ด้วยการเป็นร้านขายของชำเล็กๆ \n", | |
| "ก่อนที่สัมฤทธิ์ บุตรชาย จะเริ่มดำเนินการห้างสรรพสินค้าเซ็นทรัล สาขาแรกเมื่อ พ.ศ. 2499 ที่ย่านวังบูรพา \n", | |
| "และกลายมาเป็นห้างสรรพสินค้าชั้นนำของไทยในปัจจุบัน\n", | |
| "'''\n", | |
| "article_en = th2en_spm.translate(article_th)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "ZSS4mHKXtUTH", | |
| "cellView": "form", | |
| "outputId": "f1a56b4e-1198-4ce5-8de5-61e455920012", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 124 | |
| } | |
| }, | |
| "source": [ | |
| "max_length = 200 #@param {type:\"slider\", min:150, max:300, step:50}\n", | |
| "\n", | |
| "# T5 uses a max_length of 512 so we cut the article to 512 tokens.\n", | |
| "inputs = tokenizer.encode(\"summarize: \" + article_en, return_tensors=\"pt\", \n", | |
| " max_length=512,truncation=True)\n", | |
| "outputs = model.generate(inputs, max_length=max_length, min_length=50, \n", | |
| " length_penalty=2.0, num_beams=4, early_stopping=True)\n", | |
| "summary_en = [tokenizer.decode(i) for i in outputs][0]\n", | |
| "summary_th = en2th_spm.translate(summary_en)\n", | |
| "\n", | |
| "print('Summary:')\n", | |
| "textwrap.wrap(summary_th)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Summary:\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "['เซ็นทรัล โค้ท (Central Co. หรือ Central)',\n", | |
| " 'เป็นหนึ่งในห้างสรรพสินค้าที่เก่าแก่ที่สุดของประเทศไทย',\n", | |
| " 'ก่อตั้งโดยเตียงจิราตีวัตต์ (Jirathiwatt) ซึ่งเริ่มมีร้านค้าขนาดเล็ก',\n", | |
| " 'ในปี พ.ศ. 2490 ลูกชายเริ่มดําเนินการขายห้างสรรพสินค้าแห่งแรกใน พ.ศ.',\n", | |
| " '2499']" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 82 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "HP6glRK3JrmU" | |
| }, | |
| "source": [ | |
| "## GPT-2 Text Generation" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "p8UgFYX6ZKCE" | |
| }, | |
| "source": [ | |
| "Enter a prompt and generate texts using GPT-2." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "q6hLinXZSmVv" | |
| }, | |
| "source": [ | |
| "from transformers import AutoModelForCausalLM, AutoTokenizer\n", | |
| "\n", | |
| "tokenizer = AutoTokenizer.from_pretrained(\"gpt2\")\n", | |
| "model = AutoModelForCausalLM.from_pretrained(\"gpt2\", pad_token_id=tokenizer.eos_token_id)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "l5Pg9bf6V--u", | |
| "cellView": "form", | |
| "outputId": "43c0045e-d529-4576-91c7-961b621d4548", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 319 | |
| } | |
| }, | |
| "source": [ | |
| "#@title Generate Texts\n", | |
| "prompt_th = \"กาลครั้งหนึ่งนานมาแล้ว \" #@param {type:\"string\"}\n", | |
| "max_length = 300 #@param {type:\"slider\", min:250, max:500, step:50}\n", | |
| "prompt_en = th2en_spm.translate(prompt_th)\n", | |
| "inputs = tokenizer.encode(prompt_en, \n", | |
| " add_special_tokens=False, \n", | |
| " return_tensors=\"pt\")\n", | |
| "prompt_length = len(tokenizer.decode(inputs[0], skip_special_tokens=True, \n", | |
| " clean_up_tokenization_spaces=True))\n", | |
| "outputs = model.generate(inputs, max_length=max_length, do_sample=True, \n", | |
| " top_p=0.95, top_k=60)\n", | |
| "generated = prompt_th + en2th_spm.translate(tokenizer.decode(outputs[0])[prompt_length:])\n", | |
| "textwrap.wrap(generated)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "['กาลครั้งหนึ่งนานมาแล้ว เหตุผลที่ฉันคิดว่ามันสําคัญมากสําหรับบล็อกนี้ที',\n", | |
| " '่จะสะท้อนให้เห็นถึงข้อเท็จจริงที่เรียบง่ายของชีวิตและสถานการณ์ชีวิตแทน',\n", | |
| " 'ที่จะเป็นความคิดที่เป็นนามธรรมว่าชีวิตเป็นอย่างไรตอนนี้เป็นเพราะพวกเขา',\n", | |
| " 'เป็นเรื่องราวของชีวิตอย่างที่มันทํา, เวลาที่สมองของเราทํางานเพื่อให้รู',\n", | |
| " '้สึกถึงทุกสิ่งที่เราประสบและเพื่อนําทางเราในทางที่มีความหมายบางอย่าง',\n", | |
| " 'ชีวิตไม่เพียง แต่แตกต่างกันเท่านั้น, มันซับซ้อน,',\n", | |
| " 'และบางครั้งคุณใช้ชีวิตและกิจกรรมของสมองในชีวิตซึ่งอารมณ์อื่น ๆ ทั้งหมด',\n", | |
| " '- อารมณ์ของความเจ็บปวด, ความทุกข์ทรมาน, ความหงุดหงิด, ความเครียด -',\n", | |
| " 'เล่นบทบาทขนาดใหญ่. บางครั้งเรามีประสบการณ์ชีวิตเล็กน้อยเมื่อเราอาศัยอย',\n", | |
| " 'ู่ในช่วงเวลาที่ยากลําบากที่สุดเพราะเหตุการณ์ที่น่าเศร้าบางอย่าง',\n", | |
| " '(เช่นอุบัติเหตุรถชนหรือความตายแม้ว่าเรามีประสบการณ์เหมือนกัน)',\n", | |
| " 'หรือแม้ว่าเราจะไม่มีแผนที่ถูกต้องในการจัดการกับสถานการณ์เหล่านั้น. หรื',\n", | |
| " 'อบางทีเราอาจมีความเจ็บป่วยทางจิตที่ทําให้เราตกใจอย่างถาวรหรือแม้กระทั่',\n", | |
| " 'งความเศร้าโศกและมันเป็นเหตุผลที่เรามีความพิการทางจิตหรือหนึ่งในความผิด',\n", | |
| " 'ปกติที่เราเคยอยู่ในอารมณ์ที่ดี.',\n", | |
| " 'เรามักจะพบว่ามันยากที่จะมุ่งเน้นที่ทั้งหมด,',\n", | |
| " 'เพื่อให้คิดต่อไปว่าทั้งหมดนี้จะพาเรา']" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 77 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "uohWti2YW3iT" | |
| }, | |
| "source": [ | |
| "" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| } | |
| ] | |
| } |
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