aitech-eks-pub/cw/13_transformery2.ipynb

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2021-06-16 15:14:42 +02:00
{
2021-10-05 15:04:58 +02:00
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n",
"<div class=\"alert alert-block alert-info\">\n",
"<h1> Ekstrakcja informacji </h1>\n",
"<h2> 13. <i>Transformery 2</i> [ćwiczenia]</h2> \n",
"<h3> Jakub Pokrywka (2021)</h3>\n",
"</div>\n",
"\n",
"![Logo 2](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech2.jpg)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Wizualizacja atencji\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"https://github.com/jessevig/bertviz"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"!pip install bertviz"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from transformers import AutoTokenizer, AutoModel\n",
"from bertviz import model_view, head_view"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"TEXT = \"This is a sample input sentence for a transformer model\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"MODEL = \"distilbert-base-uncased\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tokenizer = AutoTokenizer.from_pretrained(MODEL)\n",
"model = AutoModel.from_pretrained(MODEL, output_attentions=True)\n",
"inputs = tokenizer.encode(TEXT, return_tensors='pt')\n",
"outputs = model(inputs)\n",
"attention = outputs[-1]\n",
"tokens = tokenizer.convert_ids_to_tokens(inputs[0]) \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### SELF ATTENTION MODELS"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"head_view(attention, tokens)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model_view(attention, tokens)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ENCODER-DECODER MODELS"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"MODEL = \"Helsinki-NLP/opus-mt-en-de\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"TEXT_ENCODER = \"She sees the small elephant.\"\n",
"TEXT_DECODER = \"Sie sieht den kleinen Elefanten.\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tokenizer = AutoTokenizer.from_pretrained(MODEL)\n",
"model = AutoModel.from_pretrained(MODEL, output_attentions=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"encoder_input_ids = tokenizer(TEXT_ENCODER, return_tensors=\"pt\", add_special_tokens=True).input_ids\n",
"decoder_input_ids = tokenizer(TEXT_DECODER, return_tensors=\"pt\", add_special_tokens=True).input_ids\n",
"\n",
"outputs = model(input_ids=encoder_input_ids, decoder_input_ids=decoder_input_ids)\n",
"\n",
"encoder_text = tokenizer.convert_ids_to_tokens(encoder_input_ids[0])\n",
"decoder_text = tokenizer.convert_ids_to_tokens(decoder_input_ids[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"head_view(\n",
" encoder_attention=outputs.encoder_attentions,\n",
" decoder_attention=outputs.decoder_attentions,\n",
" cross_attention=outputs.cross_attentions,\n",
" encoder_tokens= encoder_text,\n",
" decoder_tokens = decoder_text\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"model_view(\n",
" encoder_attention=outputs.encoder_attentions,\n",
" decoder_attention=outputs.decoder_attentions,\n",
" cross_attention=outputs.cross_attentions,\n",
" encoder_tokens= encoder_text,\n",
" decoder_tokens = decoder_text\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Zadanie (10 minut)\n",
"\n",
"Za pomocą modelu en-fr przetłumacz dowolne zdanie z angielskiego na język francuski i sprawdź wagi atencji dla tego tłumaczenia"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### PRZYKŁAD: GPT3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ZADANIE DOMOWE - POLEVAL"
]
}
],
"metadata": {
"author": "Jakub Pokrywka",
"email": "kubapok@wmi.amu.edu.pl",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"lang": "pl",
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3"
},
"subtitle": "13.Transformery 2[ćwiczenia]",
"title": "Ekstrakcja informacji",
"year": "2021"
},
"nbformat": 4,
"nbformat_minor": 4
}