2166 lines
85 KiB
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2166 lines
85 KiB
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "W8-j-5oV0o46",
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"outputId": "2b90f9b1-acb4-45e2-9a2f-2818e6fe03b8"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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"Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.0.1+cu118)\n",
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"Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.12.0)\n",
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"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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"Collecting transformers\n",
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" Downloading transformers-4.30.2-py3-none-any.whl (7.2 MB)\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.2/7.2 MB\u001b[0m \u001b[31m91.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"Collecting huggingface-hub<1.0,>=0.14.1 (from transformers)\n",
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" Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
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"\u001b[?25hCollecting safetensors>=0.3.1 (from transformers)\n",
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"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
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"Installing collected packages: tokenizers, safetensors, huggingface-hub, transformers\n",
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"Successfully installed huggingface-hub-0.15.1 safetensors-0.3.1 tokenizers-0.13.3 transformers-4.30.2\n",
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"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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"Collecting datasets\n",
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" Downloading datasets-2.13.0-py3-none-any.whl (485 kB)\n",
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"Collecting xxhash (from datasets)\n",
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" Downloading xxhash-3.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (212 kB)\n",
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"\u001b[?25hCollecting multiprocess (from datasets)\n",
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"\u001b[?25hCollecting frozenlist>=1.1.1 (from aiohttp->datasets)\n",
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" Downloading frozenlist-1.3.3-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (149 kB)\n",
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"Installing collected packages: xxhash, multidict, frozenlist, dill, async-timeout, yarl, multiprocess, aiosignal, aiohttp, datasets\n",
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"Successfully installed aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 datasets-2.13.0 dill-0.3.6 frozenlist-1.3.3 multidict-6.0.4 multiprocess-0.70.14 xxhash-3.2.0 yarl-1.9.2\n"
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]
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}
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],
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"source": [
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"!pip install torch\n",
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"!pip install transformers\n",
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"!pip install datasets"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "O6aa5mpE0s6H",
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"outputId": "43b0e006-1678-41f1-9b40-6964f9127a74"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Cloning into 'challenging-america-word-gap-prediction'...\n",
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"remote: Wymienianie obiektów: 27, gotowe.\u001b[K\n",
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"remote: Zliczanie obiektów: 100% (27/27), gotowe.\u001b[K\n",
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"remote: Kompresowanie obiektów: 100% (23/23), gotowe.\u001b[K\n",
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"remote: Razem 27 (delty 2), użyte ponownie 17 (delty 0), paczki użyte ponownie 0\u001b[K\n",
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"Receiving objects: 100% (27/27), 278.33 MiB | 21.80 MiB/s, done.\n",
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"Resolving deltas: 100% (2/2), done.\n"
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]
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}
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],
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"source": [
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"!git clone --single-branch git://gonito.net/challenging-america-word-gap-prediction -b master"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"id": "uHkXCRs-0iSr"
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},
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"outputs": [],
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"source": [
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"import torch\n",
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"import sys\n",
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"from transformers import GPT2Tokenizer, GPT2LMHeadModel\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"id": "HyKM4zn41YvQ"
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},
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"outputs": [],
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"source": [
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"import torch\n",
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"import sys\n",
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"from transformers import GPT2Tokenizer, GPT2LMHeadModel\n",
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"\n",
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"import lzma\n",
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"from itertools import islice\n",
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"import regex as re\n",
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"import math\n",
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"import sys\n",
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"from torchtext.vocab import build_vocab_from_iterator\n",
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"from torch import nn\n",
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"from torch.utils.data import IterableDataset\n",
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"import itertools"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
|
|||
|
"metadata": {
|
|||
|
"colab": {
|
|||
|
"base_uri": "https://localhost:8080/"
|
|||
|
},
|
|||
|
"id": "-k8RhlmI06mQ",
|
|||
|
"outputId": "39d0fc5d-c6ea-4f4b-e75e-cbe199bd175a"
|
|||
|
},
|
|||
|
"outputs": [
|
|||
|
{
|
|||
|
"output_type": "stream",
|
|||
|
"name": "stdout",
|
|||
|
"text": [
|
|||
|
"/content/challenging-america-word-gap-prediction\n"
|
|||
|
]
|
|||
|
}
|
|||
|
],
|
|||
|
"source": [
|
|||
|
"%cd /content/challenging-america-word-gap-prediction"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"source": [
|
|||
|
"\n",
|
|||
|
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
|
|||
|
],
|
|||
|
"metadata": {
|
|||
|
"id": "PCA7Ank2dnwM"
|
|||
|
},
|
|||
|
"execution_count": 7,
|
|||
|
"outputs": []
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"source": [
|
|||
|
"tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2-medium\")\n",
|
|||
|
"model = GPT2LMHeadModel.from_pretrained(\"gpt2-medium\").to(device)"
|
|||
|
],
|
|||
|
"metadata": {
|
|||
|
"colab": {
|
|||
|
"base_uri": "https://localhost:8080/",
|
|||
|
"height": 177,
|
|||
|
"referenced_widgets": [
|
|||
|
"679cc8100859428eaed49abdb40f93d1",
|
|||
|
"c4a853a74ab44187a321bba0e0c242a9",
|
|||
|
"2196c28d51fc4e7da12de6b95f915c83",
|
|||
|
"eab433dc0a754595be140a7bebac7d53",
|
|||
|
"2716e1a313324f17a613d453496da711",
|
|||
|
"493d2cdcbe1048c498818ba831518823",
|
|||
|
"c0683a76665147959ac3590d686dac79",
|
|||
|
"1f4322bee4ab472ab079e125367565f9",
|
|||
|
"fbe1a50893a440ac9f94da7da561724c",
|
|||
|
"6680540157cb4a408f23f5139b5afb7e",
|
|||
|
"7f8864f4e2194bb19ae2259b8fcae171",
|
|||
|
"744ae4c109ee48fb9b5dfd20174a1ba8",
|
|||
|
"069716cdd6a249389631e7372e8bdd1f",
|
|||
|
"2a4ab12c36134facafed947c59e56c1f",
|
|||
|
"db7297bd37e140978362223b898b296e",
|
|||
|
"75b0b357c261489e9de1013f9e744d7b",
|
|||
|
"f6fcf563441841e382b22946a0c62141",
|
|||
|
"2f34c38768f4451490eceea17929ea28",
|
|||
|
"59faac5f23584f419e76f46ff2c67eb8",
|
|||
|
"42434cce3dfc4d71a822bad1c25403ce",
|
|||
|
"e2804fce63cc45caa0a0b10a454b8d5e",
|
|||
|
"137db27055614a15b3254c70caa394ba",
|
|||
|
"54c307e63f804593ad0a31a5cdae1cd1",
|
|||
|
"6933bf7f852148a790a8cda8d6a9c249",
|
|||
|
"632496f950244273b54b25aecd11c744",
|
|||
|
"bde223731a20426a91cf605fdf2b31bf",
|
|||
|
"12d37d60b9fe479c9f2e1b8fcff1a9da",
|
|||
|
"3f17bc5fc47140239f82d75bbb464dbd",
|
|||
|
"07e02b1760ff4ba1a56e53f69f906059",
|
|||
|
"5717dca4a03b41f3af7a31807ce4614e",
|
|||
|
"82dd58f03b1a45c4ba1139ed5e24aab7",
|
|||
|
"f1dc48052bf9460a9687f53f81b68a77",
|
|||
|
"f033acb2cb564a27af86761ecb92cf94",
|
|||
|
"5f6148c4123e40ce83c418b612bab240",
|
|||
|
"9fed501af07041589bdf1cb2e1ae43f6",
|
|||
|
"a498757719ba41a3ba9cc3d7b82ce708",
|
|||
|
"1ae0c81fc80e424a986f872d37dfd105",
|
|||
|
"bc4479f17c904fab8c48437893860666",
|
|||
|
"9eda7ec9e0654d0ebd69b893b2a82446",
|
|||
|
"3fc6686709e54b08a136857ac6746e0f",
|
|||
|
"45a0c58a53644682921c0b7719a5fbef",
|
|||
|
"3e907191449145a9be3ee67fa7608032",
|
|||
|
"65abb05649a643a78f74bf980e733544",
|
|||
|
"10024f2ae6b64ae3b6fbcf9f648fa606",
|
|||
|
"47f2c295b55544358873b0c10e86c3ae",
|
|||
|
"dc857be677954e33af409b3ad0eb8bad",
|
|||
|
"ea664560c3c644bcb1e9dd9aa10fa226",
|
|||
|
"5e754b29dfab4ce0993b352f0db2ae74",
|
|||
|
"410b775106004eb28f186d2465271ee8",
|
|||
|
"86383e136b274ef5b508e863bf0ad8f9",
|
|||
|
"b46a15fb1d574ed9a2271746fad0ab49",
|
|||
|
"6c4bf13f3bc741a3846cbcb303bd3a4b",
|
|||
|
"c088cf42853849e3a368c6079c2ffed3",
|
|||
|
"03509301c905465faff707374157194b",
|
|||
|
"d99a5d547a9a4e1aad28d7bced59712e"
|
|||
|
]
|
|||
|
},
|
|||
|
"id": "U0kG_W5AY7uE",
|
|||
|
"outputId": "578418b8-11d6-4db0-c55a-1084b895b3ce"
|
|||
|
},
|
|||
|
"execution_count": 8,
|
|||
|
"outputs": [
|
|||
|
{
|
|||
|
"output_type": "display_data",
|
|||
|
"data": {
|
|||
|
"text/plain": [
|
|||
|
"Downloading (…)olve/main/vocab.json: 0%| | 0.00/1.04M [00:00<?, ?B/s]"
|
|||
|
],
|
|||
|
"application/vnd.jupyter.widget-view+json": {
|
|||
|
"version_major": 2,
|
|||
|
"version_minor": 0,
|
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|
"model_id": "679cc8100859428eaed49abdb40f93d1"
|
|||
|
}
|
|||
|
},
|
|||
|
"metadata": {}
|
|||
|
},
|
|||
|
{
|
|||
|
"output_type": "display_data",
|
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|
"data": {
|
|||
|
"text/plain": [
|
|||
|
"Downloading (…)olve/main/merges.txt: 0%| | 0.00/456k [00:00<?, ?B/s]"
|
|||
|
],
|
|||
|
"application/vnd.jupyter.widget-view+json": {
|
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|
"version_major": 2,
|
|||
|
"version_minor": 0,
|
|||
|
"model_id": "744ae4c109ee48fb9b5dfd20174a1ba8"
|
|||
|
}
|
|||
|
},
|
|||
|
"metadata": {}
|
|||
|
},
|
|||
|
{
|
|||
|
"output_type": "display_data",
|
|||
|
"data": {
|
|||
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"text/plain": [
|
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"Downloading (…)lve/main/config.json: 0%| | 0.00/718 [00:00<?, ?B/s]"
|
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],
|
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"application/vnd.jupyter.widget-view+json": {
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"version_major": 2,
|
|||
|
"version_minor": 0,
|
|||
|
"model_id": "54c307e63f804593ad0a31a5cdae1cd1"
|
|||
|
}
|
|||
|
},
|
|||
|
"metadata": {}
|
|||
|
},
|
|||
|
{
|
|||
|
"output_type": "display_data",
|
|||
|
"data": {
|
|||
|
"text/plain": [
|
|||
|
"Downloading model.safetensors: 0%| | 0.00/1.52G [00:00<?, ?B/s]"
|
|||
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],
|
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"application/vnd.jupyter.widget-view+json": {
|
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|
"version_major": 2,
|
|||
|
"version_minor": 0,
|
|||
|
"model_id": "5f6148c4123e40ce83c418b612bab240"
|
|||
|
}
|
|||
|
},
|
|||
|
"metadata": {}
|
|||
|
},
|
|||
|
{
|
|||
|
"output_type": "display_data",
|
|||
|
"data": {
|
|||
|
"text/plain": [
|
|||
|
"Downloading (…)neration_config.json: 0%| | 0.00/124 [00:00<?, ?B/s]"
|
|||
|
],
|
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|
"application/vnd.jupyter.widget-view+json": {
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|
"version_major": 2,
|
|||
|
"version_minor": 0,
|
|||
|
"model_id": "47f2c295b55544358873b0c10e86c3ae"
|
|||
|
}
|
|||
|
},
|
|||
|
"metadata": {}
|
|||
|
}
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"execution_count": 1,
|
|||
|
"metadata": {
|
|||
|
"id": "PIUjH8-ow1y9"
|
|||
|
},
|
|||
|
"outputs": [],
|
|||
|
"source": [
|
|||
|
"def create_outputs(folder_name):\n",
|
|||
|
" print(f'Creating outputs in {folder_name}')\n",
|
|||
|
" with lzma.open(f'{folder_name}/in.tsv.xz', mode='rt', encoding='utf-8') as fid:\n",
|
|||
|
" with open(f'{folder_name}/out-{folder_name}.tsv', 'w', encoding='utf-8', newline='\\n') as f:\n",
|
|||
|
" for line in fid:\n",
|
|||
|
" separated = line.split('\\t')\n",
|
|||
|
" prefix = separated[6].replace(r'\\n', ' ')\n",
|
|||
|
" new_val = []\n",
|
|||
|
" for j in range(5):\n",
|
|||
|
" val = 1\n",
|
|||
|
" prefix = separated[6].replace(r'\\n', ' ')\n",
|
|||
|
" for i in range(5):\n",
|
|||
|
" left_context =tokenizer.encode(prefix, return_tensors=\"pt\").to(device)\n",
|
|||
|
" out = model(left_context)\n",
|
|||
|
" prob_dist=torch.softmax(out[0][0][-1],dim=0)\n",
|
|||
|
" values,index =prob_dist.topk(5)\n",
|
|||
|
" token = []\n",
|
|||
|
" total = 0\n",
|
|||
|
" for x in index:\n",
|
|||
|
" token.append(tokenizer.decode(x))\n",
|
|||
|
" zipped = list(zip(values, token))\n",
|
|||
|
" val *=float(values[0])\n",
|
|||
|
" prefix+=' '+zipped[j][1].strip()\n",
|
|||
|
" new_val.append(math.sqrt(val))\n",
|
|||
|
" prefix = separated[6].replace(r'\\n', ' ')\n",
|
|||
|
" left_context =tokenizer.encode(prefix, return_tensors=\"pt\").to(device)\n",
|
|||
|
" out = model(left_context)\n",
|
|||
|
" prob_dist=torch.softmax(out[0][0][-1],dim=0)\n",
|
|||
|
" values,index =prob_dist.topk(5)\n",
|
|||
|
" token = []\n",
|
|||
|
" total = 0\n",
|
|||
|
" for x in index:\n",
|
|||
|
" token.append(tokenizer.decode(x))\n",
|
|||
|
" output = new_val\n",
|
|||
|
" zipped = list(zip(values, token))\n",
|
|||
|
" res = sorted(zipped, key = lambda x: x[1])\n",
|
|||
|
"\n",
|
|||
|
" output_line = ' '.join([f'{x[1].strip()}:{x[0]}' for x in res])\n",
|
|||
|
" f.write(output_line + '\\n')"
|
|||
|
]
|
|||
|
},
|
|||
|
{
|
|||
|
"cell_type": "code",
|
|||
|
"source": [
|
|||
|
"create_outputs('dev-0')\n",
|
|||
|
"create_outputs('test-A')"
|
|||
|
],
|
|||
|
"metadata": {
|
|||
|
"colab": {
|
|||
|
"base_uri": "https://localhost:8080/"
|
|||
|
},
|
|||
|
"id": "iR0OV-fVUo_I",
|
|||
|
"outputId": "dd95cec3-6432-408e-9e3b-1b6592c1c3bb"
|
|||
|
},
|
|||
|
"execution_count": 9,
|
|||
|
"outputs": [
|
|||
|
{
|
|||
|
"output_type": "stream",
|
|||
|
"name": "stdout",
|
|||
|
"text": [
|
|||
|
"Creating outputs in dev-0\n",
|
|||
|
"Creating outputs in test-A\n"
|
|||
|
]
|
|||
|
}
|
|||
|
]
|
|||
|
}
|
|||
|
],
|
|||
|
"metadata": {
|
|||
|
"accelerator": "GPU",
|
|||
|
"colab": {
|
|||
|
"provenance": []
|
|||
|
},
|
|||
|
"kernelspec": {
|
|||
|
"display_name": "Python 3",
|
|||
|
"name": "python3"
|
|||
|
},
|
|||
|
"language_info": {
|
|||
|
"name": "python"
|
|||
|
},
|
|||
|
"widgets": {
|
|||
|
"application/vnd.jupyter.widget-state+json": {
|
|||
|
"679cc8100859428eaed49abdb40f93d1": {
|
|||
|
"model_module": "@jupyter-widgets/controls",
|
|||
|
"model_name": "HBoxModel",
|
|||
|
"model_module_version": "1.5.0",
|
|||
|
"state": {
|
|||
|
"_dom_classes": [],
|
|||
|
"_model_module": "@jupyter-widgets/controls",
|
|||
|
"_model_module_version": "1.5.0",
|
|||
|
"_model_name": "HBoxModel",
|
|||
|
"_view_count": null,
|
|||
|
"_view_module": "@jupyter-widgets/controls",
|
|||
|
"_view_module_version": "1.5.0",
|
|||
|
"_view_name": "HBoxView",
|
|||
|
"box_style": "",
|
|||
|
"children": [
|
|||
|
"IPY_MODEL_c4a853a74ab44187a321bba0e0c242a9",
|
|||
|
"IPY_MODEL_2196c28d51fc4e7da12de6b95f915c83",
|
|||
|
"IPY_MODEL_eab433dc0a754595be140a7bebac7d53"
|
|||
|
],
|
|||
|
"layout": "IPY_MODEL_2716e1a313324f17a613d453496da711"
|
|||
|
}
|
|||
|
},
|
|||
|
"c4a853a74ab44187a321bba0e0c242a9": {
|
|||
|
"model_module": "@jupyter-widgets/controls",
|
|||
|
"model_name": "HTMLModel",
|
|||
|
"model_module_version": "1.5.0",
|
|||
|
"state": {
|
|||
|
"_dom_classes": [],
|
|||
|
"_model_module": "@jupyter-widgets/controls",
|
|||
|
"_model_module_version": "1.5.0",
|
|||
|
"_model_name": "HTMLModel",
|
|||
|
"_view_count": null,
|
|||
|
"_view_module": "@jupyter-widgets/controls",
|
|||
|
"_view_module_version": "1.5.0",
|
|||
|
"_view_name": "HTMLView",
|
|||
|
"description": "",
|
|||
|
"description_tooltip": null,
|
|||
|
"layout": "IPY_MODEL_493d2cdcbe1048c498818ba831518823",
|
|||
|
"placeholder": "",
|
|||
|
"style": "IPY_MODEL_c0683a76665147959ac3590d686dac79",
|
|||
|
"value": "Downloading (…)olve/main/vocab.json: 100%"
|
|||
|
}
|
|||
|
},
|
|||
|
"2196c28d51fc4e7da12de6b95f915c83": {
|
|||
|
"model_module": "@jupyter-widgets/controls",
|
|||
|
"model_name": "FloatProgressModel",
|
|||
|
"model_module_version": "1.5.0",
|
|||
|
"state": {
|
|||
|
"_dom_classes": [],
|
|||
|
"_model_module": "@jupyter-widgets/controls",
|
|||
|
"_model_module_version": "1.5.0",
|
|||
|
"_model_name": "FloatProgressModel",
|
|||
|
"_view_count": null,
|
|||
|
"_view_module": "@jupyter-widgets/controls",
|
|||
|
"_view_module_version": "1.5.0",
|
|||
|
"_view_name": "ProgressView",
|
|||
|
"bar_style": "success",
|
|||
|
"description": "",
|
|||
|
"description_tooltip": null,
|
|||
|
"layout": "IPY_MODEL_1f4322bee4ab472ab079e125367565f9",
|
|||
|
"max": 1042301,
|
|||
|
"min": 0,
|
|||
|
"orientation": "horizontal",
|
|||
|
"style": "IPY_MODEL_fbe1a50893a440ac9f94da7da561724c",
|
|||
|
"value": 1042301
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|||
|
}
|
|||
|
},
|
|||
|
"eab433dc0a754595be140a7bebac7d53": {
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|
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"state": {
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|
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|
"_model_module": "@jupyter-widgets/controls",
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|
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|
|||
|
"_model_name": "HTMLModel",
|
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|
"_view_count": null,
|
|||
|
"_view_module": "@jupyter-widgets/controls",
|
|||
|
"_view_module_version": "1.5.0",
|
|||
|
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|
|||
|
"description": "",
|
|||
|
"description_tooltip": null,
|
|||
|
"layout": "IPY_MODEL_6680540157cb4a408f23f5139b5afb7e",
|
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