From 81e0329199fb1b03513db513189b5a12bdbd4edc Mon Sep 17 00:00:00 2001 From: kubapok Date: Mon, 17 May 2021 15:24:29 +0200 Subject: [PATCH] seq labelling work in progress --- cw/09_sequence_labeling_ODPOWIEDZI.ipynb | 17845 ++++++++++++--------- 1 file changed, 10595 insertions(+), 7250 deletions(-) diff --git a/cw/09_sequence_labeling_ODPOWIEDZI.ipynb b/cw/09_sequence_labeling_ODPOWIEDZI.ipynb index 569af38..75d0ef3 100644 --- a/cw/09_sequence_labeling_ODPOWIEDZI.ipynb +++ b/cw/09_sequence_labeling_ODPOWIEDZI.ipynb @@ -7,6 +7,13 @@ "# Klasyfikacja wieloklasowa i sequence labelling" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "https://www.aclweb.org/anthology/W03-0419.pdf" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -393,7 +400,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -403,7 +410,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -413,7 +420,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -422,7 +429,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -431,7 +438,7 @@ "array([17])" ] }, - "execution_count": 26, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -442,7 +449,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -454,7 +461,7 @@ " 0.04601515, 0.03421612, 0.31493021, 0.06406168, 0.02710846]])" ] }, - "execution_count": 27, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -465,7 +472,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -474,7 +481,7 @@ "0.314930213117783" ] }, - "execution_count": 28, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -485,7 +492,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -494,7 +501,7 @@ "0.9558043606364172" ] }, - "execution_count": 29, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -505,7 +512,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -514,7 +521,7 @@ "0.8745139625309296" ] }, - "execution_count": 30, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -525,7 +532,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -534,7 +541,7 @@ "0.8027084439723845" ] }, - "execution_count": 31, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -561,7 +568,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -571,7 +578,7 @@ "\twith 1115170 stored elements in Compressed Sparse Row format>" ] }, - "execution_count": 33, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -582,7 +589,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -602,7 +609,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -611,7 +618,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -620,24 +627,24 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "tensor([[0.3320, 0.3328, 0.3347, 0.3342, 0.3328, 0.3325, 0.3304, 0.3357, 0.3349,\n", - " 0.3329, 0.3312, 0.3340, 0.3350, 0.3334, 0.3349, 0.3319, 0.3340, 0.3345,\n", - " 0.3326, 0.3322],\n", - " [0.3355, 0.3337, 0.3323, 0.3318, 0.3339, 0.3345, 0.3339, 0.3306, 0.3355,\n", - " 0.3343, 0.3336, 0.3328, 0.3331, 0.3309, 0.3318, 0.3349, 0.3337, 0.3318,\n", - " 0.3338, 0.3337],\n", - " [0.3325, 0.3335, 0.3331, 0.3340, 0.3333, 0.3330, 0.3357, 0.3337, 0.3296,\n", - " 0.3328, 0.3352, 0.3332, 0.3319, 0.3357, 0.3333, 0.3332, 0.3323, 0.3337,\n", - " 0.3337, 0.3341]], grad_fn=)" + "tensor([[0.3347, 0.3319, 0.3343, 0.3335, 0.3365, 0.3322, 0.3336, 0.3335, 0.3365,\n", + " 0.3299, 0.3329, 0.3343, 0.3303, 0.3343, 0.3338, 0.3325, 0.3327, 0.3330,\n", + " 0.3331, 0.3320],\n", + " [0.3317, 0.3330, 0.3296, 0.3341, 0.3297, 0.3341, 0.3311, 0.3346, 0.3334,\n", + " 0.3339, 0.3313, 0.3351, 0.3344, 0.3334, 0.3337, 0.3343, 0.3337, 0.3333,\n", + " 0.3337, 0.3345],\n", + " [0.3337, 0.3351, 0.3360, 0.3324, 0.3338, 0.3337, 0.3352, 0.3319, 0.3301,\n", + " 0.3362, 0.3358, 0.3306, 0.3354, 0.3323, 0.3325, 0.3332, 0.3337, 0.3337,\n", + " 0.3331, 0.3336]], grad_fn=)" ] }, - "execution_count": 37, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -648,7 +655,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -657,7 +664,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -666,7 +673,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -676,7 +683,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -702,7 +709,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 40, "metadata": { "scrolled": true }, @@ -719,7 +726,7 @@ { "data": { "text/plain": [ - "(-0.21234585829496244, 0.6987625220978196)" + "(-0.21239922212795714, 0.6947554507955215)" ] }, "metadata": {}, @@ -728,7 +735,7 @@ { "data": { "text/plain": [ - "(-0.21147151558948685, 0.6341463414634146)" + "(-0.2115392372480927, 0.6376811594202898)" ] }, "metadata": {}, @@ -746,7 +753,7 @@ { "data": { "text/plain": [ - "(-0.22606862114679835, 0.7084266352386565)" + "(-0.226128276834856, 0.705833824395993)" ] }, "metadata": {}, @@ -755,7 +762,7 @@ { "data": { "text/plain": [ - "(-0.22413486314982903, 0.6489925768822906)" + "(-0.22420935996980287, 0.6422764227642277)" ] }, "metadata": {}, @@ -773,7 +780,7 @@ { "data": { "text/plain": [ - "(-0.24118578127537604, 0.7084266352386565)" + "(-0.24125191222483366, 0.7097230406599883)" ] }, "metadata": {}, @@ -782,7 +789,7 @@ { "data": { "text/plain": [ - "(-0.23807573430674706, 0.6504065040650406)" + "(-0.23815746801866158, 0.6489925768822906)" ] }, "metadata": {}, @@ -800,7 +807,7 @@ { "data": { "text/plain": [ - "(-0.25767021444030697, 0.7105480259281084)" + "(-0.25774253752910187, 0.7105480259281084)" ] }, "metadata": {}, @@ -809,7 +816,7 @@ { "data": { "text/plain": [ - "(-0.25327372663157616, 0.6514669494521033)" + "(-0.2533627246411372, 0.6489925768822906)" ] }, "metadata": {}, @@ -827,7 +834,7 @@ { "data": { "text/plain": [ - "(-0.27537571211632517, 0.7124337065409546)" + "(-0.27545327459213376, 0.7123158515026518)" ] }, "metadata": {}, @@ -836,7 +843,7 @@ { "data": { "text/plain": [ - "(-0.26960182536529037, 0.6532343584305408)" + "(-0.26969742850285616, 0.6504065040650406)" ] }, "metadata": {}, @@ -875,16 +882,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([5, 10000])" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "X.shape" ] }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -1893,7 +1911,7 @@ " ...]" ] }, - "execution_count": 43, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -1906,107 +1924,71 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Podejście softmax z embeddingami" + "### Podejście softmax z embeddingami na przykładzie NER" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": { "scrolled": true }, "outputs": [], "source": [ - "# !pip install torchtext\n" + "# !pip install torchtext\n", + "# !pip install datasets" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, + "execution_count": 44, + "metadata": {}, "outputs": [], "source": [ - "!pip install spacy" + "from datasets import load_dataset\n", + "from torchtext.vocab import Vocab\n", + "from collections import Counter" ] }, { "cell_type": "code", "execution_count": 45, - "metadata": {}, - "outputs": [], + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Reusing dataset conll2003 (/home/kuba/.cache/huggingface/datasets/conll2003/conll2003/1.0.0/40e7cb6bcc374f7c349c83acd1e9352a4f09474eb691f64f364ee62eb65d0ca6)\n" + ] + } + ], "source": [ - "from torchtext.data.utils import get_tokenizer\n", - "from torchtext.vocab import Vocab\n", - "from collections import Counter\n" + "dataset = load_dataset(\"conll2003\")" ] }, { "cell_type": "code", "execution_count": 46, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: en-core-web-sm==3.0.0 from https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.0.0/en_core_web_sm-3.0.0-py3-none-any.whl#egg=en_core_web_sm==3.0.0 in /media/kuba/ssdsam/anaconda3/lib/python3.8/site-packages (3.0.0)\n", - "Requirement already satisfied: spacy<3.1.0,>=3.0.0 in /media/kuba/ssdsam/anaconda3/lib/python3.8/site-packages (from en-core-web-sm==3.0.0) (3.0.6)\n", - "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /media/kuba/ssdsam/anaconda3/lib/python3.8/site-packages (from spacy<3.1.0,>=3.0.0->en-core-web-sm==3.0.0) (3.0.5)\n", - "Requirement already satisfied: 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(2.10)\n", - "Requirement already satisfied: certifi>=2017.4.17 in /media/kuba/ssdsam/anaconda3/lib/python3.8/site-packages (from requests<3.0.0,>=2.13.0->spacy<3.1.0,>=3.0.0->en-core-web-sm==3.0.0) (2020.6.20)\n", - "Requirement already satisfied: six in /media/kuba/ssdsam/anaconda3/lib/python3.8/site-packages (from packaging>=20.0->spacy<3.1.0,>=3.0.0->en-core-web-sm==3.0.0) (1.15.0)\n", - "Requirement already satisfied: pyparsing>=2.0.2 in /media/kuba/ssdsam/anaconda3/lib/python3.8/site-packages (from packaging>=20.0->spacy<3.1.0,>=3.0.0->en-core-web-sm==3.0.0) (2.4.7)\n", - "Requirement already satisfied: smart-open<4.0.0,>=2.2.0 in /media/kuba/ssdsam/anaconda3/lib/python3.8/site-packages (from pathy>=0.3.5->spacy<3.1.0,>=3.0.0->en-core-web-sm==3.0.0) (3.0.0)\n", - "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n", - "You can now load the package via spacy.load('en_core_web_sm')\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ - "!python -m spacy download en_core_web_sm" + "def build_vocab(dataset):\n", + " counter = Counter()\n", + " for document in dataset:\n", + " counter.update(document)\n", + " return Vocab(counter, specials=['', '', '', ''])" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/media/kuba/ssdsam/anaconda3/lib/python3.8/site-packages/torchtext/data/utils.py:123: UserWarning: Spacy model \"en\" could not be loaded, trying \"en_core_web_sm\" instead\n", - " warnings.warn(f'Spacy model \"{language}\" could not be loaded, trying \"{OLD_MODEL_SHORTCUTS[language]}\" instead')\n" - ] - } - ], + "outputs": [], "source": [ - "tokenizer = get_tokenizer('spacy', language='en')" + "vocab = build_vocab(dataset['train']['tokens'])" ] }, { @@ -2017,7 +1999,10129 @@ { "data": { "text/plain": [ - "functools.partial(, spacy=)" + "[['EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'lamb', '.'],\n", + " ['Peter', 'Blackburn'],\n", + " ['BRUSSELS', '1996-08-22'],\n", + " ['The',\n", + " 'European',\n", + " 'Commission',\n", + " 'said',\n", + " 'on',\n", + " 'Thursday',\n", + " 'it',\n", + " 'disagreed',\n", + " 'with',\n", + " 'German',\n", + " 'advice',\n", + " 'to',\n", + " 'consumers',\n", + " 'to',\n", + " 'shun',\n", + " 'British',\n", + " 'lamb',\n", + " 'until',\n", + " 'scientists',\n", + " 'determine',\n", + " 'whether',\n", + " 'mad',\n", + " 'cow',\n", + " 'disease',\n", + " 'can',\n", + " 'be',\n", + " 'transmitted',\n", + " 'to',\n", + " 'sheep',\n", + " '.'],\n", + " ['Germany',\n", + " \"'s\",\n", + " 'representative',\n", + " 'to',\n", + " 'the',\n", + " 'European',\n", + " 'Union',\n", + " \"'s\",\n", + " 'veterinary',\n", + " 'committee',\n", + " 'Werner',\n", + " 'Zwingmann',\n", + " 'said',\n", + " 'on',\n", + " 'Wednesday',\n", + " 'consumers',\n", + " 'should',\n", + " 'buy',\n", + " 'sheepmeat',\n", + " 'from',\n", + " 'countries',\n", + " 'other',\n", + " 'than',\n", + " 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'.'],\n", + " ['GOLF', '-', 'GERMAN', 'OPEN', 'FIRST', 'ROUND', 'SCORES', '.'],\n", + " ['STUTTGART', ',', 'Germany', '1996-08-22'],\n", + " ['Leading', 'first', 'round'],\n", + " ['scores',\n", + " 'in',\n", + " 'the',\n", + " 'German',\n", + " 'Open',\n", + " 'golf',\n", + " 'championship',\n", + " 'on',\n", + " 'Thursday',\n", + " '(',\n", + " 'Britain'],\n", + " ['unless', 'stated', ')', ':'],\n", + " ['62', 'Paul', 'Broadhurst'],\n", + " ['63', 'Raymond', 'Russell'],\n", + " ['64',\n", + " 'David',\n", + " 'J.',\n", + " 'Russell',\n", + " ',',\n", + " 'Michael',\n", + " 'Campbell',\n", + " '(',\n", + " 'New',\n", + " 'Zealand',\n", + " ')',\n", + " ',',\n", + " 'Ian'],\n", + " ['Woosnam',\n", + " ',',\n", + " 'Bernhard',\n", + " 'Langer',\n", + " '(',\n", + " 'Germany',\n", + " ')',\n", + " ',',\n", + " 'Ronan',\n", + " 'Rafferty',\n", + " ',',\n", + " 'Mats'],\n", + " ['Lanner', '(', 'Sweden', ')', ',', 'Wayne', 'Riley', '(', 'Australia', ')'],\n", + " ['65',\n", + " 'Eamonn',\n", + " 'Darcy',\n", + " '(',\n", + " 'Ireland',\n", + " ')',\n", + " ',',\n", + " 'Per',\n", + " 'Nyman',\n", + " '(',\n", + " 'Sweden',\n", + " ')',\n", + " ',',\n", + " 'Russell',\n", + " 'Claydon',\n", + " ','],\n", + " ['Mark',\n", + " 'Roe',\n", + " ',',\n", + " 'Retief',\n", + " 'Goosen',\n", + " '(',\n", + " 'South',\n", + " 'Africa',\n", + " ')',\n", + " ',',\n", + " 'Carl',\n", + " 'Suneson'],\n", + " ['66',\n", + " 'Stephen',\n", + " 'Field',\n", + " ',',\n", + " 'Paul',\n", + " 'Lawrie',\n", + " ',',\n", + " 'Ian',\n", + " 'Pyman',\n", + " ',',\n", + " 'Max',\n", + " 'Anglert'],\n", + " ['(',\n", + " 'Sweden',\n", + " ')',\n", + " ',',\n", + " 'Miles',\n", + " 'Tunnicliff',\n", + " ',',\n", + " 'Christian',\n", + " 'Cevaer',\n", + " '(',\n", + " 'France',\n", + " ')',\n", + " ','],\n", + " ['Des',\n", + " 'Smyth',\n", + " '(',\n", + " 'Ireland',\n", + " ')',\n", + " ',',\n", + " 'David',\n", + " 'Carter',\n", + " ',',\n", + " 'Lee',\n", + " 'Westwood',\n", + " 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'Michael',\n", + " 'Jonzon'],\n", + " ['(',\n", + " 'Sweden',\n", + " ')',\n", + " ',',\n", + " 'Paul',\n", + " 'Eales',\n", + " ',',\n", + " 'David',\n", + " 'Williams',\n", + " ',',\n", + " 'Andrew',\n", + " 'Coltart',\n", + " ','],\n", + " ['Jonathan',\n", + " 'Lomas',\n", + " ',',\n", + " 'Jose',\n", + " 'Rivero',\n", + " '(',\n", + " 'Spain',\n", + " ')',\n", + " ',',\n", + " 'Robert',\n", + " 'Karlsson'],\n", + " ['(',\n", + " 'Sweden',\n", + " ')',\n", + " ',',\n", + " 'Marcus',\n", + " 'Wills',\n", + " ',',\n", + " 'Pedro',\n", + " 'Linhart',\n", + " '(',\n", + " 'Spain',\n", + " ')',\n", + " ',',\n", + " 'Jamie'],\n", + " ['Spence',\n", + " ',',\n", + " 'Terry',\n", + " 'Price',\n", + " '(',\n", + " 'Australia',\n", + " ')',\n", + " ',',\n", + " 'Juan',\n", + " 'Carlos',\n", + " 'Pinero',\n", + " '(',\n", + " 'Spain',\n", + " ')',\n", + " ','],\n", + " ['Mark', 'Mouland'],\n", + " ['SOCCER',\n", + " '-',\n", + " 'UEFA',\n", + " 'REWARDS',\n", + " 'THREE',\n", + " 'COUNTRIES',\n", + " 'FOR',\n", + " 'FAIR',\n", + " 'PLAY',\n", + " '.'],\n", + " ['GENEVA', '1996-08-22'],\n", + " ['Norway',\n", + " ',',\n", + " 'England',\n", + " 'and',\n", + " 'Sweden',\n", + " 'were',\n", + " 'rewarded',\n", + " 'for',\n", + " 'their',\n", + " 'fair',\n", + " 'play',\n", + " 'on',\n", + " 'Thursday',\n", + " 'with',\n", + " 'an',\n", + " 'additional',\n", + " 'place',\n", + " 'in',\n", + " 'the',\n", + " '1997-98',\n", + " 'UEFA',\n", + " 'Cup',\n", + " 'competition',\n", + " '.'],\n", + " ['Norway',\n", + " 'headed',\n", + " 'the',\n", + " 'UEFA',\n", + " 'Fair',\n", + " 'Play',\n", + " 'rankings',\n", + " 'for',\n", + " '1995-96',\n", + " 'with',\n", + " '8.62',\n", + " 'points',\n", + " ',',\n", + " 'ahead',\n", + " 'of',\n", + " 'England',\n", + " 'with',\n", + " '8.61',\n", + " 'and',\n", + " 'Sweden',\n", + " '8.57',\n", + " '.'],\n", + " ['The',\n", + " 'rankings',\n", + " 'are',\n", + " 'based',\n", + " 'on',\n", + " 'a',\n", + " 'formula',\n", + " 'that',\n", + " 'takes',\n", + " 'into',\n", + " 'account',\n", + " 'many',\n", + " 'factors',\n", + " 'including',\n", + " 'red',\n", + " 'and',\n", + " 'yellow',\n", + " 'cards',\n", + " ',',\n", + " 'and',\n", + " 'coaching',\n", + " 'and',\n", + " 'spectators',\n", + " \"'\",\n", + " 'behaviour',\n", + " 'at',\n", + " 'matches',\n", + " 'played',\n", + " 'at',\n", + " 'an',\n", + " 'international',\n", + " 'level',\n", + " 'by',\n", + " 'clubs',\n", + " 'and',\n", + " 'national',\n", + " 'teams',\n", + " '.'],\n", + " ['Only',\n", + " 'the',\n", + " 'top',\n", + " 'three',\n", + " 'countries',\n", + " 'are',\n", + " 'allocated',\n", + " 'additional',\n", + " 'places',\n", + " '.'],\n", + " ['The', 'UEFA', 'Fair', 'Play', 'rankings', 'are', ':', '1', '.'],\n", + " ['Norway', '8.62', 'points'],\n", + " ['2.', 'England', '8.61'],\n", + " ['3.', 'Sweden', '8.57'],\n", + " ['4.', 'Faroe', 'Islands', '8.56'],\n", + " ['5.', 'Wales', '8.54'],\n", + " ['6.', 'Estonia', '8.52'],\n", + " ['7.', 'Ireland', '8.45'],\n", + " ['8.', 'Belarus', '8.39'],\n", + " ['9.', 'Iceland', '8.35'],\n", + " ['10.', 'Netherlands', '8.30'],\n", + " ['10.', 'Denmark', '8.30'],\n", + " ['10.', 'Germany', '8.30'],\n", + " ['13.', 'Scotland', '8.29'],\n", + " ['13.', 'Latvia', '8.29'],\n", + " ['15.', 'Moldova', '8.24'],\n", + " ['16.', 'Yugoslavia', '8.22'],\n", + " ['16.', 'Belgium', '8.22'],\n", + " ['18.', 'Luxembourg', '8.20'],\n", + " ['19.', 'France', '8.18'],\n", + " ['20.', 'Israel', '8.17'],\n", + " ['21.', 'Switzerland', '8.15'],\n", + " ['21.', 'Slovakia', '8.15'],\n", + " ['23.', 'Poland', '8.12'],\n", + " ['23.', 'Portugal', '8.12'],\n", + " ['25.', 'Georgia', '8.10'],\n", + " ['26.', 'Ukraine', '8.09'],\n", + " ['26.', 'Spain', '8.09'],\n", + " ['26.', 'Finland', '8.09'],\n", + " ['29.', 'Macedonia', '8.07'],\n", + " ['30.', 'Lithuania', '8.06'],\n", + " ['31.', 'Austria', '8.05'],\n", + " ['32.', 'Russia', '8.03'],\n", + " ['33.', 'Romania', '8.02'],\n", + " ['33.', 'Turkey', '8.02'],\n", + " ['35.', 'Hungary', '7.98'],\n", + " ['36.', 'Czech', 'Republic', '7.95'],\n", + " ['37.', 'Greece', '7.89'],\n", + " ['37.', 'Northern', 'Ireland', '7.89'],\n", + " ['39.', 'Italy', '7.85'],\n", + " ['40.', 'Cyprus', '7.83'],\n", + " ['41.', 'Armenia', '7.80'],\n", + " ['42.', 'Slovenia', '7.77'],\n", + " ['43.', 'Croatia', '7.75'],\n", + " ['44.', 'Bulgaria', '7.73'],\n", + " ['45.', 'Malta', '7.40'],\n", + " ['CRICKET',\n", + " '-',\n", + " 'POLICE',\n", + " 'COMMANDOS',\n", + " 'ON',\n", + " 'HAND',\n", + " 'FOR',\n", + " 'AUSTRALIANS',\n", + " \"'\",\n", + " 'FIRST',\n", + " 'MATCH',\n", + " '.'],\n", + " ['COLOMBO', '1996-08-22'],\n", + " ['Armed',\n", + " 'police',\n", + " 'commandos',\n", + " 'patrolled',\n", + " 'the',\n", + " 'ground',\n", + " 'when',\n", + " 'Australia',\n", + " 'opened',\n", + " 'their',\n", + " 'short',\n", + " 'tour',\n", + " 'of',\n", + " 'Sri',\n", + " 'Lanka',\n", + " 'with',\n", + " 'a',\n", + " 'five-run',\n", + " 'win',\n", + " 'over',\n", + " 'the',\n", + " 'country',\n", + " \"'s\",\n", + " 'youth',\n", + " 'team',\n", + " 'on',\n", + " 'Thursday',\n", + " '.'],\n", + " ['Australia',\n", + " ',',\n", + " 'in',\n", + " 'Sri',\n", + " 'Lanka',\n", + " 'for',\n", + " 'a',\n", + " 'limited',\n", + " 'overs',\n", + " 'tournament',\n", + " 'which',\n", + " 'also',\n", + " 'includes',\n", + " 'India',\n", + " 'and',\n", + " 'Zimbabwe',\n", + " ',',\n", + " 'have',\n", + " 'been',\n", + " 'promised',\n", + " 'the',\n", + " 'presence',\n", + " 'of',\n", + " 'commandos',\n", + " ',',\n", + " 'sniffer',\n", + " 'dogs',\n", + " 'and',\n", + " 'plainclothes',\n", + " 'policemen',\n", + " 'to',\n", + " 'ensure',\n", + " 'the',\n", + " 'tournament',\n", + " 'is',\n", + " 'trouble-free',\n", + " '.'],\n", + " ['They',\n", + " 'are',\n", + " 'making',\n", + " 'their',\n", + " 'first',\n", + " 'visit',\n", + " 'to',\n", + " 'the',\n", + " 'island',\n", + " 'since',\n", + " 'boycotting',\n", + " 'a',\n", + " 'World',\n", + " 'Cup',\n", + " 'fixture',\n", + " 'in',\n", + " 'February',\n", + " 'because',\n", + " 'of',\n", + " 'fears',\n", + " 'over',\n", + " 'ethnic',\n", + " 'violence',\n", + " '.'],\n", + " ['Australia',\n", + " ',',\n", + " 'batting',\n", + " 'first',\n", + " 'in',\n", + " 'Thursday',\n", + " \"'s\",\n", + " 'the',\n", + " 'warm-up',\n", + " 'match',\n", + " ',',\n", + " 'scored',\n", + " '251',\n", + " 'for',\n", + " 'seven',\n", + " 'from',\n", + " 'their',\n", + " '50',\n", + " 'overs',\n", + " '.'],\n", + " ['Ricky',\n", + " 'Ponting',\n", + " 'led',\n", + " 'the',\n", + " 'way',\n", + " 'with',\n", + " '100',\n", + " 'off',\n", + " '119',\n", + " 'balls',\n", + " 'with',\n", + " 'two',\n", + " 'sixes',\n", + " 'and',\n", + " 'nine',\n", + " 'fours',\n", + " 'before',\n", + " 'retiring',\n", + " '.'],\n", + " ['The', 'youth', 'side', 'replied', 'with', '246', 'for', 'seven', '.'],\n", + " ...]" ] }, "execution_count": 48, @@ -2026,178 +12130,47 @@ } ], "source": [ - "tokenizer" + "dataset['train']['tokens']" ] }, { "cell_type": "code", - "execution_count": 53, - "metadata": { - "scrolled": true - }, + "execution_count": 49, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['From',\n", - " ':',\n", - " 'DSHAL@vmd.cso.uiuc.edu',\n", - " '\\n',\n", - " 'Subject',\n", - " ':',\n", - " 'Re',\n", - " ':',\n", - " 'Clintons',\n", - " 'views',\n", - " 'on',\n", - " 'Jerusalem',\n", - " '\\n',\n", - " 'Organization',\n", - " ':',\n", - " 'C.C.S.O.',\n", - " '\\n',\n", - " 'Lines',\n", - " ':',\n", - " '10',\n", - " '\\n\\n',\n", - " 'It',\n", - " 'seems',\n", - " 'that',\n", - " 'President',\n", - " 'Clinton',\n", - " 'can',\n", - " 'recognize',\n", - " 'Jerusalem',\n", - " 'as',\n", - " 'Israels',\n", - " 'capitol',\n", - " '\\n',\n", - " 'while',\n", - " 'still',\n", - " 'keeping',\n", - " 'his',\n", - " 'diplomatic',\n", - " 'rear',\n", - " 'door',\n", - " 'open',\n", - " 'by',\n", - " 'stating',\n", - " 'that',\n", - " 'the',\n", - " 'Parties',\n", - " '\\n',\n", - " 'concerned',\n", - " 'should',\n", - " 'decide',\n", - " 'the',\n", - " 'city',\n", - " \"'s\",\n", - " 'final',\n", - " 'status',\n", - " '.',\n", - " 'Even',\n", - " 'as',\n", - " 'I',\n", - " 'endorse',\n", - " 'Clintons',\n", - " 'vie',\n", - " '\\n',\n", - " 'w',\n", - " '(',\n", - " 'of',\n", - " 'course',\n", - " ')',\n", - " ',',\n", - " 'it',\n", - " 'is',\n", - " 'definitely',\n", - " 'a',\n", - " 'matter',\n", - " 'to',\n", - " 'be',\n", - " 'decided',\n", - " 'upon',\n", - " 'by',\n", - " 'Israel',\n", - " '(',\n", - " 'and',\n", - " '\\n',\n", - " 'other',\n", - " 'participating',\n", - " 'neighboring',\n", - " 'contries',\n", - " ')',\n", - " '.',\n", - " '\\n',\n", - " 'I',\n", - " 'see',\n", - " 'no',\n", - " 'real',\n", - " 'conflict',\n", - " 'in',\n", - " 'stating',\n", - " 'both',\n", - " 'views',\n", - " ',',\n", - " 'nor',\n", - " 'expect',\n", - " 'any',\n", - " 'better',\n", - " 'from',\n", - " '\\n',\n", - " 'politicians',\n", - " '.',\n", - " '\\n',\n", - " '-----',\n", - " '\\n',\n", - " 'David',\n", - " 'Shalhevet',\n", - " '/',\n", - " 'dshal@vmd.cso.uiuc.edu',\n", - " '/',\n", - " 'University',\n", - " 'of',\n", - " 'Illinois',\n", - " '\\n',\n", - " 'Dept',\n", - " 'Anim',\n", - " 'Sci',\n", - " '/',\n", - " '220',\n", - " 'PABL',\n", - " '/',\n", - " '1201',\n", - " 'W.',\n", - " 'Gregory',\n", - " 'Dr.',\n", - " '/',\n", - " 'Urbana',\n", - " ',',\n", - " 'IL',\n", - " '61801',\n", - " '\\n']" + "23627" ] }, - "execution_count": 53, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "tokenizer(newsgroups_train_text[0])" + "len(vocab.itos)" ] }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 50, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "15" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "def build_vocab(dataset, tokenizer):\n", - " counter = Counter()\n", - " for document in newsgroups_train_text:\n", - " counter.update(tokenizer(document))\n", - " return Vocab(counter, specials=['', '', '', ''])" + "vocab['on']" ] }, { @@ -2206,156 +12179,88 @@ "metadata": {}, "outputs": [], "source": [ - "vocab = build_vocab(newsgroups_train_text, tokenizer)" + "def data_process(dt):\n", + " return [ torch.tensor([vocab['']] +[vocab[token] for token in document ] + [vocab['']], dtype = torch.long) for document in dt]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "def labels_process(dt):\n", + " return [ torch.tensor([0] + document + [0], dtype = torch.long) for document in dt]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "train_tokens_ids = data_process(dataset['train']['tokens'])" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [], + "source": [ + "test_tokens_ids = data_process(dataset['test']['tokens'])" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "train_labels = labels_process(dataset['train']['ner_tags'])" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [], + "source": [ + "test_labels = labels_process(dataset['test']['ner_tags'])" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([ 2, 966, 22409, 238, 773, 9, 4588, 212, 7686, 4,\n", + " 3])" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_tokens_ids[0]" ] }, { "cell_type": "code", "execution_count": 57, - "metadata": { - "scrolled": true - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[52,\n", - " 9,\n", - " 85832,\n", - " 4,\n", - " 53,\n", - " 9,\n", - " 76,\n", - " 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11282,\n", - " 58156,\n", - " 16,\n", - " 6,\n", - " 4,\n", - " 21,\n", - " 160,\n", - " 102,\n", - " 355,\n", - " 2644,\n", - " 24,\n", - " 5190,\n", - " 292,\n", - " 1332,\n", - " 5,\n", - " 1051,\n", - " 972,\n", - " 93,\n", - " 237,\n", - " 59,\n", - " 4,\n", - " 7044,\n", - " 6,\n", - " 4,\n", - " 2303,\n", - " 4,\n", - " 278,\n", - " 116400,\n", - " 41,\n", - " 131502,\n", - " 41,\n", - " 101,\n", - " 12,\n", - " 1231,\n", - " 4,\n", - " 652,\n", - " 48358,\n", - " 3638,\n", - " 41,\n", - " 8123,\n", - " 111224,\n", - " 41,\n", - " 22507,\n", - " 1598,\n", - " 3908,\n", - " 1178,\n", - " 41,\n", - " 1971,\n", - " 5,\n", - " 2720,\n", - " 29726,\n", - " 4]" + "8" ] }, "execution_count": 57, @@ -2364,7 +12269,7 @@ } ], "source": [ - "[vocab[token] for token in tokenizer(newsgroups_train_text[0])]" + "max([max(x) for x in dataset['train']['ner_tags'] ])" ] }, { @@ -2373,6911 +12278,351 @@ "metadata": {}, "outputs": [], "source": [ - "def data_process(dataset):\n", - " data = []\n", - " for document in dataset:\n", - " tensor_ = torch.tensor([vocab[token] for token in tokenizer(document)],\n", - " dtype=torch.long)\n", - " data.append(tensor_)\n", - " return data" + "class NERModel(torch.nn.Module):\n", + "\n", + " def __init__(self,):\n", + " super(NERModel, self).__init__()\n", + " self.emb = torch.nn.Embedding(23627,200)\n", + " self.fc1 = torch.nn.Linear(600,9)\n", + " #self.softmax = torch.nn.Softmax(dim=0)\n", + " # nie trzeba, bo używamy https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html\n", + " # jako kryterium\n", + " \n", + "\n", + " def forward(self, x):\n", + " x = self.emb(x)\n", + " x = x.reshape(600) \n", + " x = self.fc1(x)\n", + " #x = self.softmax(x)\n", + " return x" ] }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([ 966, 22409, 238])" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_tokens_ids[0][1:4]" + ] + }, + { + "cell_type": "code", + "execution_count": 92, "metadata": {}, "outputs": [], "source": [ - "processed = data_process(newsgroups_train_text)" + "ner_model = NERModel()" ] }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 93, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "8485" + "tensor([-0.6856, 0.8183, -0.4898, -0.3286, -0.2323, 0.4644, 0.3463, 0.3909,\n", + " -0.1110], grad_fn=)" ] }, - "execution_count": 72, + "execution_count": 93, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "len(processed)" + "ner_model(train_tokens_ids[0][1:4])" ] }, { "cell_type": "code", - "execution_count": 73, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting datasets\n", - " Using cached datasets-1.6.2-py3-none-any.whl (221 kB)\n", - "Requirement already satisfied: numpy>=1.17 in 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cached multiprocess-0.70.11.1-py38-none-any.whl (126 kB)\n", - "Collecting huggingface-hub<0.1.0\n", - " Using cached huggingface_hub-0.0.8-py3-none-any.whl (34 kB)\n", - "Collecting xxhash\n", - " Using cached xxhash-2.0.2-cp38-cp38-manylinux2010_x86_64.whl (243 kB)\n", - "Requirement already satisfied: chardet<4,>=3.0.2 in /media/kuba/ssdsam/anaconda3/lib/python3.8/site-packages (from requests>=2.19.0->datasets) (3.0.4)\n", - "Requirement already satisfied: certifi>=2017.4.17 in /media/kuba/ssdsam/anaconda3/lib/python3.8/site-packages (from requests>=2.19.0->datasets) (2020.6.20)\n", - "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /media/kuba/ssdsam/anaconda3/lib/python3.8/site-packages (from requests>=2.19.0->datasets) (1.25.10)\n", - "Requirement already satisfied: idna<3,>=2.5 in /media/kuba/ssdsam/anaconda3/lib/python3.8/site-packages (from requests>=2.19.0->datasets) (2.10)\n", - "Requirement already satisfied: python-dateutil>=2.7.3 in 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"execution_count": 74, + "execution_count": 94, "metadata": {}, "outputs": [], "source": [ - "from datasets import load_dataset" + "criterion = torch.nn.CrossEntropyLoss()" ] }, { "cell_type": "code", - "execution_count": 76, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "270fe968cf8e465da01325acc041e24f", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=2603.0, style=ProgressStyle(description…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "98275d240d2840939274c46df3c4255f", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1781.0, style=ProgressStyle(description…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Downloading and preparing dataset conll2003/conll2003 (download: 4.63 MiB, generated: 9.78 MiB, post-processed: Unknown size, total: 14.41 MiB) to /home/kuba/.cache/huggingface/datasets/conll2003/conll2003/1.0.0/40e7cb6bcc374f7c349c83acd1e9352a4f09474eb691f64f364ee62eb65d0ca6...\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "43fcbf2d69bb4d0da844c010cb7c3ccd", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=649539.0, style=ProgressStyle(descripti…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "36292b46c7f04f4a94103541e735d3b2", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=162714.0, style=ProgressStyle(descripti…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "abe2116690164e7ba9fbd8bef8a8e0b0", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=145897.0, style=ProgressStyle(descripti…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=1.0, bar_style='info', layout=Layout(width='20px'), max=1.0), HTML(value=''…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=1.0, bar_style='info', layout=Layout(width='20px'), max=1.0), HTML(value=''…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=1.0, bar_style='info', layout=Layout(width='20px'), max=1.0), HTML(value=''…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset conll2003 downloaded and prepared to /home/kuba/.cache/huggingface/datasets/conll2003/conll2003/1.0.0/40e7cb6bcc374f7c349c83acd1e9352a4f09474eb691f64f364ee62eb65d0ca6. Subsequent calls will reuse this data.\n" - ] - } - ], + "execution_count": 95, + "metadata": {}, + "outputs": [], "source": [ - "dataset = load_dataset(\"conll2003\")" + "optimizer = torch.optim.Adam(ner_model.parameters())" ] }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 96, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Dataset({\n", - " features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\n", - " num_rows: 14041\n", - "})" + "14041" ] }, - "execution_count": 80, + "execution_count": 96, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dataset['train']" + "len(train_labels)" ] }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY_predictions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/media/kuba/ssdsam/anaconda3/lib/python3.8/site-packages/torch/optim/optimizer.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 87\u001b[0m \u001b[0mprofile_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Optimizer.step#{}.step\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 88\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprofiler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecord_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprofile_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 89\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 90\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 91\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/media/kuba/ssdsam/anaconda3/lib/python3.8/site-packages/torch/autograd/grad_mode.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 28\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/media/kuba/ssdsam/anaconda3/lib/python3.8/site-packages/torch/optim/adam.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0mbeta1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbeta2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'betas'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m F.adam(params_with_grad,\n\u001b[0m\u001b[1;32m 109\u001b[0m \u001b[0mgrads\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 110\u001b[0m \u001b[0mexp_avgs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/media/kuba/ssdsam/anaconda3/lib/python3.8/site-packages/torch/optim/_functional.py\u001b[0m in \u001b[0;36madam\u001b[0;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, amsgrad, beta1, beta2, lr, weight_decay, eps)\u001b[0m\n\u001b[1;32m 82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 83\u001b[0m \u001b[0;31m# Decay the first and second moment running average coefficient\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 84\u001b[0;31m \u001b[0mexp_avg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmul_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbeta1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mbeta1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 85\u001b[0m \u001b[0mexp_avg_sq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmul_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbeta2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddcmul_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mbeta2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 86\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mamsgrad\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "for epoch in range(1):\n", + " loss_score = 0\n", + " acc_score = 0\n", + " prec_score = 0\n", + " selected_items = 0\n", + " recall_score = 0\n", + " relevant_items = 0\n", + " items_total = 0\n", + " nn_model.train()\n", + " for i in range(len(train_labels)):\n", + " #for i in range(100):\n", + " for j in range(1, len(train_labels[i]) - 1):\n", + " \n", + " X = train_tokens_ids[i][j-1: j+2]\n", + " Y = train_labels[i][j: j+1]\n", + "\n", + " Y_predictions = ner_model(X)\n", + " \n", + " \n", + " acc_score += int(torch.argmax(Y_predictions) == Y)\n", + " \n", + " if torch.argmax(Y_predictions) != 0:\n", + " selected_items +=1\n", + " if torch.argmax(Y_predictions) != 0 and torch.argmax(Y_predictions) == Y.item():\n", + " prec_score += 1\n", + " \n", + " if Y.item() != 0:\n", + " relevant_items +=1\n", + " if Y.item() != 0 and torch.argmax(Y_predictions) == Y.item():\n", + " recall_score += 1\n", + " \n", + " items_total += 1\n", + "\n", + " \n", + " optimizer.zero_grad()\n", + " loss = criterion(Y_predictions.unsqueeze(0), Y)\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + "\n", + " loss_score += loss.item() \n", + " \n", + " precision = prec_score / selected_items\n", + " recall = recall_score / relevant_items\n", + " f1_score = (2*precision * recall) / (precision + recall)\n", + " display('epoch: ', epoch)\n", + " display('loss: ', loss_score / items_total)\n", + " display('acc: ', acc_score / items_total)\n", + " display('prec: ', precision)\n", + " display('recall: : ', recall)\n", + " display('f1: ', f1_score)" + ] + }, + { + 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[ + "0.8530239099859352" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'prec: '" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.5646687697160884" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'recall: : '" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.5327380952380952" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'f1: '" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "0.5482388973966309" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "dataset['train']['pos_tags']" + "loss_score = 0\n", + "acc_score = 0\n", + "prec_score = 0\n", + "selected_items = 0\n", + "recall_score = 0\n", + "relevant_items = 0\n", + "items_total = 0\n", + "nn_model.eval()\n", + "for i in range(100):\n", + "#for i in range(len(test_labels)):\n", + " for j in range(1, len(test_labels[i]) - 1):\n", + "\n", + " X = test_tokens_ids[i][j-1: j+2]\n", + " Y = test_labels[i][j: j+1]\n", + "\n", + " Y_predictions = ner_model(X)\n", + "\n", + "\n", + " acc_score += int(torch.argmax(Y_predictions) == Y)\n", + "\n", + " if torch.argmax(Y_predictions) != 0:\n", + " selected_items +=1\n", + " if torch.argmax(Y_predictions) != 0 and torch.argmax(Y_predictions) == Y.item():\n", + " prec_score += 1\n", + "\n", + " if Y.item() != 0:\n", + " relevant_items +=1\n", + " if Y.item() != 0 and torch.argmax(Y_predictions) == Y.item():\n", + " recall_score += 1\n", + "\n", + " items_total += 1\n", + "\n", + "\n", + " loss = criterion(Y_predictions.unsqueeze(0), Y)\n", + "\n", + "\n", + "\n", + " loss_score += loss.item() \n", + "\n", + "precision = prec_score / selected_items\n", + "recall = recall_score / relevant_items\n", + "f1_score = (2*precision * recall) / (precision + recall)\n", + "display('epoch: ', epoch)\n", + "display('loss: ', loss_score / items_total)\n", + "display('acc: ', acc_score / items_total)\n", + "display('prec: ', precision)\n", + "display('recall: : ', recall)\n", + "display('f1: ', f1_score)" ] }, {