From 3833bbc547a6461c586c11f1714c95332c745e13 Mon Sep 17 00:00:00 2001 From: Anna Nowak Date: Sun, 16 May 2021 12:04:03 +0200 Subject: [PATCH] poprawki --- 07-parsing-semantyczny-uczenie.ipynb | 682 +++++++++++---------------- Janet_test.conllu | 93 ++-- 2 files changed, 319 insertions(+), 456 deletions(-) diff --git a/07-parsing-semantyczny-uczenie.ipynb b/07-parsing-semantyczny-uczenie.ipynb index 6879296..05e001f 100644 --- a/07-parsing-semantyczny-uczenie.ipynb +++ b/07-parsing-semantyczny-uczenie.ipynb @@ -66,13 +66,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "C:\\Users\\domstr2\\l07\n" + "C:\\Users\\Ania\\Desktop\\System_Dialogowy_Janet\\l07\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ + "A subdirectory or file -p already exists.\n", + "Error occurred while processing: -p.\n", + "A subdirectory or file l07 already exists.\n", + "Error occurred while processing: l07.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C:\\Users\\Ania\\Desktop\\System_Dialogowy_Janet\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "** Resuming transfer from byte position 8923190\n", " % Total % Received % Xferd Average Speed Time Time Time Current\n", " Dload Upload Total Spent Left Speed\n", "\n", @@ -80,23 +98,7 @@ " 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0\n", "\n", " 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0\n", - " 1 8714k 1 95352 0 0 66216 0 0:02:14 0:00:01 0:02:13 93666\n", - "100 8714k 100 8714k 0 0 4211k 0 0:00:02 0:00:02 --:--:-- 5290k\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "C:\\Users\\domstr2\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "'unzip' is not recognized as an internal or external command,\n", - "operable program or batch file.\n" + "100 49 100 49 0 0 56 0 --:--:-- --:--:-- --:--:-- 742\n" ] } ], @@ -116,14 +118,14 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: conllu in c:\\users\\domstr2\\anaconda3\\lib\\site-packages (4.4)\n" + "Requirement already satisfied: conllu in c:\\programdata\\anaconda3\\lib\\site-packages (4.4)\n" ] } ], @@ -136,9 +138,9 @@ "def nolabel2o(line, i):\n", " return 'O' if line[i] == 'NoLabel' else line[i]\n", "\n", - "with open('l07/Janet_test.conllu', encoding='utf-8') as trainfile:\n", + "with open('Janet_test.conllu', encoding='utf-8') as trainfile:\n", " trainset = list(parse_incr(trainfile, fields=fields, field_parsers={'slot': nolabel2o}))\n", - "with open('l07/Janet_test.conllu', encoding='utf-8') as testfile:\n", + "with open('Janet_test.conllu', encoding='utf-8') as testfile:\n", " testset = list(parse_incr(testfile, fields=fields, field_parsers={'slot': nolabel2o}))" ] }, @@ -151,14 +153,14 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: tabulate in c:\\users\\domstr2\\anaconda3\\lib\\site-packages (0.8.9)" + "Requirement already satisfied: tabulate in c:\\programdata\\anaconda3\\lib\\site-packages (0.8.9)\n" ] }, { @@ -174,16 +176,9 @@ "'\\n\\n\\n\\n
1hejgreetingO
'" ] }, - "execution_count": 31, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] } ], "source": [ @@ -192,76 +187,6 @@ "tabulate(trainset[0], tablefmt='html')" ] }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
1chcialbymprescription/collectO
2odebrac prescription/collectO
3receptę prescription/collectO
" - ], - "text/plain": [ - "'\\n\\n\\n\\n\\n\\n
1chcialbymprescription/collectO
2odebrac prescription/collectO
3receptę prescription/collectO
'" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tabulate(trainset[10], tablefmt='html')" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
1dzień appoinment/create_appointmentO
2dobry, appoinment/create_appointmentO
3chciałbym appoinment/create_appointmentO
4umówić appoinment/create_appointmentO
5się appoinment/create_appointmentO
6na appoinment/create_appointmentO
7wizytę appoinment/create_appointmentO
8do appoinment/create_appointmentO
9lekarza appoinment/create_appointmentB-appoinment/doctor
10rodzinnego. appoinment/create_appointmentI-appoinment/doctor
11najlepiej appoinment/create_appointmentO
12dzisiaj appoinment/create_appointmentB-datetime
13w appoinment/create_appointmentI-datetime
14godzinach appoinment/create_appointmentI-datetime
15popołudniowych.appoinment/create_appointmentI-datetime
" - ], - "text/plain": [ - "'\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n
1dzień appoinment/create_appointmentO
2dobry, appoinment/create_appointmentO
3chciałbym appoinment/create_appointmentO
4umówić appoinment/create_appointmentO
5się appoinment/create_appointmentO
6na appoinment/create_appointmentO
7wizytę appoinment/create_appointmentO
8do appoinment/create_appointmentO
9lekarza appoinment/create_appointmentB-appoinment/doctor
10rodzinnego. appoinment/create_appointmentI-appoinment/doctor
11najlepiej appoinment/create_appointmentO
12dzisiaj appoinment/create_appointmentB-datetime
13w appoinment/create_appointmentI-datetime
14godzinach appoinment/create_appointmentI-datetime
15popołudniowych.appoinment/create_appointmentI-datetime
'" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tabulate(trainset[1], tablefmt='html')" - ] - }, { "cell_type": "markdown", "metadata": { @@ -271,33 +196,6 @@ "Na potrzeby prezentacji procesu uczenia w jupyterowym notatniku zawęzimy zbiór danych do początkowych przykładów." ] }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "trainset = trainset[:100]\n", - "testset = testset[:100]" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ąę\n" - ] - } - ], - "source": [ - "print('ąę')" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -308,100 +206,91 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: flair in c:\\users\\domstr2\\anaconda3\\lib\\site-packages (0.8.0.post1)\n", - 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This is because pip will change the way that it resolves dependency conflicts.\n", - "\n", - "We recommend you use --use-feature=2020-resolver to test your packages with the new resolver before it becomes the default.\n", - "\n", - "conda 4.10.1 requires ruamel_yaml_conda>=0.11.14, which is not installed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: torch in c:\\users\\domstr2\\anaconda3\\lib\\site-packages (1.7.1)\n", - "Requirement already satisfied: typing-extensions in c:\\users\\domstr2\\anaconda3\\lib\\site-packages (from torch) (3.7.4.3)\n", - "Requirement already satisfied: numpy in c:\\users\\domstr2\\anaconda3\\lib\\site-packages (from torch) (1.19.2)\n" + "Requirement already satisfied: flair in c:\\programdata\\anaconda3\\lib\\site-packages (0.8.0.post1)\n", + "Requirement already satisfied: huggingface-hub in c:\\programdata\\anaconda3\\lib\\site-packages (from flair) (0.0.8)\n", + "Requirement already 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flair.datasets import SentenceDataset\n", "from flair.embeddings import StackedEmbeddings\n", @@ -435,15 +324,15 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Corpus: 37 train + 4 dev + 41 test sentences\n", - "Dictionary with 13 tags: , O, B-appoinment/doctor, I-appoinment/doctor, B-datetime, I-datetime, B-login/id, B-login/password, B-appointment/type, I-appointment/type, B-prescription/type, , \n" + "Corpus: 36 train + 4 dev + 40 test sentences\n", + "Dictionary with 13 tags: , O, B-appoinment/doctor, I-appoinment/doctor, B-datetime, I-datetime, B-login/id, B-appointment/type, I-appointment/type, B-prescription/type, B-login/password, , \n" ] } ], @@ -481,132 +370,9 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 7, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2021-05-12 17:01:27,807 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copying C:\\Users\\domstr2\\AppData\\Local\\Temp\\tmp30skh32n to cache at C:\\Users\\domstr2\\.flair\\embeddings\\lm-polish-backward-v0.2.pt\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2021-05-12 17:02:46,828 removing temp file C:\\Users\\domstr2\\AppData\\Local\\Temp\\tmp30skh32n\n" - ] - } - ], + "outputs": [], "source": [ "embedding_types = [\n", " WordEmbeddings('pl'),\n", @@ -631,7 +397,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -687,15 +453,15 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2021-05-12 17:07:41,538 ----------------------------------------------------------------------------------------------------\n", - "2021-05-12 17:07:41,539 Model: \"SequenceTagger(\n", + "2021-05-16 11:40:14,273 ----------------------------------------------------------------------------------------------------\n", + "2021-05-16 11:40:14,274 Model: \"SequenceTagger(\n", " (embeddings): StackedEmbeddings(\n", " (list_embedding_0): WordEmbeddings('pl')\n", " (list_embedding_1): FlairEmbeddings(\n", @@ -728,53 +494,154 @@ " (weights): None\n", " (weight_tensor) None\n", ")\"\n", - "2021-05-12 17:07:41,540 ----------------------------------------------------------------------------------------------------\n", - "2021-05-12 17:07:41,541 Corpus: \"Corpus: 37 train + 4 dev + 41 test sentences\"\n", - "2021-05-12 17:07:41,541 ----------------------------------------------------------------------------------------------------\n", - "2021-05-12 17:07:41,542 Parameters:\n", - "2021-05-12 17:07:41,542 - learning_rate: \"0.1\"\n", - "2021-05-12 17:07:41,543 - mini_batch_size: \"32\"\n", - "2021-05-12 17:07:41,543 - patience: \"3\"\n", - "2021-05-12 17:07:41,544 - anneal_factor: \"0.5\"\n", - "2021-05-12 17:07:41,544 - max_epochs: \"10\"\n", - "2021-05-12 17:07:41,545 - shuffle: \"True\"\n", - "2021-05-12 17:07:41,546 - train_with_dev: \"False\"\n", - "2021-05-12 17:07:41,546 - batch_growth_annealing: \"False\"\n", - "2021-05-12 17:07:41,547 ----------------------------------------------------------------------------------------------------\n", - "2021-05-12 17:07:41,547 Model training base path: \"slot-model\"\n", - "2021-05-12 17:07:41,548 ----------------------------------------------------------------------------------------------------\n", - "2021-05-12 17:07:41,549 Device: cpu\n", - "2021-05-12 17:07:41,549 ----------------------------------------------------------------------------------------------------\n", - "2021-05-12 17:07:41,550 Embeddings storage mode: cpu\n", - "2021-05-12 17:07:41,552 ----------------------------------------------------------------------------------------------------\n", - "2021-05-12 17:07:46,139 epoch 1 - iter 1/2 - loss 9.51263237 - samples/sec: 6.98 - lr: 0.100000\n", - "2021-05-12 17:07:47,186 epoch 1 - iter 2/2 - loss 7.22621894 - samples/sec: 30.58 - lr: 0.100000\n", - "2021-05-12 17:07:47,188 ----------------------------------------------------------------------------------------------------\n", - "2021-05-12 17:07:47,189 EPOCH 1 done: loss 7.2262 - lr 0.1000000\n", - "2021-05-12 17:07:48,466 DEV : loss 5.046579837799072 - score 0.0\n", - "2021-05-12 17:07:48,468 BAD EPOCHS (no improvement): 0\n", - "saving best model\n" + "2021-05-16 11:40:14,275 ----------------------------------------------------------------------------------------------------\n", + "2021-05-16 11:40:14,277 Corpus: \"Corpus: 36 train + 4 dev + 40 test sentences\"\n", + "2021-05-16 11:40:14,277 ----------------------------------------------------------------------------------------------------\n", + "2021-05-16 11:40:14,278 Parameters:\n", + "2021-05-16 11:40:14,279 - learning_rate: \"0.1\"\n", + "2021-05-16 11:40:14,280 - mini_batch_size: \"32\"\n", + "2021-05-16 11:40:14,280 - patience: \"3\"\n", + "2021-05-16 11:40:14,281 - anneal_factor: \"0.5\"\n", + "2021-05-16 11:40:14,282 - max_epochs: \"10\"\n", + "2021-05-16 11:40:14,283 - shuffle: \"True\"\n", + "2021-05-16 11:40:14,285 - train_with_dev: \"False\"\n", + "2021-05-16 11:40:14,286 - batch_growth_annealing: \"False\"\n", + "2021-05-16 11:40:14,287 ----------------------------------------------------------------------------------------------------\n", + "2021-05-16 11:40:14,288 Model training base path: \"slot-model\"\n", + "2021-05-16 11:40:14,288 ----------------------------------------------------------------------------------------------------\n", + "2021-05-16 11:40:14,289 Device: cpu\n", + "2021-05-16 11:40:14,290 ----------------------------------------------------------------------------------------------------\n", + "2021-05-16 11:40:14,292 Embeddings storage mode: cpu\n", + "2021-05-16 11:40:14,295 ----------------------------------------------------------------------------------------------------\n", + "2021-05-16 11:40:18,737 epoch 1 - iter 1/2 - loss 13.17695141 - samples/sec: 7.21 - lr: 0.100000\n", + "2021-05-16 11:40:19,989 epoch 1 - iter 2/2 - loss 11.51309586 - samples/sec: 25.57 - lr: 0.100000\n", + "2021-05-16 11:40:19,989 ----------------------------------------------------------------------------------------------------\n", + "2021-05-16 11:40:19,989 EPOCH 1 done: loss 11.5131 - lr 0.1000000\n", + "2021-05-16 11:40:20,670 DEV : loss 5.320306777954102 - score 0.0\n", + "2021-05-16 11:40:20,671 BAD EPOCHS (no improvement): 0\n", + "saving best model\n", + "2021-05-16 11:40:30,073 ----------------------------------------------------------------------------------------------------\n", + "2021-05-16 11:40:30,802 epoch 2 - iter 1/2 - loss 8.20096970 - samples/sec: 45.04 - lr: 0.100000\n", + "2021-05-16 11:40:31,005 epoch 2 - iter 2/2 - loss 5.87843704 - samples/sec: 157.40 - lr: 0.100000\n", + "2021-05-16 11:40:31,006 ----------------------------------------------------------------------------------------------------\n", + "2021-05-16 11:40:31,008 EPOCH 2 done: loss 5.8784 - lr 0.1000000\n", + "2021-05-16 11:40:31,020 DEV : loss 2.201185703277588 - score 0.0\n", + "2021-05-16 11:40:31,038 BAD EPOCHS (no improvement): 0\n", + "saving best model\n", + "2021-05-16 11:40:40,878 ----------------------------------------------------------------------------------------------------\n", + "2021-05-16 11:40:41,800 epoch 3 - iter 1/2 - loss 3.59802794 - samples/sec: 34.83 - lr: 0.100000\n", + "2021-05-16 11:40:42,230 epoch 3 - iter 2/2 - loss 7.24588382 - samples/sec: 74.64 - lr: 0.100000\n", + "2021-05-16 11:40:42,231 ----------------------------------------------------------------------------------------------------\n", + "2021-05-16 11:40:42,233 EPOCH 3 done: loss 7.2459 - lr 0.1000000\n", + "2021-05-16 11:40:42,290 DEV : loss 2.3815672397613525 - score 0.0\n", + "2021-05-16 11:40:42,295 BAD EPOCHS (no improvement): 1\n", + "2021-05-16 11:40:42,300 ----------------------------------------------------------------------------------------------------\n", + "2021-05-16 11:40:43,662 epoch 4 - iter 1/2 - loss 4.05115032 - samples/sec: 23.57 - lr: 0.100000\n", + "2021-05-16 11:40:44,013 epoch 4 - iter 2/2 - loss 3.16846037 - samples/sec: 91.53 - lr: 0.100000\n", + "2021-05-16 11:40:44,015 ----------------------------------------------------------------------------------------------------\n", + "2021-05-16 11:40:44,018 EPOCH 4 done: loss 3.1685 - lr 0.1000000\n", + "2021-05-16 11:40:44,072 DEV : loss 1.7660648822784424 - score 0.0\n", + "2021-05-16 11:40:44,075 BAD EPOCHS (no improvement): 0\n", + "saving best model\n", + "2021-05-16 11:40:53,620 ----------------------------------------------------------------------------------------------------\n", + "2021-05-16 11:40:54,419 epoch 5 - iter 1/2 - loss 3.52825356 - samples/sec: 40.10 - lr: 0.100000\n", + "2021-05-16 11:40:54,594 epoch 5 - iter 2/2 - loss 3.12245941 - samples/sec: 183.91 - lr: 0.100000\n", + "2021-05-16 11:40:54,595 ----------------------------------------------------------------------------------------------------\n", + "2021-05-16 11:40:54,596 EPOCH 5 done: loss 3.1225 - lr 0.1000000\n", + "2021-05-16 11:40:54,624 DEV : loss 1.8835055828094482 - score 0.0\n", + "2021-05-16 11:40:54,626 BAD EPOCHS (no improvement): 1\n", + "2021-05-16 11:40:54,627 ----------------------------------------------------------------------------------------------------\n", + "2021-05-16 11:40:55,393 epoch 6 - iter 1/2 - loss 2.84318709 - samples/sec: 41.88 - lr: 0.100000\n", + "2021-05-16 11:40:55,648 epoch 6 - iter 2/2 - loss 4.79819477 - samples/sec: 125.98 - lr: 0.100000\n", + "2021-05-16 11:40:55,649 ----------------------------------------------------------------------------------------------------\n", + "2021-05-16 11:40:55,650 EPOCH 6 done: loss 4.7982 - lr 0.1000000\n", + "2021-05-16 11:40:55,675 DEV : loss 1.9106686115264893 - score 0.0\n", + "2021-05-16 11:40:55,677 BAD EPOCHS (no improvement): 2\n", + "2021-05-16 11:40:55,678 ----------------------------------------------------------------------------------------------------\n", + "2021-05-16 11:40:56,467 epoch 7 - iter 1/2 - loss 3.35292196 - samples/sec: 40.66 - lr: 0.100000\n", + "2021-05-16 11:40:56,661 epoch 7 - iter 2/2 - loss 1.90253919 - samples/sec: 165.80 - lr: 0.100000\n", + "2021-05-16 11:40:56,662 ----------------------------------------------------------------------------------------------------\n", + "2021-05-16 11:40:56,663 EPOCH 7 done: loss 1.9025 - lr 0.1000000\n", + "2021-05-16 11:40:56,689 DEV : loss 1.5785303115844727 - score 0.0\n", + "2021-05-16 11:40:56,691 BAD EPOCHS (no improvement): 0\n", + "saving best model\n", + "2021-05-16 11:41:09,226 ----------------------------------------------------------------------------------------------------\n", + "2021-05-16 11:41:10,375 epoch 8 - iter 1/2 - loss 3.24992299 - samples/sec: 27.87 - lr: 0.100000\n", + "2021-05-16 11:41:10,744 epoch 8 - iter 2/2 - loss 3.30123496 - samples/sec: 87.17 - lr: 0.100000\n", + "2021-05-16 11:41:10,745 ----------------------------------------------------------------------------------------------------\n", + "2021-05-16 11:41:10,746 EPOCH 8 done: loss 3.3012 - lr 0.1000000\n", + "2021-05-16 11:41:10,798 DEV : loss 1.590420126914978 - score 0.0\n", + "2021-05-16 11:41:10,802 BAD EPOCHS (no improvement): 1\n", + "2021-05-16 11:41:10,807 ----------------------------------------------------------------------------------------------------\n", + "2021-05-16 11:41:12,175 epoch 9 - iter 1/2 - loss 2.74546242 - samples/sec: 23.41 - lr: 0.100000\n", + "2021-05-16 11:41:12,515 epoch 9 - iter 2/2 - loss 2.34704965 - samples/sec: 94.40 - lr: 0.100000\n", + "2021-05-16 11:41:12,518 ----------------------------------------------------------------------------------------------------\n", + "2021-05-16 11:41:12,520 EPOCH 9 done: loss 2.3470 - lr 0.1000000\n", + "2021-05-16 11:41:12,573 DEV : loss 1.6068150997161865 - score 0.0\n" ] }, { - "ename": "RuntimeError", - "evalue": "[enforce fail at ..\\caffe2\\serialize\\inline_container.cc:274] . unexpected pos 64 vs 0", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mOSError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torch\\serialization.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(obj, f, pickle_module, pickle_protocol, _use_new_zipfile_serialization)\u001b[0m\n\u001b[0;32m 371\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0m_open_zipfile_writer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mopened_file\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mopened_zipfile\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 372\u001b[1;33m \u001b[0m_save\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mopened_zipfile\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpickle_protocol\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 373\u001b[0m \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\torch\\serialization.py\u001b[0m in \u001b[0;36m_save\u001b[1;34m(obj, zip_file, pickle_module, pickle_protocol)\u001b[0m\n\u001b[0;32m 477\u001b[0m \u001b[0mdata_value\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata_buf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 478\u001b[1;33m \u001b[0mzip_file\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite_record\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'data.pkl'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata_value\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_value\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 479\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mOSError\u001b[0m: [Errno 28] No space left on device", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[1;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mtrainer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mModelTrainer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtagger\u001b[0m\u001b[1;33m,\u001b[0m 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train_with_dev, train_with_test, monitor_train, monitor_test, embeddings_storage_mode, checkpoint, save_final_model, anneal_with_restarts, anneal_with_prestarts, batch_growth_annealing, shuffle, param_selection_mode, write_weights, num_workers, sampler, use_amp, amp_opt_level, eval_on_train_fraction, eval_on_train_shuffle, save_model_at_each_epoch, **kwargs)\u001b[0m\n\u001b[0;32m 592\u001b[0m ):\n\u001b[0;32m 593\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"saving best model\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 594\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbase_path\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;34m\"best-model.pt\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 595\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 596\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0manneal_with_prestarts\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\flair\\nn.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, model_file)\u001b[0m\n\u001b[0;32m 70\u001b[0m \u001b[0mmodel_state\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_state_dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 71\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel_state\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel_file\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m 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"output_type": "stream", + "text": [ + "2021-05-16 11:41:12,575 BAD EPOCHS (no improvement): 2\n", + "2021-05-16 11:41:12,577 ----------------------------------------------------------------------------------------------------\n", + "2021-05-16 11:41:13,690 epoch 10 - iter 1/2 - loss 2.63941884 - samples/sec: 28.79 - lr: 0.100000\n", + "2021-05-16 11:41:13,878 epoch 10 - iter 2/2 - loss 2.18226165 - samples/sec: 171.12 - lr: 0.100000\n", + "2021-05-16 11:41:13,879 ----------------------------------------------------------------------------------------------------\n", + "2021-05-16 11:41:13,880 EPOCH 10 done: loss 2.1823 - lr 0.1000000\n", + "2021-05-16 11:41:13,906 DEV : loss 1.458857536315918 - score 0.0\n", + "2021-05-16 11:41:13,907 BAD EPOCHS (no improvement): 0\n", + "saving best model\n", + "2021-05-16 11:41:33,558 ----------------------------------------------------------------------------------------------------\n", + "2021-05-16 11:41:33,559 Testing using best model ...\n", + "2021-05-16 11:41:33,560 loading file slot-model\\best-model.pt\n", + "2021-05-16 11:41:45,502 0.1765\t0.1667\t0.1714\n", + "2021-05-16 11:41:45,503 \n", + "Results:\n", + "- F1-score (micro) 0.1714\n", + "- F1-score (macro) 0.1161\n", + "\n", + "By class:\n", + "appoinment/doctor tp: 1 - fp: 9 - fn: 5 - precision: 0.1000 - recall: 0.1667 - f1-score: 0.1250\n", + "appointment/type tp: 0 - fp: 0 - fn: 2 - precision: 0.0000 - recall: 0.0000 - f1-score: 0.0000\n", + "datetime tp: 0 - fp: 1 - fn: 3 - precision: 0.0000 - recall: 0.0000 - f1-score: 0.0000\n", + "login/id tp: 2 - fp: 2 - fn: 1 - precision: 0.5000 - recall: 0.6667 - f1-score: 0.5714\n", + "login/password tp: 0 - fp: 0 - fn: 3 - precision: 0.0000 - recall: 0.0000 - f1-score: 0.0000\n", + "prescription/type tp: 0 - fp: 2 - fn: 1 - precision: 0.0000 - recall: 0.0000 - f1-score: 0.0000\n", + "2021-05-16 11:41:45,503 ----------------------------------------------------------------------------------------------------\n" ] + }, + { + "data": { + "text/plain": [ + "{'test_score': 0.17142857142857143,\n", + " 'dev_score_history': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n", + " 'train_loss_history': [11.51309585571289,\n", + " 5.878437042236328,\n", + " 7.245883822441101,\n", + " 3.1684603691101074,\n", + " 3.1224594116210938,\n", + " 4.798194766044617,\n", + " 1.9025391936302185,\n", + " 3.3012349605560303,\n", + " 2.347049653530121,\n", + " 2.182261645793915],\n", + " 'dev_loss_history': [5.320306777954102,\n", + " 2.201185703277588,\n", + " 2.3815672397613525,\n", + " 1.7660648822784424,\n", + " 1.8835055828094482,\n", + " 1.9106686115264893,\n", + " 1.5785303115844727,\n", + " 1.590420126914978,\n", + " 1.6068150997161865,\n", + " 1.458857536315918]}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -829,14 +696,14 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2021-05-12 16:58:59,033 loading file slot-model/final-model.pt\n" + "2021-05-16 11:41:45,529 loading file slot-model/final-model.pt\n" ] } ], @@ -854,7 +721,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -875,7 +742,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -883,29 +750,29 @@ "text/html": [ "\n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "\n", "
doktor O
lekarz O
wizyta O
kolano O
na O
godzineO
jutro O
dzisiajO
13:00 O
doktor I-appoinment/doctor
lekarza B-appoinment/doctor
rodzinnego O
najlepiej O
dzisiaj O
w O
godzinach O
popołudniowychO
dziś O
" ], "text/plain": [ - "'\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n
doktor O
lekarz O
wizyta O
kolano O
na O
godzineO
jutro O
dzisiajO
13:00 O
'" + "'\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n
doktor I-appoinment/doctor
lekarza B-appoinment/doctor
rodzinnego O
najlepiej O
dzisiaj O
w O
godzinach O
popołudniowychO
dziś O
'" ] }, - "execution_count": 23, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "tabulate(predict(model, 'doktor lekarz wizyta kolano na godzine jutro dzisiaj 13:00'.split()), tablefmt='html')" + "tabulate(predict(model, 'doktor lekarza rodzinnego najlepiej dzisiaj w godzinach popołudniowych dziś '.split()), tablefmt='html')" ] }, { @@ -920,6 +787,13 @@ " 4. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin, Attention is All you Need, NIPS 2017, pp. 5998-6008, https://arxiv.org/abs/1706.03762\n", " 5. Alan Akbik, Duncan Blythe, Roland Vollgraf, Contextual String Embeddings for Sequence Labeling, Proceedings of the 27th International Conference on Computational Linguistics, pp. 1638–1649, https://www.aclweb.org/anthology/C18-1139.pdf\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/Janet_test.conllu b/Janet_test.conllu index fab69bf..77598d6 100644 --- a/Janet_test.conllu +++ b/Janet_test.conllu @@ -3,11 +3,11 @@ # slots: 1 hej greeting NoLabel -# text: Dzień dobry, chciałbym umówić się na wizytę do lekarza rodzinnego. Najlepiej dzisiaj w godzinach popołudniowych. +# text: Dzień dobry chciałbym umówić się na wizytę do lekarza rodzinnego Najlepiej dzisiaj w godzinach popołudniowych # intent: appoinment/create_appointment # slots: 1 dzień appoinment/create_appointment NoLabel -2 dobry, appoinment/create_appointment NoLabel +2 dobry appoinment/create_appointment NoLabel 3 chciałbym appoinment/create_appointment NoLabel 4 umówić appoinment/create_appointment NoLabel 5 się appoinment/create_appointment NoLabel @@ -15,12 +15,12 @@ 7 wizytę appoinment/create_appointment NoLabel 8 do appoinment/create_appointment NoLabel 9 lekarza appoinment/create_appointment B-appoinment/doctor -10 rodzinnego. appoinment/create_appointment I-appoinment/doctor +10 rodzinnego appoinment/create_appointment I-appoinment/doctor 11 najlepiej appoinment/create_appointment NoLabel 12 dzisiaj appoinment/create_appointment B-datetime 13 w appoinment/create_appointment I-datetime 14 godzinach appoinment/create_appointment I-datetime -15 popołudniowych. appoinment/create_appointment I-datetime +15 popołudniowych appoinment/create_appointment I-datetime # text: 12345678AFD # intent: login/enter_id @@ -52,16 +52,16 @@ 1 ten appoinment/confirm NoLabel 2 termin appoinment/confirm NoLabel 3 mi appoinment/confirm NoLabel -4 odpowiada! appoinment/confirm NoLabel +4 odpowiada appoinment/confirm NoLabel -# text: Tak, bardzo dziękuję. +# text: Tak bardzo dziękuję # intent: affirm # slots: -1 tak, affirm NoLabel +1 tak affirm NoLabel 2 bardzo affirm NoLabel -3 dziękuję. affirm NoLabel +3 dziękuję affirm NoLabel -# text: Chciałbym też od razu zrobić badania morfologii krwi. Kiedy mogę przyjść na pobranie krwi? +# text: Chciałbym też od razu zrobić badania morfologii krwi Kiedy mogę przyjść na pobranie krwi? # intent: appoinment/create_appointment request_information/opening_hours # slots: 1 chciałbym appoinment/create_appointment NoLabel @@ -71,26 +71,26 @@ 5 zrobić appoinment/create_appointment NoLabel 6 badania appoinment/create_appointment B-appointment/type 7 morfologii appoinment/create_appointment I-appointment/type -8 krwi. appoinment/create_appointment I-appointment/type +8 krwi appoinment/create_appointment I-appointment/type 9 kiedy request_information/opening_hours NoLabel 10 mogę request_information/opening_hours NoLabel 11 przyjść request_information/opening_hours NoLabel 12 na request_information/opening_hours NoLabel 13 pobranie request_information/opening_hours B-appointment/type -14 krwi? request_information/opening_hours I-appointment/type +14 krwi request_information/opening_hours I-appointment/type -# text: Dziękuję bardzo za informację. W takim przypadku to wszystko. +# text: Dziękuję bardzo za informację W takim przypadku to wszystko # intent: end_conversation # slots: 1 dziękuję end_conversation NoLabel 2 bardzo end_conversation NoLabel 3 za end_conversation NoLabel -4 informację. end_conversation NoLabel +4 informację end_conversation NoLabel 5 w end_conversation NoLabel 6 takim end_conversation NoLabel 7 przypadku end_conversation NoLabel 8 to end_conversation NoLabel -9 wszystko. end_conversation NoLabel +9 wszystko end_conversation NoLabel # text: Dzień dobry # intent: greeting @@ -141,10 +141,10 @@ # slots: 1 tak affirm NoLabel -# text: 12.04.2021 +# text: 12042021 # intent: appoinment/set_date # slots: -1 12.04.2021 appoinment/set_date B-datetime +1 12042021 appoinment/set_date B-datetime # text: 13:00 # intent: appoinment/set_time @@ -176,17 +176,17 @@ # slots: 1 cześć greeting NoLabel -# text: Chciałbym się dowiedzieć, czy mam umówione jakieś wizyty. +# text: Chciałbym się dowiedzieć czy mam umówione jakieś wizyty # intent: appoinment/check_appointments # slots: 1 chciałbym appoinment/check_appointments NoLabel 2 się appoinment/check_appointments NoLabel -3 dowiedzieć, appoinment/check_appointments NoLabel +3 dowiedzieć appoinment/check_appointments NoLabel 4 czy appoinment/check_appointments NoLabel 5 mam appoinment/check_appointments NoLabel 6 umówione appoinment/check_appointments NoLabel 7 jakieś appoinment/check_appointments NoLabel -8 wizyty. appoinment/check_appointments NoLabel +8 wizyty appoinment/check_appointments NoLabel # text: 34534535 # intent: login/enter_id @@ -223,7 +223,7 @@ 6 państwa request_information/doctors NoLabel 7 przychodni? request_information/doctors NoLabel -# text: Chciałbym umówić wizytę do doktora Kolano. +# text: Chciałbym umówić wizytę do doktora Kolano # intent: appoinment/create_appointment # slots: 1 chciałbym appoinment/create_appointment NoLabel @@ -231,35 +231,34 @@ 3 wizytę appoinment/create_appointment NoLabel 4 do appoinment/create_appointment NoLabel 5 doktora appoinment/create_appointment B-appoinment/doctor -6 kolano. appoinment/create_appointment I-appoinment/doctor +6 kolano appoinment/create_appointment I-appoinment/doctor -# text: Ten termin mi odpowiada. +# text: Ten termin mi odpowiada # intent: appoinment/confirm # slots: 1 ten appoinment/confirm NoLabel 2 termin appoinment/confirm NoLabel 3 mi appoinment/confirm NoLabel -4 odpowiada. appoinment/confirm NoLabel +4 odpowiada appoinment/confirm NoLabel # text: tak # intent: affirm # slots: 1 tak affirm NoLabel -# text: Nie, to wszystko. Do widzenia. +# text: Nie to wszystko Do widzenia # intent: end_conversation # slots: -1 nie, end_conversation NoLabel +1 nie end_conversation NoLabel 2 to end_conversation NoLabel -3 wszystko. end_conversation NoLabel +3 wszystko end_conversation NoLabel 4 do end_conversation NoLabel -5 widzenia. end_conversation NoLabel +5 widzenia end_conversation NoLabel -# text: Cześć :) +# text: Cześć # intent: greeting # slots: 1 cześć greeting NoLabel -2 :) greeting NoLabel # text: Jakie usługi medyczne są dostępne? # intent: request_information/medical_services @@ -268,54 +267,44 @@ 2 usługi request_information/medical_services NoLabel 3 medyczne request_information/medical_services NoLabel 4 są request_information/medical_services NoLabel -5 dostępne? request_information/medical_services NoLabel +5 dostępne request_information/medical_services NoLabel -# text: Chciałbym zapisać się do okulisty. Ile kosztuje wizyta? +# text: Chciałbym zapisać się do okulisty Ile kosztuje wizyta? # intent: appoinment/create_appointment request_information/cost # slots: 1 chciałbym appoinment/create_appointment NoLabel 2 zapisać appoinment/create_appointment NoLabel 3 się appoinment/create_appointment NoLabel 4 do appoinment/create_appointment NoLabel -5 okulisty. appoinment/create_appointment B-appoinment/doctor +5 okulisty appoinment/create_appointment B-appoinment/doctor 6 ile request_information/cost NoLabel 7 kosztuje request_information/cost NoLabel -8 wizyta? request_information/cost NoLabel +8 wizyta request_information/cost NoLabel -# text: Nie? -# intent: deny -# slots: -1 nie? deny NoLabel - -# text: Nie, ten jest idealny. +# text: Nie ten jest idealny # intent: affirm # slots: -1 nie, affirm NoLabel +1 nie affirm NoLabel 2 ten affirm NoLabel 3 jest affirm NoLabel -4 idealny. affirm NoLabel +4 idealny affirm NoLabel -# text: Tak. +# text: Tak # intent: affirm # slots: -1 tak. affirm NoLabel +1 tak affirm NoLabel -# text: Dziękuję za informację : ). +# text: Dziękuję za informację # intent: end_conversation # slots: 1 dziękuję end_conversation NoLabel 2 za end_conversation NoLabel 3 informację end_conversation NoLabel -4 : end_conversation NoLabel -5 ). end_conversation NoLabel -# text: Nie, dziękuję - to wszystko : ). +# text: Nie dziękuję to wszystko # intent: end_conversation # slots: -1 nie, end_conversation NoLabel +1 nie end_conversation NoLabel 2 dziękuję end_conversation NoLabel -3 - end_conversation NoLabel 4 to end_conversation NoLabel -5 wszystko end_conversation NoLabel -6 : end_conversation NoLabel -7 ). end_conversation NoLabel \ No newline at end of file +5 wszystko end_conversation NoLabel \ No newline at end of file