forked from kubapok/en-ner-conll-2003
update main
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24
main.ipynb
24
main.ipynb
@ -28,17 +28,13 @@
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"import torch\n",
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"import pandas as pd\n",
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"\n",
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"from sklearn.model_selection import train_test_split\n",
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"from torchtext.vocab import Vocab\n",
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"from collections import Counter\n",
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"from sklearn.feature_extraction.text import TfidfVectorizer\n",
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"from sklearn.metrics import accuracy_score\n",
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"\n",
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"import lzma\n",
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"import re\n",
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"import itertools"
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"import re"
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],
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"execution_count": 2,
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"execution_count": null,
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"outputs": []
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},
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{
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@ -60,7 +56,7 @@
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" x = self.softmax(x)\n",
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" return x"
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],
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"execution_count": 22,
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"execution_count": null,
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"outputs": []
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},
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{
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@ -82,7 +78,7 @@
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" x = self.fc1(x)\n",
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" return x"
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],
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"execution_count": 23,
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"execution_count": null,
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"outputs": []
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},
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{
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@ -111,7 +107,7 @@
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"for i in range(len(ner_tags_set)):\n",
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" ner_tags_dictionary[ner_tags_set[i]] = i"
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],
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"execution_count": 46,
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"execution_count": null,
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"outputs": []
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},
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{
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@ -141,7 +137,7 @@
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"train_tokens_ids = data_preprocessing(tokens)\n",
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"train_labels = labels_preprocessing(ner_tags)"
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],
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"execution_count": 47,
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"execution_count": null,
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"outputs": []
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},
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{
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@ -215,7 +211,7 @@
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" display('recall: : ', recall)\n",
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" display('f1: ', f1_score)"
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],
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"execution_count": 27,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "display_data",
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@ -539,7 +535,7 @@
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"test_tokens_ids = data_preprocessing(dev_0_data)\n",
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"test_labels = labels_preprocessing(dev_0_tags)\n"
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],
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"execution_count": 41,
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"execution_count": null,
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"outputs": []
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},
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{
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@ -601,7 +597,7 @@
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"display('recall: : ', recall)\n",
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"display('f1: ', f1_score)"
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],
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"execution_count": 42,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "display_data",
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@ -763,7 +759,7 @@
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" Y_predictions = ner_model(X)\n",
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" result[i].append(int(torch.argmax(Y_predictions)))"
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],
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"execution_count": 49,
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"execution_count": null,
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"outputs": []
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}
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]
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