2021-06-09 12:46:15 +02:00
{
2021-09-27 12:34:44 +02:00
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"\n",
"<div class=\"alert alert-block alert-info\">\n",
"<h1> Ekstrakcja informacji </h1>\n",
"<h2> 12. <i>Transformery</i> [\u0107wiczenia]</h2> \n",
"<h3> Jakub Pokrywka (2021)</h3>\n",
"</div>\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# bpe"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"pip install tokenizers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"https://github.com/huggingface/tokenizers/tree/master/bindings/python"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from tokenizers import Tokenizer, models, trainers"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from tokenizers.trainers import BpeTrainer"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"tokenizer = Tokenizer(models.BPE())"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"trainer = trainers.BpeTrainer(vocab_size=20000, min_frequency=2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"https://wolnelektury.pl/media/book/txt/pan-tadeusz.txt"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"tokenizer.train(files = ['/home/kuba/Syncthing/przedmioty/2020-02/ISI/zajecia9_ngramowy_model_jDDezykowy/pan-tadeusz-train.txt'], trainer = trainer)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"output = tokenizer.encode(\"Nie \u015bpiewaj\u0105 piosenek: pracuj\u0105 leniwo,\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[236, 2255, 2069, 3898, 9908, 14, 8675, 8319, 191, 7]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output.ids"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Nie', ' \u015bpie', 'waj\u0105', ' pios', 'enek', ':', ' pracuj\u0105', ' leni', 'wo', ',']"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output.tokens"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"tokenizer.save(\"./my-bpe.tokenizer.json\", pretty=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ZADANIE\n",
"stworzy\u0107 BPE tokenizer na podstawie https://git.wmi.amu.edu.pl/kubapok/lalka-lm/src/branch/master/train/train.tsv\n",
"i stworzy\u0107 stokenizowan\u0105 list\u0119: \n",
"https://git.wmi.amu.edu.pl/kubapok/lalka-lm/src/branch/master/test-A/in.tsv\n",
"\n",
"wybra\u0107 vocab_size = 8k, uwzgl\u0119dni\u0107 dodatkowe tokeny: BOS oraz EOS i wple\u015b\u0107 je do zbioru testowego"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# transformery"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"# pip install transformers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"przyk\u0142ady pochodz\u0105 cz\u0119\u015bciowo z: https://huggingface.co/"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"import torch"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"from transformers import pipeline, set_seed"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"from transformers import RobertaTokenizer, RobertaModel"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"tokenizer = RobertaTokenizer.from_pretrained('roberta-base')\n"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"model = RobertaModel.from_pretrained('roberta-base')"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"text = \"Replace me by any text you'd like. Bla Bla\""
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"encoded_input = tokenizer(text, return_tensors='pt')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[ 0, 9064, 6406, 162, 30, 143, 2788, 47, 1017, 101, 4, 2091,\n",
" 102, 2091, 102, 2]])"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"encoded_input['input_ids']"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[ 0, 9064, 6406, 162, 30, 143, 2788, 47, 1017, 101, 4, 2091,\n",
" 102, 2091, 102, 2]])"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"encoded_input['input_ids']"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"' me'"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tokenizer.decode([162])"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"output = model(**encoded_input)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"BaseModelOutputWithPoolingAndCrossAttentions(last_hidden_state=tensor([[[-4.4858e-02, 8.6642e-02, -7.2129e-03, ..., -4.6295e-02,\n",
" -3.9316e-02, 4.5264e-04],\n",
" [-6.0603e-02, 1.5684e-01, 4.3705e-02, ..., 5.3485e-01,\n",
" 8.4371e-02, 1.4826e-01],\n",
" [-2.3786e-02, -1.2086e-02, 7.8233e-02, ..., -4.9132e-01,\n",
" 1.2500e-01, 3.3293e-01],\n",
" ...,\n",
" [ 6.7192e-02, 2.4028e-01, -2.9984e-01, ..., 2.1992e-01,\n",
" 1.9186e-02, 1.5355e-01],\n",
" [ 1.7611e-01, 1.4001e-01, -1.3774e-01, ..., -5.0379e-01,\n",
" 7.3958e-02, 3.7870e-02],\n",
" [-3.4405e-02, 8.7648e-02, -3.9429e-02, ..., -9.1916e-02,\n",
" -3.5529e-02, -2.6777e-02]]], grad_fn=<NativeLayerNormBackward>), pooler_output=tensor([[ 5.1994e-03, -2.1290e-01, -2.2585e-01, -9.3315e-02, 1.1761e-01,\n",
" 1.9024e-01, 2.4873e-01, -8.1097e-02, -4.4050e-02, -1.6596e-01,\n",
" 2.1572e-01, -8.3736e-03, -7.7416e-02, 8.2714e-02, -1.2487e-01,\n",
" 4.8405e-01, 2.1145e-01, -4.4653e-01, 4.1008e-02, -1.2578e-02,\n",
" -2.5560e-01, 7.3874e-02, 4.6924e-01, 3.2284e-01, 1.2382e-01,\n",
" 6.3117e-02, -1.2633e-01, -1.3542e-02, 1.6195e-01, 2.1738e-01,\n",
" 2.8682e-01, 6.2675e-02, 8.5778e-02, 2.3686e-01, -2.5219e-01,\n",
" 4.3382e-02, -3.0992e-01, 4.0551e-02, 2.3078e-01, -1.8763e-01,\n",
" -7.2084e-02, 1.5847e-01, 2.0228e-01, -1.2756e-01, -1.1429e-01,\n",
" 3.9603e-01, 2.6018e-01, 2.9297e-02, -1.2162e-01, -8.2051e-02,\n",
" -3.5665e-01, 3.4722e-01, 2.8292e-01, 1.9929e-01, -1.4734e-02,\n",
" 4.8892e-02, -1.4596e-01, 2.5527e-01, -7.5540e-02, -8.7507e-02,\n",
" -1.2164e-01, -2.0039e-01, -7.1405e-03, -5.9407e-02, 4.1914e-02,\n",
" -1.3208e-01, 6.8612e-02, -1.4154e-01, -1.1040e-01, 5.3550e-02,\n",
" -7.3125e-02, 1.6401e-01, 1.6232e-01, -3.0164e-01, -2.8429e-01,\n",
" 5.9359e-02, -5.7466e-01, -9.3444e-02, 3.1263e-01, 4.3317e-01,\n",
" -1.0505e-01, 2.0451e-01, 3.2512e-02, 2.0723e-01, -2.0692e-02,\n",
" -7.1687e-02, -4.7481e-02, -9.9879e-02, 1.8269e-01, 2.6383e-01,\n",
" -1.8467e-01, -3.7178e-01, 7.7521e-02, 2.5127e-02, -1.0401e-01,\n",
" 3.6107e-03, -2.1520e-02, -8.3309e-02, -1.6115e-01, -1.6062e-01,\n",
" 4.8140e-02, -2.4970e-01, -1.4153e-01, 2.7024e-01, -3.3932e-02,\n",
" -2.0374e-01, -2.1484e-02, 2.9751e-01, 7.9157e-02, -1.1653e-01,\n",
" -1.9188e-01, 4.2722e-01, 2.7925e-01, 2.1393e-03, -1.2266e-02,\n",
" 1.6474e-01, 1.5236e-01, -2.8896e-01, 4.2948e-01, -3.1129e-01,\n",
" -8.3017e-03, -1.2746e-01, 9.3701e-02, 1.3876e-01, -2.2297e-01,\n",
" 2.7802e-01, 1.4876e-01, 2.7402e-01, 1.8048e-01, 1.0370e-01,\n",
" -1.9344e-02, 1.5151e-01, -1.0991e-01, 1.4710e-01, 2.1547e-01,\n",
" 1.1827e-01, -3.4855e-03, -3.2536e-01, -2.0146e-01, 2.6933e-01,\n",
" 3.2889e-01, 1.6086e-01, -4.0214e-02, 1.7948e-01, 9.3386e-02,\n",
" 2.3509e-01, 1.3713e-01, -3.9689e-01, 2.7102e-02, 3.2785e-01,\n",
" 9.5205e-02, 1.6616e-01, -8.5767e-02, -2.9547e-01, -2.5672e-01,\n",
" -9.8167e-02, 4.9819e-02, -3.1859e-01, -1.0416e-01, 3.6841e-01,\n",
" 3.9702e-02, 1.0092e-02, -1.5538e-01, -2.3540e-01, -3.1184e-02,\n",
" -1.0212e-01, 2.2566e-02, 8.2764e-02, -8.8543e-02, -4.3323e-01,\n",
" -9.3633e-02, -5.2647e-01, -1.1645e-01, 1.9042e-01, -3.2305e-01,\n",
" 2.3381e-01, -2.9311e-01, 9.7979e-02, 3.9168e-01, 4.9259e-02,\n",
" -1.4776e-03, -1.9380e-01, -2.3509e-02, 1.1105e-01, 3.2593e-01,\n",
" 2.3894e-01, -4.0230e-01, 1.0931e-01, 1.4188e-01, 2.5775e-01,\n",
" 1.4593e-01, -6.1273e-02, -1.1612e-01, 1.5098e-01, -2.0754e-01,\n",
" 1.7434e-01, -2.2652e-01, 1.8130e-01, -2.4506e-01, -2.1763e-01,\n",
" 2.7327e-01, -4.0463e-01, -3.2816e-02, 8.4527e-02, 2.6249e-01,\n",
" 5.3814e-03, -3.7149e-02, -8.5346e-02, 1.2447e-01, 1.6730e-01,\n",
" 1.2632e-01, -3.9535e-01, 2.6029e-01, -2.7662e-02, -1.0249e-02,\n",
" -3.3478e-02, 1.7810e-01, 2.5045e-01, 8.5239e-02, -3.8860e-01,\n",
" -1.2383e-01, 1.0050e-01, 2.8403e-01, -2.3275e-01, 1.4317e-01,\n",
" -2.5891e-01, -3.7882e-01, -1.4209e-01, 2.0352e-01, 2.2653e-01,\n",
" 1.7335e-01, -2.7438e-01, 1.6319e-01, -1.0932e-01, -4.1693e-01,\n",
" -3.6249e-01, -1.0035e-01, 2.3360e-01, 1.6881e-01, 1.8671e-01,\n",
" 2.4667e-01, 3.3260e-02, 1.0321e-01, 1.5088e-01, 1.4948e-01,\n",
" -1.4621e-01, 1.5615e-01, -3.4600e-01, -4.0963e-02, -2.5380e-01,\n",
" -1.9230e-01, -2.1718e-01, 3.9236e-01, -2.2989e-01, 2.3594e-01,\n",
" 3.7562e-01, -3.1114e-01, -1.2649e-01, 1.6051e-01, 1.0577e-01,\n",
" 8.3363e-02, -1.2593e-01, 2.0584e-01, 1.4127e-01, -1.0632e-01,\n",
" 2.3048e-01, -9.1782e-03, 2.4953e-01, 1.7332e-01, 8.9003e-02,\n",
" 1.5702e-01, 1.1363e-01, -1.4771e-01, 3.8719e-02, 3.1532e-03,\n",
" -1.9275e-02, -2.3103e-01, -1.4421e-01, 2.3575e-01, -5.3696e-02,\n",
" 3.7994e-02, -1.6641e-01, -1.1399e-01, 1.1890e-02, 3.9652e-01,\n",
" -3.6048e-01, 2.4448e-01, 7.5291e-02, 1.5195e-01, -2.2864e-01,\n",
" -2.1031e-01, 8.9896e-02, 1.6992e-01, -3.9863e-01, 1.7435e-03,\n",
" 1.6546e-01, 1.0483e-01, 2.1218e-01, 2.8159e-01, -5.0768e-05,\n",
" -9.4031e-02, 4.9128e-01, -1.6932e-01, -1.2761e-01, 2.5249e-01,\n",
" -2.7310e-01, -2.7744e-01, 2.4945e-01, -2.9252e-02, 3.0148e-01,\n",
" 1.1743e-01, 3.8095e-02, 6.4731e-02, -6.0554e-01, 6.6722e-02,\n",
" -4.5959e-01, -8.5795e-03, 3.6827e-02, -7.1025e-02, -2.0848e-01,\n",
" 1.4228e-01, 2.9630e-01, -2.4077e-01, -4.3098e-02, 2.2334e-01,\n",
" 8.4614e-02, -1.2553e-01, 4.8810e-01, 1.4035e-03, 2.0182e-01,\n",
" -6.3799e-02, 2.4192e-01, -2.0799e-01, 2.6687e-01, -2.7694e-01,\n",
" -9.9754e-02, 4.7169e-03, 8.3846e-02, 4.5165e-02, -5.9169e-02,\n",
" -3.5243e-01, 2.2125e-01, -1.9798e-02, -5.7139e-02, -4.0613e-02,\n",
" 9.3967e-02, -1.7488e-02, 6.2663e-02, 5.2265e-02, 3.4857e-01,\n",
" 2.2626e-01, -2.6472e-02, -3.7240e-01, -1.1370e-02, -9.6964e-02,\n",
" 5.6610e-02, 2.6788e-02, -2.4786e-02, 4.3236e-01, -7.6491e-02,\n",
" 5.2428e-03, -1.4826e-01, 2.6029e-01, 1.8327e-01, 1.2439e-01,\n",
" 1.3042e-01, 6.1801e-02, 1.3667e-01, -6.7373e-02, -1.2048e-02,\n",
" -1.4603e-01, -2.2505e-01, -2.9760e-01, 2.0056e-01, -2.2011e-01,\n",
" -1.8115e-01, 1.4262e-01, 2.1523e-01, -1.3893e-01, 1.4466e-01,\n",
" 2.9357e-01, 1.2101e-01, -1.4499e-01, 2.6571e-01, -1.0101e-01,\n",
" 1.1599e-01, 2.9781e-01, -2.0156e-02, 2.0140e-01, 5.0007e-01,\n",
" 2.1717e-01, -3.5394e-01, -1.6840e-02, -2.2424e-01, 1.1153e-02,\n",
" 2.4465e-01, -1.5139e-01, 1.9410e-01, 3.8725e-01, 2.9424e-01,\n",
" 4.2920e-01, -7.3521e-03, -1.1083e-01, 9.0861e-02, 2.2493e-01,\n",
" 2.7805e-02, -1.5957e-01, -1.9878e-01, 2.5621e-01, 6.2884e-02,\n",
" -1.5698e-01, -1.3003e-02, -1.1539e-01, 2.8383e-02, -1.2329e-01,\n",
" -3.8461e-01, 3.5215e-02, 1.8146e-01, -4.7366e-01, 7.3029e-02,\n",
" -2.9380e-01, 4.3577e-02, -2.3151e-01, 2.0148e-01, -2.2549e-01,\n",
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" -1.3722e-01, 2.4749e-02, 7.4731e-03, -1.4543e-02, -4.1486e-03,\n",
" 3.3529e-01, -1.2101e-01, 3.6759e-02, 3.6844e-02, 1.9582e-01,\n",
" -5.1381e-02, 2.0516e-01, 3.1175e-02, -1.4019e-01, -4.0386e-01,\n",
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" -1.3753e-01, -2.5359e-01, -2.0423e-01, -2.4466e-01, 8.1067e-02,\n",
" 1.6987e-01, 4.7120e-01, -3.9858e-01, -6.8325e-02, 4.7077e-01,\n",
" -6.8745e-02, -1.8953e-01, 2.7360e-01, 1.8793e-01, -3.3325e-01,\n",
" 3.1144e-01, 2.6919e-01, -5.6080e-02, 1.5771e-02, 5.0668e-01,\n",
" 1.1729e-01, 1.8437e-01, -2.0954e-01, 4.4338e-01, -2.1112e-01,\n",
" 3.1039e-01, -1.6460e-01, -2.1319e-01, -2.1592e-01, -1.9942e-02,\n",
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" 3.6021e-01, -3.7626e-01, -8.6387e-02, 1.3697e-02, -3.3636e-01,\n",
" 1.2770e-01, 1.0668e-01, 2.2197e-01, -3.7968e-01, -1.5053e-02,\n",
" 3.9753e-01, -2.9535e-01, 1.3459e-01, 3.2518e-01, 7.6786e-02,\n",
" 3.4168e-01, -2.8172e-02, 1.0189e-02, 5.9536e-02, -2.3156e-01,\n",
" -3.8199e-02, 1.3041e-01, 5.4866e-01, 1.5127e-01, -3.6896e-01,\n",
" 9.5292e-02, 2.4462e-01, -1.6506e-01, 3.1529e-01, -8.9680e-02,\n",
" -4.6637e-02, 2.6508e-01, -3.6751e-02, 1.5445e-01, -9.7824e-02,\n",
" -2.1623e-01, -3.0666e-01, 3.6944e-01, -1.8711e-01, -1.1481e-01,\n",
" -1.6787e-01, -1.1253e-01, -1.4680e-01, 4.1271e-02, -3.6980e-01,\n",
" 3.3081e-01, 1.2455e-01, -1.8123e-01, -6.8767e-02, -9.6390e-02,\n",
" -1.4910e-01, -2.0524e-01, -2.6686e-01, 4.2154e-01, -1.6543e-01,\n",
" -4.5050e-01, 2.5019e-01, 3.4722e-02, 3.3103e-01, 4.8806e-02,\n",
" 9.9796e-02, -3.0042e-02, 1.4140e-01, 9.6566e-02, -1.1071e-01,\n",
" 2.7989e-01, 6.1347e-02, -5.6286e-01, -1.4422e-01, -2.1070e-01,\n",
" 7.6292e-02, 1.9691e-01, -3.3576e-01, 1.7638e-02, 2.2105e-02,\n",
" 1.4265e-01, 3.0694e-02, -1.0665e-01, -5.5197e-02, 3.9164e-01,\n",
" 2.0961e-01, 2.9841e-01, 8.7946e-02, 2.4076e-01, -1.0636e-02,\n",
" -3.3807e-01, 2.2974e-02, 8.5258e-02, -1.8663e-01, 4.1414e-01,\n",
" -9.9141e-02, -3.8117e-01, -5.6155e-02, 3.9692e-01, 9.7551e-02,\n",
" -1.8710e-02, -5.0913e-02, 2.0049e-01, 1.5407e-01, -1.2523e-01,\n",
" 1.8187e-01, -9.7470e-03, -1.3372e-01, -1.0178e-01, 8.5468e-02,\n",
" -2.1953e-01, 4.7566e-02, -1.3239e-01, 1.3200e-03, -2.0911e-01,\n",
" 3.2521e-03, -2.1387e-01, 2.4508e-01, -3.2182e-01, 9.8634e-02,\n",
" 7.5848e-02, 2.9231e-01, -3.5121e-01, -1.4159e-01, -5.7937e-02,\n",
" 1.6263e-01, 2.5830e-01, 3.5601e-01, 2.3997e-02, 2.5322e-02,\n",
" -1.5363e-01, -2.6361e-01, 5.4986e-02, -2.0897e-01, 1.2282e-01,\n",
" 7.1346e-02, 2.4762e-01, -3.0430e-01, -1.8016e-01, 2.2226e-01,\n",
" -9.7989e-02, -1.4158e-01, 4.2292e-01, 2.5139e-01, 2.1049e-01,\n",
" 2.2865e-02, 2.4210e-01, 3.7744e-02, -1.7568e-01, -1.1512e-01,\n",
" -2.4392e-01, 6.9097e-02, -8.5799e-02, -5.8893e-02, -7.1211e-02,\n",
" -1.2143e-01, -1.9825e-01, -1.5658e-01, 1.5637e-01, 1.3693e-01,\n",
" 2.9095e-02, -5.5552e-02, -2.4771e-02, -2.7771e-01, 2.9286e-01,\n",
" 2.4894e-02, 7.2069e-02, -4.8322e-02, 2.3967e-02, -1.5199e-01,\n",
" 2.3989e-01, 2.0234e-01, 8.2009e-02, -1.8899e-01, -4.8667e-02,\n",
" -2.9075e-01, -3.5470e-01, 4.1930e-02, 1.3129e-01, 1.1387e-01,\n",
" -1.0165e-01, -2.7247e-01, -2.7974e-02, -1.3051e-01, 1.8051e-01,\n",
" -9.6646e-03, -1.5500e-01, -7.4565e-02, -6.0039e-02, -5.1055e-02,\n",
" 6.7692e-02, -2.0781e-01, -1.9844e-01, -1.2495e-01, -7.5151e-02,\n",
" -6.6146e-02, 3.6196e-01, -3.5989e-02, 2.7737e-01, -1.5471e-01,\n",
" 1.1208e-02, -1.9818e-01, 1.0743e-01, -7.3001e-02, 7.3365e-02,\n",
" 2.6398e-01, -4.2969e-01, -1.5308e-01, 6.1186e-03, -2.1301e-01,\n",
" -1.4149e-01, -7.1113e-02, -4.0364e-02, 2.1242e-01, -3.4205e-01,\n",
" 2.1659e-01, -8.0915e-02, 1.8907e-01, -9.4013e-02, -2.5456e-01,\n",
" -1.6216e-01, 2.3130e-02, 2.4984e-01, -3.3239e-01, -2.2947e-01,\n",
" -2.6681e-01, -9.7903e-02, -9.0469e-02, -2.6217e-01, 4.1510e-01,\n",
" -1.0590e-01, -5.5713e-02, 9.9271e-03, 4.3321e-01, 1.9454e-01,\n",
" 1.5135e-01, 2.1670e-01, -1.3371e-02, 2.7091e-02, 1.0805e-01,\n",
" -4.6743e-01, 2.3397e-01, -2.2627e-01, -1.2724e-01, 2.7149e-02,\n",
" 8.9104e-02, -3.1547e-02, 1.2930e-02, -1.1888e-01, -1.0141e-01,\n",
" 2.0849e-01, -3.6962e-01, -1.2304e-02, 2.7230e-01, 1.4519e-01,\n",
" -2.4969e-01, 4.2865e-02, 1.2965e-01, 3.7797e-01, 8.8492e-02,\n",
" -2.2487e-01, 1.3100e-01, -3.4240e-01, -2.4896e-02, -1.8675e-01,\n",
" -2.9198e-01, 1.3836e-01, -6.9468e-02, 5.4983e-02, -6.8482e-02,\n",
" -2.7968e-01, 2.1223e-01, -5.0621e-02, -6.3859e-02, 4.1759e-01,\n",
" 3.3747e-02, -1.1644e-01, 1.5398e-01, 1.5137e-02, -5.4925e-03,\n",
" -1.0726e-01, 2.6553e-01, 2.0031e-01, -2.7755e-01, 1.2135e-01,\n",
" -1.2860e-01, -2.5987e-02, -1.1620e-01]], grad_fn=<TanhBackward>), hidden_states=None, past_key_values=None, attentions=None, cross_attentions=None)"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"https://huggingface.co/transformers/main_classes/output.html#basemodeloutputwithpoolingandcrossattentionsM"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"https://arxiv.org/pdf/1907.11692.pdf"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(output)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([1, 16, 768])"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output[0].shape"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([1, 768])"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"output[1].shape"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"output = model(**encoded_input, output_hidden_states=True)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(output)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"13"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(output[2])"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[[ 0.1664, -0.0541, -0.0014, ..., -0.0811, 0.0794, 0.0155],\n",
" [-0.7241, 0.1035, 0.0784, ..., 0.2474, -0.0535, 0.4320],\n",
" [ 0.5926, -0.1062, 0.0372, ..., -0.0140, 0.1021, -0.2212],\n",
" ...,\n",
" [ 0.4734, -0.0570, -0.2506, ..., 0.4071, 0.4481, -0.2180],\n",
" [ 0.7836, -0.2838, -0.2083, ..., -0.0959, -0.0136, 0.1995],\n",
" [ 0.2733, -0.1372, -0.0387, ..., 0.5187, 0.1545, -0.2604]]],\n",
" grad_fn=<NativeLayerNormBackward>)"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output[2][0]"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([1, 16, 768])"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output[2][0].shape"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([1, 16, 768])"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output[2][1].shape"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([1, 16, 768])"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output[2][12].shape"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"output = model(**encoded_input, output_attentions=True)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(output)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"12"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(output[2])"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([1, 12, 16, 16])"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output[2][0].shape"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[[[9.8775e-01, 6.2288e-04, 8.7264e-04, ..., 5.4309e-04,\n",
" 1.3059e-03, 1.0826e-03],\n",
" [3.3152e-01, 7.3213e-03, 3.0339e-02, ..., 1.6386e-03,\n",
" 1.1041e-03, 1.0450e-03],\n",
" [8.4058e-01, 7.4270e-04, 1.8587e-04, ..., 1.9484e-03,\n",
" 8.3106e-04, 2.2206e-03],\n",
" ...,\n",
" [8.3998e-01, 6.3201e-06, 7.9328e-06, ..., 1.8371e-02,\n",
" 5.9146e-02, 7.1377e-02],\n",
" [9.4819e-01, 3.9591e-06, 2.9191e-06, ..., 9.6707e-03,\n",
" 1.1201e-02, 2.4954e-02],\n",
" [9.2851e-01, 4.9144e-04, 2.2858e-04, ..., 9.3861e-03,\n",
" 1.7582e-02, 2.4180e-02]],\n",
"\n",
" [[9.2353e-01, 4.3481e-03, 1.9423e-02, ..., 5.0829e-03,\n",
" 7.5931e-03, 4.6599e-03],\n",
" [9.7840e-01, 4.1909e-03, 9.0263e-03, ..., 2.1102e-06,\n",
" 5.4437e-07, 6.7581e-06],\n",
" [8.3596e-01, 6.3265e-02, 7.9091e-02, ..., 1.4975e-05,\n",
" 2.1750e-06, 2.3804e-06],\n",
" ...,\n",
" [4.7469e-01, 1.1083e-04, 1.8293e-03, ..., 9.7021e-03,\n",
" 6.5544e-03, 1.9043e-03],\n",
" [2.1963e-01, 1.3427e-06, 1.2042e-04, ..., 7.5510e-01,\n",
" 2.8724e-03, 6.2941e-03],\n",
" [4.2043e-01, 3.4030e-06, 6.4028e-05, ..., 8.2335e-02,\n",
" 3.9994e-01, 9.1114e-02]],\n",
"\n",
" [[9.8968e-01, 2.9357e-04, 2.4483e-04, ..., 2.0526e-04,\n",
" 4.1698e-04, 3.3650e-03],\n",
" [9.0939e-01, 3.1261e-03, 2.7859e-02, ..., 3.1149e-04,\n",
" 8.0127e-05, 2.8887e-03],\n",
" [8.9282e-01, 2.4450e-04, 5.3892e-03, ..., 8.5178e-04,\n",
" 9.8922e-05, 2.7169e-03],\n",
" ...,\n",
" [9.3745e-01, 2.0096e-06, 4.1223e-06, ..., 4.7319e-02,\n",
" 3.8060e-03, 6.3264e-03],\n",
" [9.5799e-01, 1.2817e-04, 1.0723e-05, ..., 1.0232e-03,\n",
" 2.1168e-02, 3.7038e-03],\n",
" [9.1897e-01, 4.5952e-04, 7.4514e-05, ..., 5.2304e-05,\n",
" 3.8385e-05, 5.9209e-02]],\n",
"\n",
" ...,\n",
"\n",
" [[9.7214e-01, 1.8048e-03, 2.0910e-03, ..., 1.5654e-03,\n",
" 2.0380e-03, 2.9465e-03],\n",
" [2.0737e-01, 1.5373e-02, 3.4949e-01, ..., 1.0591e-04,\n",
" 3.8994e-06, 1.9794e-05],\n",
" [7.0131e-01, 2.8094e-03, 7.6395e-03, ..., 1.2338e-03,\n",
" 8.6231e-05, 8.1068e-05],\n",
" ...,\n",
" [4.1426e-01, 1.9507e-06, 5.5085e-05, ..., 3.8152e-02,\n",
" 4.5979e-01, 6.9998e-02],\n",
" [7.5517e-01, 2.2428e-07, 3.2856e-06, ..., 1.3153e-02,\n",
" 5.5085e-03, 2.1891e-01],\n",
" [9.4142e-01, 3.3256e-05, 6.0546e-06, ..., 9.1890e-04,\n",
" 8.7666e-03, 3.8735e-02]],\n",
"\n",
" [[9.7447e-01, 1.1291e-03, 2.3473e-03, ..., 1.6628e-03,\n",
" 1.7247e-03, 3.7978e-03],\n",
" [7.2027e-01, 5.4353e-02, 5.0394e-03, ..., 4.7070e-03,\n",
" 1.4477e-03, 7.9330e-02],\n",
" [9.1602e-01, 6.2537e-03, 6.2520e-03, ..., 3.0431e-03,\n",
" 1.6902e-03, 2.6523e-02],\n",
" ...,\n",
" [8.7035e-01, 5.6680e-03, 2.5519e-04, ..., 1.0693e-02,\n",
" 1.0154e-02, 2.8158e-02],\n",
" [7.8992e-01, 1.3184e-03, 5.2799e-04, ..., 3.8399e-03,\n",
" 2.3379e-02, 5.4757e-02],\n",
" [4.0584e-01, 5.6631e-03, 8.5153e-03, ..., 1.0006e-02,\n",
" 1.0799e-02, 1.9912e-01]],\n",
"\n",
" [[9.8713e-01, 3.3973e-04, 9.6788e-04, ..., 2.1040e-04,\n",
" 1.3595e-03, 8.0080e-04],\n",
" [1.0312e-01, 4.2905e-03, 8.3475e-01, ..., 7.3782e-06,\n",
" 1.9842e-04, 1.3445e-03],\n",
" [7.9036e-01, 2.8547e-02, 5.0725e-02, ..., 1.9356e-05,\n",
" 6.4891e-05, 2.8477e-03],\n",
" ...,\n",
" [2.1335e-01, 9.7233e-06, 6.9469e-05, ..., 3.6693e-04,\n",
" 3.3324e-01, 1.3384e-02],\n",
" [1.1667e-02, 3.0911e-05, 2.5899e-06, ..., 5.6125e-01,\n",
" 2.7517e-04, 1.5053e-03],\n",
" [8.4494e-01, 8.0791e-04, 1.0116e-03, ..., 2.4602e-03,\n",
" 6.7727e-02, 1.1728e-02]]]], grad_fn=<SoftmaxBackward>)"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output[2][2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## gotowe api"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### generowanie tekstu"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Some weights of GPT2Model were not initialized from the model checkpoint at gpt2 and are newly initialized: ['h.0.attn.masked_bias', 'h.1.attn.masked_bias', 'h.2.attn.masked_bias', 'h.3.attn.masked_bias', 'h.4.attn.masked_bias', 'h.5.attn.masked_bias', 'h.6.attn.masked_bias', 'h.7.attn.masked_bias', 'h.8.attn.masked_bias', 'h.9.attn.masked_bias', 'h.10.attn.masked_bias', 'h.11.attn.masked_bias']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
}
],
"source": [
"model = pipeline('text-generation', model='gpt2')"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
]
},
{
"data": {
"text/plain": [
"[{'generated_text': 'Hello, I\\'m a computer science student \u2013 and there\\'s very little that I do for anything else. I need to keep doing what I do.\"'},\n",
" {'generated_text': \"Hello, I'm a computer science student. I am a Computer Science graduate and am very looking forward to the next year. I don't get paid\"},\n",
" {'generated_text': \"Hello, I'm a computer science student. I love reading and writing computer programs and then having fun with them. I'm definitely an open and interested\"},\n",
" {'generated_text': 'Hello, I\\'m a computer science student.\"\\n\\n\"Hey, I got a big question, that\\'s how much your time is going to cost'},\n",
" {'generated_text': 'Hello, I\\'m a computer science student at the University of Texas at Austin with plans to start working with IBM to develop a \"computer vision and data'}]"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model(\"Hello, I'm a computer science student\", max_length=30, num_return_sequences=5)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
]
},
{
"data": {
"text/plain": [
"[{'generated_text': \"I want to contribute to Google's Computer Vision Program, which is doing extensive work on big data and other areas.\"},\n",
" {'generated_text': \"I want to contribute to Google's Computer Vision Program, which is doing extensive work on big-picture solutions to help identify and solve the world's most\"},\n",
" {'generated_text': \"I want to contribute to Google's Computer Vision Program, which is doing extensive work on big screen technologies, to help us find a better way to deliver\"},\n",
" {'generated_text': \"I want to contribute to Google's Computer Vision Program, which is doing extensive work on big data and robotics applications at the time . We're also planning\"},\n",
" {'generated_text': \"I want to contribute to Google's Computer Vision Program, which is doing extensive work on big data on robots and AI to understand why people are better at\"}]"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model(\"I want to contribute to Google's Computer Vision Program, which is doing extensive work on big\", max_length=30, num_return_sequences=5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### sentiment analysis"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"from transformers import pipeline\n",
"\n",
"model = pipeline(\"sentiment-analysis\", model='distilbert-base-uncased-finetuned-sst-2-english')"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<transformers.pipelines.text_classification.TextClassificationPipeline at 0x7fd0cf863f10>"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'label': 'POSITIVE', 'score': 0.9998474717140198}]"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model(\"I'm very happy. Today is the beatifull weather\")"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'label': 'NEGATIVE', 'score': 0.9946851134300232}]"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model(\"It's raining. What a terrible day...\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## NER"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"model = pipeline(\"sentiment-analysis\", model='distilbert-base-uncased-finetuned-sst-2-english')"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"from transformers import pipeline\n",
"model = pipeline(\"ner\")"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"text = \"George Washington went to Washington\""
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'word': 'George',\n",
" 'score': 0.9983943104743958,\n",
" 'entity': 'I-PER',\n",
" 'index': 1,\n",
" 'start': 0,\n",
" 'end': 6},\n",
" {'word': 'Washington',\n",
" 'score': 0.9992505311965942,\n",
" 'entity': 'I-PER',\n",
" 'index': 2,\n",
" 'start': 7,\n",
" 'end': 17},\n",
" {'word': 'Washington',\n",
" 'score': 0.98389732837677,\n",
" 'entity': 'I-LOC',\n",
" 'index': 5,\n",
" 'start': 26,\n",
" 'end': 36}]"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model(text)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### masked language modelling"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ZADANIE (10 minut)\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "invalid syntax (<ipython-input-1-fcb19aa882d9>, line 3)",
"output_type": "error",
"traceback": [
"\u001b[0;36m File \u001b[0;32m\"<ipython-input-1-fcb19aa882d9>\"\u001b[0;36m, line \u001b[0;32m3\u001b[0m\n\u001b[0;31m przewidzia\u0107 <mask> token w \"The world <MASK> II started in 1939\"\" wg dowolnego angloj\u0119zycznego modelu\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
]
}
],
"source": [
"przewidzie\u0107 <mask> token w \"The world <MASK> II started in 1939\"\" wg dowolnego angloj\u0119zycznego modelu"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### text summarization"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"summarizer = pipeline(\"summarization\")"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"ARTICLE = \"\"\" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York.\n",
"A year later, she got married again in Westchester County, but to a different man and without divorcing her first husband.\n",
"Only 18 days after that marriage, she got hitched yet again. Then, Barrientos declared \"I do\" five more times, sometimes only within two weeks of each other.\n",
"In 2010, she married once more, this time in the Bronx. In an application for a marriage license, she stated it was her \"first and only\" marriage.\n",
"Barrientos, now 39, is facing two criminal counts of \"offering a false instrument for filing in the first degree,\" referring to her false statements on the\n",
"2010 marriage license application, according to court documents.\n",
"Prosecutors said the marriages were part of an immigration scam.\n",
"On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to her attorney, Christopher Wright, who declined to comment further.\n",
"After leaving court, Barrientos was arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New York subway through an emergency exit, said Detective\n",
"Annette Markowski, a police spokeswoman. In total, Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002.\n",
"All occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be married to four men, and at one time, she was married to eight men at once, prosecutors say.\n",
"Prosecutors said the immigration scam involved some of her husbands, who filed for permanent residence status shortly after the marriages.\n",
"Any divorces happened only after such filings were approved. It was unclear whether any of the men will be prosecuted.\n",
"The case was referred to the Bronx District Attorney\\'s Office by Immigration and Customs Enforcement and the Department of Homeland Security\\'s\n",
"Investigation Division. Seven of the men are from so-called \"red-flagged\" countries, including Egypt, Turkey, Georgia, Pakistan and Mali.\n",
"Her eighth husband, Rashid Rajput, was deported in 2006 to his native Pakistan after an investigation by the Joint Terrorism Task Force.\n",
"If convicted, Barrientos faces up to four years in prison. Her next court appearance is scheduled for May 18.\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[{'summary_text': ' Liana Barrientos, 39, is charged with two counts of \"offering a false instrument for filing in the first degree\" In total, she has been married 10 times, with nine of her marriages occurring between 1999 and 2002 . At one time, she was married to eight men at once, prosecutors say .'}]\n"
]
}
],
"source": [
"print(summarizer(ARTICLE, max_length=130, min_length=30, do_sample=False))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ZADANIE DOMOWE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- sforkowa\u0107 repozytorium: https://git.wmi.amu.edu.pl/kubapok/paranormal-or-skeptic-ISI-public\n",
"- finetunowa\u0107 klasyfikator bazuj\u0105cy na jakie\u015b pretrenowanej sie\u0107 typu transformer (np BERT, Roberta). Mo\u017cna u\u017cy\u0107 dowolnej biblioteki\n",
" (np hugging face, fairseq)\n",
"- stworzy\u0107 predykcje w plikach dev-0/out.tsv oraz test-A/out.tsv\n",
"- wynik accuracy sprawdzony za pomoc\u0105 narz\u0119dzia geval (patrz poprzednie zadanie) powinien wynosi\u0107 conajmniej 0.67\n",
"- prosz\u0119 umie\u015bci\u0107 predykcj\u0119 oraz skrypty generuj\u0105ce (w postaci tekstowej a nie jupyter) w repo, a w MS TEAMS umie\u015bci\u0107 link do swojego repo\n",
"termin 22.06, 60 punkt\u00f3w\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3"
},
"author": "Jakub Pokrywka",
"email": "kubapok@wmi.amu.edu.pl",
"lang": "pl",
"subtitle": "12.Transformery[\u0107wiczenia]",
"title": "Ekstrakcja informacji",
"year": "2021"
2021-06-09 12:46:15 +02:00
},
2021-09-27 12:34:44 +02:00
"nbformat": 4,
"nbformat_minor": 4
}
