diff --git a/bart/sparql.ipynb b/bart/sparql.ipynb
index dd7c88a56dfbfc28d2e313d43fe8b60af9505d47..e4e19e77a5acb6b6f13a810ad2e9e0e9988fbb7e 100644
--- a/bart/sparql.ipynb
+++ b/bart/sparql.ipynb
@@ -1,3015 +1,3017 @@
{
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- {
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- "execution_count": 1,
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- "id": "5nVk6kKcX28W"
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "NOTE: Redirects are currently not supported in Windows or MacOs.\n"
- ]
- }
- ],
- "source": [
- "from transformers import BartTokenizer, BartForConditionalGeneration\n",
- "\n",
- "from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments\n",
- "from transformers.models.bart.modeling_bart import shift_tokens_right\n",
- "\n",
- "import datasets\n",
- "import torch\n",
- "import pandas as pd\n",
- "import numpy as np"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
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- ]
- },
- "id": "Mrpgx8tsdHkP",
- "outputId": "0dfaec91-dd06-4cf2-a0d2-48eade181592"
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "id": "5nVk6kKcX28W"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "NOTE: Redirects are currently not supported in Windows or MacOs.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from transformers import BartTokenizer, BartForConditionalGeneration\n",
+ "\n",
+ "from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments\n",
+ "from transformers.models.bart.modeling_bart import shift_tokens_right\n",
+ "\n",
+ "import datasets\n",
+ "import torch\n",
+ "import pandas as pd\n",
+ "import numpy as np"
+ ]
},
- "outputs": [],
- "source": [
- "model = BartForConditionalGeneration.from_pretrained(\"facebook/bart-base\")\n",
- "tokenizer = BartTokenizer.from_pretrained(\"facebook/bart-base\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "id": "WCgdb52Ldcc7"
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+ "outputId": "0dfaec91-dd06-4cf2-a0d2-48eade181592"
+ },
+ "outputs": [],
+ "source": [
+ "model = BartForConditionalGeneration.from_pretrained(\"facebook/bart-base\")\n",
+ "tokenizer = BartTokenizer.from_pretrained(\"facebook/bart-base\")"
+ ]
},
- "outputs": [],
- "source": [
- "def encode(batch):\n",
- " return tokenizer(batch, padding=\"max_length\", max_length=256, return_tensors=\"pt\")\n",
- "\n",
- "def convert_to_features(example_batch):\n",
- " input_encodings = encode(example_batch['text'])\n",
- " target_encodings = encode(example_batch['summary'])\n",
- " \n",
- " labels = target_encodings['input_ids']\n",
- " decoder_input_ids = shift_tokens_right(labels, model.config.pad_token_id, model.config.decoder_start_token_id)\n",
- " labels[labels[:, :] == model.config.pad_token_id] = -100\n",
- " \n",
- " encodings = {\n",
- " 'input_ids': input_encodings['input_ids'],\n",
- " 'attention_mask': input_encodings['attention_mask'],\n",
- " 'decoder_input_ids': decoder_input_ids,\n",
- " 'labels': labels,\n",
- " }\n",
- "\n",
- " return encodings\n",
- "\n",
- "def get_dataset(path):\n",
- " df = pd.read_csv(path, sep=\",\", on_bad_lines='skip')\n",
- " dataset = datasets.Dataset.from_pandas(df)\n",
- " dataset = dataset.map(convert_to_features, batched=True)\n",
- " columns = ['input_ids', 'labels', 'decoder_input_ids','attention_mask',] \n",
- " dataset.set_format(type='torch', columns=columns)\n",
- " return dataset"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "colab": {
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+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "id": "WCgdb52Ldcc7"
+ },
+ "outputs": [],
+ "source": [
+ "def encode(batch):\n",
+ " return tokenizer(batch, padding=\"max_length\", max_length=256, return_tensors=\"pt\")\n",
+ "\n",
+ "def convert_to_features(example_batch):\n",
+ " input_encodings = encode(example_batch['text'])\n",
+ " target_encodings = encode(example_batch['summary'])\n",
+ " \n",
+ " labels = target_encodings['input_ids']\n",
+ " decoder_input_ids = shift_tokens_right(labels, model.config.pad_token_id, model.config.decoder_start_token_id)\n",
+ " labels[labels[:, :] == model.config.pad_token_id] = -100\n",
+ " \n",
+ " encodings = {\n",
+ " 'input_ids': input_encodings['input_ids'],\n",
+ " 'attention_mask': input_encodings['attention_mask'],\n",
+ " 'decoder_input_ids': decoder_input_ids,\n",
+ " 'labels': labels,\n",
+ " }\n",
+ "\n",
+ " return encodings\n",
+ "\n",
+ "def get_dataset(path):\n",
+ " df = pd.read_csv(path, sep=\",\", on_bad_lines='skip')\n",
+ " dataset = datasets.Dataset.from_pandas(df)\n",
+ " dataset = dataset.map(convert_to_features, batched=True)\n",
+ " columns = ['input_ids', 'labels', 'decoder_input_ids','attention_mask',] \n",
+ " dataset.set_format(type='torch', columns=columns)\n",
+ " return dataset"
+ ]
},
- "outputs": [
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- },
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- "version_major": 2,
- "version_minor": 0
+ "id": "PkVpKeHKfsUM",
+ "outputId": "f30c6ef0-3572-459f-a693-13c48365e36f"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "ab64ff32e3004596a03f997d01f80a66",
+ "version_major": 2,
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},
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "EPOCHS = 1\n",
- "\n",
- "train_dataset = get_dataset(\"../data/tokenized/lcquad-train-append-1.csv\")\n",
- "test_dataset = get_dataset(\"test.csv\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "4tEcjx1ziWZT",
- "outputId": "41912f20-119c-4e9f-e6da-c6070ef8bc0d"
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "207518a287584b199b383697f5d18a74",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
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+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "EPOCHS = 1\n",
+ "\n",
+ "train_dataset = get_dataset(\"../data/tokenized/lcquad-train-append-1.csv\")\n",
+ "test_dataset = get_dataset(\"test.csv\")"
+ ]
},
- "outputs": [],
- "source": [
- "training_args = Seq2SeqTrainingArguments(\n",
- " output_dir='./models/blackbox', \n",
- " num_train_epochs=1,\n",
- " per_device_train_batch_size=1,\n",
- " per_device_eval_batch_size=1,\n",
- " warmup_steps=10,\n",
- " weight_decay=0.01,\n",
- " logging_dir='./logs',\n",
- ")\n",
- "\n",
- "trainer = Seq2SeqTrainer(\n",
- " model=model,\n",
- " args=training_args,\n",
- " train_dataset=train_dataset,\n",
- " eval_dataset=test_dataset\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 408
- },
- "id": "QqLE1QMAjR2T",
- "outputId": "4bc9626b-1d22-4c4b-ffbc-55b19e4297fe"
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "4tEcjx1ziWZT",
+ "outputId": "41912f20-119c-4e9f-e6da-c6070ef8bc0d"
+ },
+ "outputs": [],
+ "source": [
+ "training_args = Seq2SeqTrainingArguments(\n",
+ " output_dir='./models/blackbox', \n",
+ " num_train_epochs=1,\n",
+ " per_device_train_batch_size=1,\n",
+ " per_device_eval_batch_size=1,\n",
+ " warmup_steps=10,\n",
+ " weight_decay=0.01,\n",
+ " logging_dir='./logs',\n",
+ ")\n",
+ "\n",
+ "trainer = Seq2SeqTrainer(\n",
+ " model=model,\n",
+ " args=training_args,\n",
+ " train_dataset=train_dataset,\n",
+ " eval_dataset=test_dataset\n",
+ ")"
+ ]
},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "The following columns in the training set don't have a corresponding argument in `BartForConditionalGeneration.forward` and have been ignored: text, summary. If text, summary are not expected by `BartForConditionalGeneration.forward`, you can safely ignore this message.\n",
- "C:\\Users\\ludde\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\transformers\\optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
- " warnings.warn(\n",
- "***** Running training *****\n",
- " Num examples = 1984\n",
- " Num Epochs = 1\n",
- " Instantaneous batch size per device = 1\n",
- " Total train batch size (w. parallel, distributed & accumulation) = 1\n",
- " Gradient Accumulation steps = 1\n",
- " Total optimization steps = 1984\n"
- ]
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "a1d9e54a7c1b49618e4ce8a0e6bcccd0",
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- "version_minor": 0
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 408
+ },
+ "id": "QqLE1QMAjR2T",
+ "outputId": "4bc9626b-1d22-4c4b-ffbc-55b19e4297fe"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "The following columns in the training set don't have a corresponding argument in `BartForConditionalGeneration.forward` and have been ignored: text, summary. If text, summary are not expected by `BartForConditionalGeneration.forward`, you can safely ignore this message.\n",
+ "C:\\Users\\ludde\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\transformers\\optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
+ " warnings.warn(\n",
+ "***** Running training *****\n",
+ " Num examples = 1984\n",
+ " Num Epochs = 1\n",
+ " Instantaneous batch size per device = 1\n",
+ " Total train batch size (w. parallel, distributed & accumulation) = 1\n",
+ " Gradient Accumulation steps = 1\n",
+ " Total optimization steps = 1984\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "a1d9e54a7c1b49618e4ce8a0e6bcccd0",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/1984 [00:00<?, ?it/s]"
+ ]
},
- "text/plain": [
- " 0%| | 0/1984 [00:00<?, ?it/s]"
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Saving model checkpoint to ./models/blackbox\\checkpoint-500\n",
+ "Configuration saved in ./models/blackbox\\checkpoint-500\\config.json\n"
]
},
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Saving model checkpoint to ./models/blackbox\\checkpoint-500\n",
- "Configuration saved in ./models/blackbox\\checkpoint-500\\config.json\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "{'loss': 0.7989, 'learning_rate': 3.75886524822695e-05, 'epoch': 0.25}\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Model weights saved in ./models/blackbox\\checkpoint-500\\pytorch_model.bin\n",
- "Saving model checkpoint to ./models/blackbox\\checkpoint-1000\n",
- "Configuration saved in ./models/blackbox\\checkpoint-1000\\config.json\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "{'loss': 0.4205, 'learning_rate': 2.492401215805471e-05, 'epoch': 0.5}\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Model weights saved in ./models/blackbox\\checkpoint-1000\\pytorch_model.bin\n",
- "Saving model checkpoint to ./models/blackbox\\checkpoint-1500\n",
- "Configuration saved in ./models/blackbox\\checkpoint-1500\\config.json\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "{'loss': 0.3245, 'learning_rate': 1.225937183383992e-05, 'epoch': 0.76}\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Model weights saved in ./models/blackbox\\checkpoint-1500\\pytorch_model.bin\n",
- "\n",
- "\n",
- "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
- "\n",
- "\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "{'train_runtime': 204.0052, 'train_samples_per_second': 9.725, 'train_steps_per_second': 9.725, 'train_loss': 0.4499410736945368, 'epoch': 1.0}\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "TrainOutput(global_step=1984, training_loss=0.4499410736945368, metrics={'train_runtime': 204.0052, 'train_samples_per_second': 9.725, 'train_steps_per_second': 9.725, 'train_loss': 0.4499410736945368, 'epoch': 1.0})"
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'loss': 0.7989, 'learning_rate': 3.75886524822695e-05, 'epoch': 0.25}\n"
]
},
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#torch.cuda.empty_cache()\n",
- "trainer.train()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Saving model checkpoint to ./trained-models/blackbox\n",
- "Configuration saved in ./trained-models/blackbox\\config.json\n",
- "Model weights saved in ./trained-models/blackbox\\pytorch_model.bin\n"
- ]
- }
- ],
- "source": [
- "trainer.save_model(\"./trained-models/blackbox\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 69
- },
- "id": "ivZONIgak1IQ",
- "outputId": "e90429cf-94db-4376-cece-1a89b3e3f6fe"
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "***** Running Prediction *****\n",
- " Num examples = 1\n",
- " Batch size = 1\n"
- ]
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "97cd3b3590f345499e483810ad116c7c",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- " 0%| | 0/1 [00:00<?, ?it/s]"
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Model weights saved in ./models/blackbox\\checkpoint-500\\pytorch_model.bin\n",
+ "Saving model checkpoint to ./models/blackbox\\checkpoint-1000\n",
+ "Configuration saved in ./models/blackbox\\checkpoint-1000\\config.json\n"
]
},
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "prediction = trainer.predict([test_dataset[0]], max_length=256, num_beams=4)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "3cC1vcudmGBT",
- "outputId": "2b096bb7-c16b-4349-9405-3e5357f7fb5f"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(1, 256, 50265)\n",
- " \" \":: D:Salt?:Saltp: res dbtime_ \": Dp:time }:timep?? duri? \" DISTINCT?uri WHERE { res:Salt_Lake_City db?:urip}?:regionries }type_? \"uri } \"ries:????????????? </s> </s></s></s></s></s> r r r r r r r</s></s></s></s></s> r r r r r r r r</s></s> r r r r r r r r r r r r r r r r r d_ dByByBy__ d d _ d d d____??____?????????</s>???????????????????????????????????????</s></s>????</s>AreaAreaAreaArea</s></s>AreaAreaAreaArea</s>_AreaAreaAreaAreaArea_AreaAreaArea</s>__AreaAreaAreaBy_AreaAreaAreaBy__\n",
- "(1, 256, 1024)\n",
- " says even major major major major says \" \" \" says News a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a news a a a a a a a says \" \" a a a a a a a a] a a a a a a a] a a a a a a a a a a a a a a a a] a a says says a a a a major major major major a a a]]]] news a a a a a a a a a a a a a a a a a a a News a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a newsA aA newsA]AA]]]]]]]]] news a a a a a a a a a a a.\" a \" a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a \" says a\n"
- ]
- }
- ],
- "source": [
- "for pred in prediction.predictions:\n",
- " print(np.array(pred).shape)\n",
- " print(tokenizer.batch_decode(torch.tensor(np.argmax(pred, axis=-1)))[0])\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "id": "oJXYzdTaoNDH"
- },
- "outputs": [],
- "source": [
- "def to_query(sentence):\n",
- " input = encode(sentence)\n",
- " input.to('cuda')\n",
- " predictions = model.generate(input['input_ids'], max_length=256, num_beams=20)\n",
- " return tokenizer.batch_decode(predictions)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "tdT5HoN0v2h0",
- "outputId": "652cfcb6-bfea-414e-c6bf-108b76fbf70d"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['</s><s> \" SELECT DISTINCT?uri WHERE {?uri dbp:president res:Barack_Onger } \"</s>']"
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'loss': 0.4205, 'learning_rate': 2.492401215805471e-05, 'epoch': 0.5}\n"
]
},
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "q = to_query(\"Who is barack obama?\")\n",
- "\n",
- "q"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Not currently used\n",
- "\n",
- "# import json\n",
- "# import csv\n",
- "# from tqdm import tqdm\n",
- "\n",
- "# pred = {}\n",
- "# pred[\"questions\"] = []\n",
- "# with open(\"../data/qald-9-test.json\", \"r\", encoding='utf-8') as f:\n",
- "# corr = json.load(f)\n",
- "# for i in tqdm(range(len(list(corr[\"questions\"])))):\n",
- "# #Build the json predicted file\n",
- "# pred[\"questions\"].append({\"query\": {\"sparql\" : to_query(corr[\"questions\"][i][\"question\"][0][\"string\"])[0][7:-4]}})\n",
- "\n",
- "# json.dump(pred, open(\"pred.json\", \"w\"))\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "ename": "FileNotFoundError",
- "evalue": "[Errno 2] No such file or directory: '../data/tokenized/lcquad-test-append-1'",
- "output_type": "error",
- "traceback": [
- "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[1;32mIn [9], line 21\u001b[0m\n\u001b[0;32m 18\u001b[0m \u001b[39mif\u001b[39;00m BERT_LARGE:\n\u001b[0;32m 19\u001b[0m out_file \u001b[39m=\u001b[39m out_file\u001b[39m.\u001b[39mreplace(\u001b[39m\"\u001b[39m\u001b[39m.csv\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m-large.csv\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m---> 21\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mopen\u001b[39m(TEST_FILE, \u001b[39m\"\u001b[39m\u001b[39mr\u001b[39m\u001b[39m\"\u001b[39m, encoding\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mutf-8\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mas\u001b[39;00m f, \u001b[39mopen\u001b[39m(out_file, \u001b[39m\"\u001b[39m\u001b[39mw\u001b[39m\u001b[39m\"\u001b[39m, encoding\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mutf-8\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mas\u001b[39;00m out:\n\u001b[0;32m 22\u001b[0m \u001b[39mif\u001b[39;00m TEST_FILE_IS_JSON:\n\u001b[0;32m 23\u001b[0m test_data \u001b[39m=\u001b[39m json\u001b[39m.\u001b[39mload(f)\n",
- "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../data/tokenized/lcquad-test-append-1'"
- ]
- }
- ],
- "source": [
- "import csv\n",
- "import json\n",
- "import generate_single_query as gsq\n",
- "\n",
- "# Uncomment the following line to use a pretrained model\n",
- "# model, tokenizer = gsq.setup()\n",
- "\n",
- "# Open test file and predict all queries, save to new csv file\n",
- "TEST_FILE = \"../data/tokenized/lcquad-test-append-1.csv\"\n",
- "# JSON or CSV\n",
- "TEST_FILE_IS_JSON = False\n",
- "out_file = TEST_FILE.replace(\"/tokenized/\", \"/predicted/\").replace(\"tokenized\", \"predicted\")\n",
- "if (EPOCHS > 1):\n",
- " out_file = out_file.replace(\".csv\", f\"-{EPOCHS}-epochs.csv\")\n",
- "\n",
- "\n",
- "BERT_LARGE = False\n",
- "if BERT_LARGE:\n",
- " out_file = out_file.replace(\".csv\", \"-large.csv\")\n",
- "\n",
- "with open(TEST_FILE, \"r\", encoding=\"utf-8\") as f, open(out_file, \"w\", encoding=\"utf-8\") as out:\n",
- " if TEST_FILE_IS_JSON:\n",
- " test_data = json.load(f)\n",
- " test_data = test_data[\"questions\"]\n",
- " test_data = [q[\"question\"][0][\"string\"] for q in test_data]\n",
- " else:\n",
- " test_data = csv.reader(f, delimiter=\",\")\n",
- " next(test_data)\n",
- " test_data = list(test_data)\n",
- " test_data = [q[0] for q in test_data]\n",
- " \n",
- " out.write(\"text,summary\\n\")\n",
- " \n",
- " for i, question in enumerate(test_data):\n",
- " predicted = gsq.predict_query(question, model, tokenizer)\n",
- " out.write(f\"\\\"{question}\\\",\\\"{predicted}\\\"\\n\")\n",
- " print(f\"\\\"{question}\\\",\\\"{predicted}\\\"\\n\")\n",
- "\n",
- " \n"
- ]
- },
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- "cell_type": "code",
- "execution_count": null,
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- "language": "python",
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+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Model weights saved in ./models/blackbox\\checkpoint-1000\\pytorch_model.bin\n",
+ "Saving model checkpoint to ./models/blackbox\\checkpoint-1500\n",
+ "Configuration saved in ./models/blackbox\\checkpoint-1500\\config.json\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'loss': 0.3245, 'learning_rate': 1.225937183383992e-05, 'epoch': 0.76}\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Model weights saved in ./models/blackbox\\checkpoint-1500\\pytorch_model.bin\n",
+ "\n",
+ "\n",
+ "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'train_runtime': 204.0052, 'train_samples_per_second': 9.725, 'train_steps_per_second': 9.725, 'train_loss': 0.4499410736945368, 'epoch': 1.0}\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "TrainOutput(global_step=1984, training_loss=0.4499410736945368, metrics={'train_runtime': 204.0052, 'train_samples_per_second': 9.725, 'train_steps_per_second': 9.725, 'train_loss': 0.4499410736945368, 'epoch': 1.0})"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#torch.cuda.empty_cache()\n",
+ "trainer.train()"
+ ]
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+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Saving model checkpoint to ./trained-models/blackbox\n",
+ "Configuration saved in ./trained-models/blackbox\\config.json\n",
+ "Model weights saved in ./trained-models/blackbox\\pytorch_model.bin\n"
+ ]
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- "value": " 26.0/26.0 [00:00<00:00, 824B/s]"
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+ "source": [
+ "trainer.save_model(\"./trained-models/blackbox\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 69
+ },
+ "id": "ivZONIgak1IQ",
+ "outputId": "e90429cf-94db-4376-cece-1a89b3e3f6fe"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "***** Running Prediction *****\n",
+ " Num examples = 1\n",
+ " Batch size = 1\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "97cd3b3590f345499e483810ad116c7c",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/1 [00:00<?, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
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- "top": null,
- "visibility": null,
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+ ],
+ "source": [
+ "prediction = trainer.predict([test_dataset[0]], max_length=256, num_beams=4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "3cC1vcudmGBT",
+ "outputId": "2b096bb7-c16b-4349-9405-3e5357f7fb5f"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(1, 256, 50265)\n",
+ " \" \":: D:Salt?:Saltp: res dbtime_ \": Dp:time }:timep?? duri? \" DISTINCT?uri WHERE { res:Salt_Lake_City db?:urip}?:regionries }type_? \"uri } \"ries:????????????? </s> </s></s></s></s></s> r r r r r r r</s></s></s></s></s> r r r r r r r r</s></s> r r r r r r r r r r r r r r r r r d_ dByByBy__ d d _ d d d____??____?????????</s>???????????????????????????????????????</s></s>????</s>AreaAreaAreaArea</s></s>AreaAreaAreaArea</s>_AreaAreaAreaAreaArea_AreaAreaArea</s>__AreaAreaAreaBy_AreaAreaAreaBy__\n",
+ "(1, 256, 1024)\n",
+ " says even major major major major says \" \" \" says News a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a news a a a a a a a says \" \" a a a a a a a a] a a a a a a a] a a a a a a a a a a a a a a a a] a a says says a a a a major major major major a a a]]]] news a a a a a a a a a a a a a a a a a a a News a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a newsA aA newsA]AA]]]]]]]]] news a a a a a a a a a a a.\" a \" a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a \" says a\n"
+ ]
}
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- ],
- "layout": "IPY_MODEL_628de0291b4346dbb6e8d04b16149009"
+ ],
+ "source": [
+ "for pred in prediction.predictions:\n",
+ " print(np.array(pred).shape)\n",
+ " print(tokenizer.batch_decode(torch.tensor(np.argmax(pred, axis=-1)))[0])\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "id": "oJXYzdTaoNDH"
+ },
+ "outputs": [],
+ "source": [
+ "def to_query(sentence):\n",
+ " input = encode(sentence)\n",
+ " input.to('cuda')\n",
+ " predictions = model.generate(input['input_ids'], max_length=256, num_beams=20)\n",
+ " return tokenizer.batch_decode(predictions)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "tdT5HoN0v2h0",
+ "outputId": "652cfcb6-bfea-414e-c6bf-108b76fbf70d"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['</s><s> \" SELECT DISTINCT?uri WHERE {?uri dbp:president res:Barack_Onger } \"</s>']"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "q = to_query(\"Who is barack obama?\")\n",
+ "\n",
+ "q"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Not currently used\n",
+ "\n",
+ "# import json\n",
+ "# import csv\n",
+ "# from tqdm import tqdm\n",
+ "\n",
+ "# pred = {}\n",
+ "# pred[\"questions\"] = []\n",
+ "# with open(\"../data/qald-9-test.json\", \"r\", encoding='utf-8') as f:\n",
+ "# corr = json.load(f)\n",
+ "# for i in tqdm(range(len(list(corr[\"questions\"])))):\n",
+ "# #Build the json predicted file\n",
+ "# pred[\"questions\"].append({\"query\": {\"sparql\" : to_query(corr[\"questions\"][i][\"question\"][0][\"string\"])[0][7:-4]}})\n",
+ "\n",
+ "# json.dump(pred, open(\"pred.json\", \"w\"))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "FileNotFoundError",
+ "evalue": "[Errno 2] No such file or directory: '../data/tokenized/lcquad-test-append-1'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[1;32mIn [9], line 21\u001b[0m\n\u001b[0;32m 18\u001b[0m \u001b[39mif\u001b[39;00m BERT_LARGE:\n\u001b[0;32m 19\u001b[0m out_file \u001b[39m=\u001b[39m out_file\u001b[39m.\u001b[39mreplace(\u001b[39m\"\u001b[39m\u001b[39m.csv\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m-large.csv\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m---> 21\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mopen\u001b[39m(TEST_FILE, \u001b[39m\"\u001b[39m\u001b[39mr\u001b[39m\u001b[39m\"\u001b[39m, encoding\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mutf-8\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mas\u001b[39;00m f, \u001b[39mopen\u001b[39m(out_file, \u001b[39m\"\u001b[39m\u001b[39mw\u001b[39m\u001b[39m\"\u001b[39m, encoding\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mutf-8\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mas\u001b[39;00m out:\n\u001b[0;32m 22\u001b[0m \u001b[39mif\u001b[39;00m TEST_FILE_IS_JSON:\n\u001b[0;32m 23\u001b[0m test_data \u001b[39m=\u001b[39m json\u001b[39m.\u001b[39mload(f)\n",
+ "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../data/tokenized/lcquad-test-append-1'"
+ ]
}
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+ ],
+ "source": [
+ "import csv\n",
+ "import json\n",
+ "import generate_single_query as gsq\n",
+ "\n",
+ "# Uncomment the following line to use a pretrained model\n",
+ "# model, tokenizer = gsq.setup()\n",
+ "\n",
+ "# Open test file and predict all queries, save to new csv file\n",
+ "TEST_FILE = \"../data/tokenized/lcquad-test-append-1.csv\"\n",
+ "# JSON or CSV\n",
+ "TEST_FILE_IS_JSON = False\n",
+ "out_file = TEST_FILE.replace(\"/tokenized/\", \"/predicted/\").replace(\"tokenized\", \"predicted\")\n",
+ "if (EPOCHS > 1):\n",
+ " out_file = out_file.replace(\".csv\", f\"-{EPOCHS}-epochs.csv\")\n",
+ "\n",
+ "\n",
+ "BERT_LARGE = False\n",
+ "if BERT_LARGE:\n",
+ " out_file = out_file.replace(\".csv\", \"-large.csv\")\n",
+ "\n",
+ "with open(TEST_FILE, \"r\", encoding=\"utf-8\") as f, open(out_file, \"w\", encoding=\"utf-8\") as out:\n",
+ " if TEST_FILE_IS_JSON:\n",
+ " test_data = json.load(f)\n",
+ " test_data = test_data[\"questions\"]\n",
+ " test_data = [q[\"question\"][0][\"string\"] for q in test_data]\n",
+ " else:\n",
+ " test_data = csv.reader(f, delimiter=\",\")\n",
+ " next(test_data)\n",
+ " test_data = list(test_data)\n",
+ " test_data = [q[0] for q in test_data]\n",
+ " \n",
+ " out.write(\"text,summary\\n\")\n",
+ " \n",
+ " for i, question in enumerate(test_data):\n",
+ " predicted = gsq.predict_query(question, model, tokenizer)\n",
+ " out.write(f\"\\\"{question}\\\",\\\"{predicted}\\\"\\n\")\n",
+ " print(f\"\\\"{question}\\\",\\\"{predicted}\\\"\\n\")\n",
+ "\n",
+ " \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
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