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Kacper Dudzic 6628892bee Reupload solution
2023-06-08 20:23:57 +02:00

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},
"cells": [
{
"cell_type": "markdown",
"source": [
"# Setup"
],
"metadata": {
"id": "tZbwIaoXO3di"
}
},
{
"cell_type": "code",
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "n3ScGYli1QGA",
"outputId": "6ac1a81b-f9a0-40a8-9547-adefff60e792"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mounted at /content/drive\n"
]
}
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "6taSJjLGkTYE",
"outputId": "2dd9b9b0-833b-41d5-b524-b7eef762994d"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Cloning into 'cnlps-caiccaic'...\n",
"remote: Enumerating objects: 73, done.\u001b[K\n",
"remote: Counting objects: 100% (73/73), done.\u001b[K\n",
"remote: Compressing objects: 100% (56/56), done.\u001b[K\n",
"remote: Total 73 (delta 32), reused 41 (delta 11), pack-reused 0\u001b[K\n",
"Unpacking objects: 100% (73/73), 1.89 MiB | 4.48 MiB/s, done.\n"
]
}
],
"source": [
"!git clone https://github.com/kubapok/cnlps-caiccaic.git"
]
},
{
"cell_type": "code",
"source": [
"!pip install -Uq datasets transformers peft bitsandbytes loralib accelerate"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "1qS0p25llt37",
"outputId": "e77b4672-c85f-4a5c-9e98-7de4070267a0"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m474.6/474.6 kB\u001b[0m \u001b[31m10.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.1/7.1 MB\u001b[0m \u001b[31m78.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.8/56.8 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.2/92.2 MB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m227.5/227.5 kB\u001b[0m \u001b[31m27.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m110.5/110.5 kB\u001b[0m \u001b[31m14.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m212.5/212.5 kB\u001b[0m \u001b[31m23.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.3/134.3 kB\u001b[0m \u001b[31m16.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m75.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m236.8/236.8 kB\u001b[0m \u001b[31m27.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m126.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.5/114.5 kB\u001b[0m \u001b[31m12.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m30.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.6/149.6 kB\u001b[0m \u001b[31m19.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# Create dataset"
],
"metadata": {
"id": "rDDe5DJCy2r_"
}
},
{
"cell_type": "code",
"source": [
"!cat cnlps-caiccaic/train/in.tsv cnlps-caiccaic/dev-A/in.tsv > in.tsv\n",
"!cat cnlps-caiccaic/train/expected.tsv cnlps-caiccaic/dev-A/expected.tsv > expected.tsv"
],
"metadata": {
"id": "OxO5rSflnWkT"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import csv\n",
"from datasets import Dataset, DatasetDict\n",
"\n",
"with open('in.tsv', encoding='utf-8') as train_f_in, open('expected.tsv', encoding='utf-8') as train_f_exp:\n",
" train_list_in = list(csv.reader(train_f_in, delimiter='\\t'))\n",
" train_list_exp = train_f_exp.readlines()\n",
"\n",
"with open('cnlps-caiccaic/test-A/in.tsv', encoding='utf-8') as test_f_in:\n",
" test_list_in = list(csv.reader(test_f_in, delimiter='\\t'))\n",
"\n",
"train_data = Dataset.from_list([{'text': f'{in_[3]} language: {in_[1]}', 'intent': exp.strip().replace('}', ']').replace('{', '[').replace('\\t', '|')} for in_, exp in zip(train_list_in, train_list_exp)])\n",
"test_data = Dataset.from_list([{'text': f'{in_[3]} language: {in_[1]}', 'intent': ''} for in_ in test_list_in])\n",
"dataset = DatasetDict({'train': train_data, 'test': test_data})"
],
"metadata": {
"id": "mOM2JAw4npqM"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"dataset['train'][600]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "bYmEQ35Rvjr-",
"outputId": "23d6c786-d03a-4301-da8d-d50480af3888"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'text': \"don't alert me when an event in my calendar in location kenner begins language: en-US\",\n",
" 'intent': \"Calendar|NotNotifyOnEventInLocation|['location': 'kenner']\"}"
]
},
"metadata": {},
"execution_count": 6
}
]
},
{
"cell_type": "markdown",
"source": [
"# Define training components"
],
"metadata": {
"id": "GeSaTKE0y4_d"
}
},
{
"cell_type": "code",
"source": [
"from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n",
"\n",
"model_name = 'google/flan-t5-large'\n",
"tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
"model = AutoModelForSeq2SeqLM.from_pretrained(model_name, device_map='auto')"
],
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"id": "qt0MadQYlO_l",
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"fc721e11600248f692501fec9cd1c7c4",
"c2b55cb44a064726823537251c298111",
"2f5e8d2a03d84b619ab913a370cf0beb",
"137ca2d2ace545a08206f0422b45d44f",
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"504c68ff3b8a4f00b0fa24fd563f39e7",
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"outputId": "3f2e2a79-3a8b-4bc9-eb03-835b637738c3"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
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"text/plain": [
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],
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"version_major": 2,
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},
{
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],
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}
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"def preprocess_function(sample):\n",
" inputs = ['Create an annotation based on the following data. ' + item for item in sample['text']]\n",
" model_inputs = tokenizer(inputs, padding=True, truncation=True)\n",
" labels = tokenizer(text_target=sample['intent'], padding=True, truncation=True)\n",
" labels['input_ids'] = [[(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels['input_ids']]\n",
" model_inputs['labels'] = labels['input_ids']\n",
" return model_inputs\n",
"\n",
"tokenized_dataset = dataset.map(preprocess_function, batched=True, remove_columns=['text', 'intent'])"
],
"metadata": {
"id": "hcRqB5TaQmgJ"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from transformers import DataCollatorForSeq2Seq\n",
"\n",
"data_collator = DataCollatorForSeq2Seq(\n",
" tokenizer,\n",
" model=model\n",
")"
],
"metadata": {
"id": "YyDdFqpL09aA"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Training"
],
"metadata": {
"id": "unliUG2W1BDg"
}
},
{
"cell_type": "code",
"source": [
"from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments\n",
"\n",
"output_dir='flan-t5_large'\n",
"\n",
"training_args = Seq2SeqTrainingArguments(\n",
" output_dir=output_dir,\n",
"\t auto_find_batch_size=True,\n",
" num_train_epochs=1,\n",
" logging_dir=f'{output_dir}/logs',\n",
" save_steps=3000\n",
")\n",
"\n",
"trainer = Seq2SeqTrainer(\n",
" model=model,\n",
" args=training_args,\n",
" data_collator=data_collator,\n",
" train_dataset=tokenized_dataset['train']\n",
")\n",
"model.config.use_cache = False "
],
"metadata": {
"id": "m8xxtxKw1BIZ"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"trainer.train()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 952
},
"id": "aQS3QFMN1DD4",
"outputId": "8b7a521b-ded6-41fe-c73a-1d44b0579a88"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='2' max='6437' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [ 2/6437 : < :, Epoch 0.00/1]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Step</th>\n",
" <th>Training Loss</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='12874' max='12874' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [12874/12874 3:22:15, Epoch 1/1]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Step</th>\n",
" <th>Training Loss</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>500</td>\n",
" <td>0.348400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1000</td>\n",
" <td>0.147000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1500</td>\n",
" <td>0.096300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2000</td>\n",
" <td>0.077400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2500</td>\n",
" <td>0.070700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3000</td>\n",
" <td>0.053100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3500</td>\n",
" <td>0.051700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4000</td>\n",
" <td>0.046900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4500</td>\n",
" <td>0.040200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5000</td>\n",
" <td>0.039800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5500</td>\n",
" <td>0.038500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6000</td>\n",
" <td>0.037600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6500</td>\n",
" <td>0.040100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7000</td>\n",
" <td>0.040800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7500</td>\n",
" <td>0.038600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8000</td>\n",
" <td>0.036000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8500</td>\n",
" <td>0.035600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9000</td>\n",
" <td>0.040000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9500</td>\n",
" <td>0.038300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10000</td>\n",
" <td>0.034400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10500</td>\n",
" <td>0.038000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>11000</td>\n",
" <td>0.037300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>11500</td>\n",
" <td>0.035400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>12000</td>\n",
" <td>0.035500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>12500</td>\n",
" <td>0.035800</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"TrainOutput(global_step=12874, training_loss=0.06059208474804456, metrics={'train_runtime': 12136.5406, 'train_samples_per_second': 4.243, 'train_steps_per_second': 1.061, 'total_flos': 2.330193322605773e+16, 'train_loss': 0.06059208474804456, 'epoch': 1.0})"
]
},
"metadata": {},
"execution_count": 23
}
]
},
{
"cell_type": "markdown",
"source": [
"# Save the model"
],
"metadata": {
"id": "mDOXSmts1J-G"
}
},
{
"cell_type": "code",
"source": [
"trainer.model.save_pretrained('results_v2')\n",
"tokenizer.save_pretrained('results_v2')"
],
"metadata": {
"id": "RN9rhtHw1KC9"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!rm -r /content/drive/MyDrive/caiccaic; mkdir /content/drive/MyDrive/caiccaic\n",
"!cp -r results_v2 /content/drive/MyDrive/caiccaic"
],
"metadata": {
"id": "FPUZs3SZPQ4z"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# from google.colab import runtime\n",
"# runtime.unassign()"
],
"metadata": {
"id": "em0VFt0rz3uJ"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Inference"
],
"metadata": {
"id": "ntt7iUnd8Wyc"
}
},
{
"cell_type": "code",
"source": [
"!cp -r /content/drive/MyDrive/results_v2 ."
],
"metadata": {
"id": "muGPGGT5m4Wx"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n",
"import torch\n",
"\n",
"model = AutoModelForSeq2SeqLM.from_pretrained('results_v2', device_map={'':0})\n",
"tokenizer = AutoTokenizer.from_pretrained('results_v2')\n",
"model.eval()"
],
"metadata": {
"id": "hTshD-E_mv4p",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "30da8e10-c8fb-4d01-d558-07e8cb02d693"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"T5ForConditionalGeneration(\n",
" (shared): Embedding(32128, 1024)\n",
" (encoder): T5Stack(\n",
" (embed_tokens): Embedding(32128, 1024)\n",
" (block): ModuleList(\n",
" (0): T5Block(\n",
" (layer): ModuleList(\n",
" (0): T5LayerSelfAttention(\n",
" (SelfAttention): T5Attention(\n",
" (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (relative_attention_bias): Embedding(32, 16)\n",
" )\n",
" (layer_norm): T5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (1): T5LayerFF(\n",
" (DenseReluDense): T5DenseGatedActDense(\n",
" (wi_0): Linear(in_features=1024, out_features=2816, bias=False)\n",
" (wi_1): Linear(in_features=1024, out_features=2816, bias=False)\n",
" (wo): Linear(in_features=2816, out_features=1024, bias=False)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" (act): NewGELUActivation()\n",
" )\n",
" (layer_norm): T5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" (1-23): 23 x T5Block(\n",
" (layer): ModuleList(\n",
" (0): T5LayerSelfAttention(\n",
" (SelfAttention): T5Attention(\n",
" (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
" )\n",
" (layer_norm): T5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (1): T5LayerFF(\n",
" (DenseReluDense): T5DenseGatedActDense(\n",
" (wi_0): Linear(in_features=1024, out_features=2816, bias=False)\n",
" (wi_1): Linear(in_features=1024, out_features=2816, bias=False)\n",
" (wo): Linear(in_features=2816, out_features=1024, bias=False)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" (act): NewGELUActivation()\n",
" )\n",
" (layer_norm): T5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (final_layer_norm): T5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (decoder): T5Stack(\n",
" (embed_tokens): Embedding(32128, 1024)\n",
" (block): ModuleList(\n",
" (0): T5Block(\n",
" (layer): ModuleList(\n",
" (0): T5LayerSelfAttention(\n",
" (SelfAttention): T5Attention(\n",
" (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (relative_attention_bias): Embedding(32, 16)\n",
" )\n",
" (layer_norm): T5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (1): T5LayerCrossAttention(\n",
" (EncDecAttention): T5Attention(\n",
" (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
" )\n",
" (layer_norm): T5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (2): T5LayerFF(\n",
" (DenseReluDense): T5DenseGatedActDense(\n",
" (wi_0): Linear(in_features=1024, out_features=2816, bias=False)\n",
" (wi_1): Linear(in_features=1024, out_features=2816, bias=False)\n",
" (wo): Linear(in_features=2816, out_features=1024, bias=False)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" (act): NewGELUActivation()\n",
" )\n",
" (layer_norm): T5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" (1-23): 23 x T5Block(\n",
" (layer): ModuleList(\n",
" (0): T5LayerSelfAttention(\n",
" (SelfAttention): T5Attention(\n",
" (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
" )\n",
" (layer_norm): T5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (1): T5LayerCrossAttention(\n",
" (EncDecAttention): T5Attention(\n",
" (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
" (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
" )\n",
" (layer_norm): T5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (2): T5LayerFF(\n",
" (DenseReluDense): T5DenseGatedActDense(\n",
" (wi_0): Linear(in_features=1024, out_features=2816, bias=False)\n",
" (wi_1): Linear(in_features=1024, out_features=2816, bias=False)\n",
" (wo): Linear(in_features=2816, out_features=1024, bias=False)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" (act): NewGELUActivation()\n",
" )\n",
" (layer_norm): T5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (final_layer_norm): T5LayerNorm()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (lm_head): Linear(in_features=1024, out_features=32128, bias=False)\n",
")"
]
},
"metadata": {},
"execution_count": 9
}
]
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"from datasets import load_from_disk\n",
"from tqdm import tqdm\n",
"\n",
"def evaluate_peft_model(sample):\n",
" outputs = model.generate(input_ids=torch.tensor(sample['input_ids']).unsqueeze(0).cuda(), max_new_tokens=512) \n",
" prediction = tokenizer.decode(outputs[0].detach().cpu().numpy(), skip_special_tokens=True)\n",
" return prediction\n",
"\n",
"test_dataset = tokenized_dataset['test']\n",
"\n",
"predictions, references = [], []\n",
"i = 0\n",
"for sample in tqdm(test_dataset):\n",
" p = evaluate_peft_model(sample)\n",
" if i % 100 == 0:\n",
" print('\\n', p)\n",
" predictions.append(p)\n",
" i += 1"
],
"metadata": {
"id": "4t0Epw1x8YOG",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "a93e8654-603c-4353-bef6-04118302cf36"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"\r 0%| | 0/10358 [00:00<?, ?it/s]/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1255: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation)\n",
" warnings.warn(\n",
" 0%| | 1/10358 [00:03<9:53:16, 3.44s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Airconditioner|SetTemperatureOnDevice|['device_name': 'firmes stet']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
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]
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]
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" Contacts|ShowContactWithNumberAndWithEmail|['email': 'teresa.aguilera-peon@enron.com', 'phone_number': '011 48 193 424']\n"
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},
{
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" Email|SendEmailToAddressWithSubjectAndWithMessage|['message': 'for their championship this week','subject': 'fw: new erisa case against enron', 'to': 'bettine@kpmg.com']\n"
]
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" Gdrive|OpenFileWithName|['file_name': 'adoby's cony h it']\n"
]
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]
},
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},
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},
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{
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" Spotify|AddSongWithNameToPlaylistWithName|['playlist':'solonce patifar','song': 'o my plailest work']\n"
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},
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" Spotify|AddSongWithNameToPlaylistWithName|['playlist': 'i still see your shadows in my room','song': 'endemoniada']\n"
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" Spotify|PlaySongByArtist|['artist': 'terry hoax','song':'super fly']\n"
]
},
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},
{
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" Translate|TranslateTextFromLanguageToLanguageWithEngine|['src_lang_es':'spanish', 'text_es':'mircles', 'translator': 'deeple', 'trg_lang_de': 'german']\n"
]
},
{
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},
{
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]
},
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},
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]
},
{
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},
{
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"\n",
" Translate|TranslateTextFromLanguageToLanguage|['src_lang_it': 'italian', 'text_it':'so lotted', 'trg_lang_en': 'english']\n"
]
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},
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" Translate|TranslateTextFromLanguageToLanguage|['src_lang_de': 'german', 'text_de': 'bitine coal and mighty rump', 'trg_lang_pl': 'polish']\n"
]
},
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},
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" Translate|TranslateTextFromLanguageToLanguage|['src_lang_fr': 'french', 'text_fr': 'des légumes', 'trg_lang_de': 'german']\n"
]
},
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},
{
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]
},
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},
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]
},
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},
{
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" Websearch|SearchImagesWithTextOnEngineWithWidthAndWithHeightAndWithCondition|[]\n"
]
},
{
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},
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]
},
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},
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" Websearch|SearchImagesWithTextOnEngineWithWidthAndWithHeightAndWithCondition|['img_query': 'heisenberg']\n"
]
},
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},
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" Websearch|SearchImagesWithTextOnEngineWithWidthAndWithHeight|[]\n"
]
},
{
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},
{
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"\n",
" Websearch|SearchTextOnEngine|['txt_query': 'nhl scores']\n"
]
},
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},
{
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" Wikipedia|GoToElementNumber|[]\n"
]
},
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},
{
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"name": "stdout",
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"\n",
" Yelp|SearchByQueryInLocation|['location':'swan river', 'query': 'chicken parmesan']\n"
]
},
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},
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" Youtube|FindQuery|['query': 'troy lanez']\n"
]
},
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},
{
"output_type": "stream",
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"text": [
"\n",
" Calendar|AddEventWithName|['event_name': 'zubrzyca']\n"
]
},
{
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},
{
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"\n",
" Calendar|NotifyOnEventInLocation|['location': 'klubie lustra']\n"
]
},
{
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},
{
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"name": "stdout",
"text": [
"\n",
" Console|ConsoleEdit|['filename': 'p1_babych_pdf']\n"
]
},
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},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Contacts|EditContactWithNumber|['phone_number': '55 469 53 16']\n"
]
},
{
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]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Contacts|ShowContactWithNumberAndWithEmail|['email':'mojesz@onet.pl', 'phone_number': '+48 783 492 786']\n"
]
},
{
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]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Email|SendEmailToAddressWithSubject|['subject': 'przewraliwiany szcznie dorosa munda']\n"
]
},
{
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]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Email|ShowEmailFromSender|['sender_address': 'ludmia13277@onet.pl']\n"
]
},
{
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]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Facebook|PostPictureWithUrl|['picture_url': 'bit.ly/gruffly']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 41%|████ | 4201/10358 [2:22:16<1:57:30, 1.15s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Fitbit|NotifyOnWeight|['weight': '88 kg']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
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]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Fitbit|ShowStepsOnDate|['date': '18 czerwca']\n"
]
},
{
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]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Gdrive|ShowFilesWithSize|['file_size': '50 mb']\n"
]
},
{
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},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Instagram|ShowPictures|[]\n"
]
},
{
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},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Instagram|ShowPicturesWithLocation|['location': 'grenola']\n"
]
},
{
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]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" News|NotifyWhenPortalUpdatesInSection|['portal': 'wp.pl','section': 'gwiazdy']\n"
]
},
{
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"name": "stderr",
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},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Phone|CallEmergency|[]\n"
]
},
{
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]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Slack|CheckMessagesFromUser|['sender': 'j.winiewski']\n"
]
},
{
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]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Spotify|AddSongToPlaylistWithName|['playlist':'signed xoxo']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
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]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Spotify|OpenSpotify|[]\n"
]
},
{
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]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Spotify|PlaySongByArtist|['artist': 'organek','song':'sarajevo grade moj']\n"
]
},
{
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"name": "stderr",
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]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Translate|TranslateTextFromLanguage|['src_lang_en': 'angielskiego', 'text_en':'south']\n"
]
},
{
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]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Translate|TranslateTextFromLanguage|['src_lang_en': 'angielskiego', 'text_en': 'does this bus stop in busan']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
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]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Translate|TranslateTextFromLanguageToLanguage|['src_lang_en': 'angielskiego', 'text_en': 'does the room come with bedsheets', 'trg_lang_pl': 'polski']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
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]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Translate|TranslateTextFromLanguageToLanguageWithEngine|['src_lang_es': 'hiszpaskiego', 'text_es': 'la próxima semana', 'translator':'microsoft', 'trg_lang_it': 'woski']\n"
]
},
{
"output_type": "stream",
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]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Translate|TranslateTextFromLanguageToLanguageWithEngine|['src_lang_en': 'angielskiego', 'text_en': 'november', 'translator': 'google', 'trg_lang_it': 'woski']\n"
]
},
{
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]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Weather|SunriseInLocation|['location': 'róanka']\n"
]
},
{
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]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Websearch|SearchImagesWithTextOnEngineWithWidthAndWithHeight|['img_query': 'koptyjski zrozumiao']\n"
]
},
{
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"name": "stderr",
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},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Websearch|SearchImagesWithTextOnEngineWithWidthAndWithHeightAndWithCondition|['img_query': 'kruchuteki ucisk']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 59%|█████▉ | 6101/10358 [3:29:02<2:50:21, 2.40s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Websearch|SearchImagesWithTextOnEngineWithWidthAndWithHeightAndWithCondition|['img_query': 'kolorowanki wielkanocne']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 60%|█████▉ | 6201/10358 [3:32:31<1:43:03, 1.49s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Websearch|SearchText|['txt_query': 'trackim tuck tucko']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 61%|██████ | 6301/10358 [3:34:27<1:16:13, 1.13s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Yelp|OpenRestaurantsInLocation|['location': 'wrzeszcz dolny']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 62%|██████▏ | 6401/10358 [3:37:40<2:21:13, 2.14s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Yelp|SearchByReviewCountInLocation|['location': 'poplar branch','review_count': '40']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 63%|██████▎ | 6501/10358 [3:40:28<2:10:15, 2.03s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Youtube|FindQueryOnChannel|['channel_id': 'laughing stock', 'query': 'barier cardi']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 64%|██████▎ | 6601/10358 [3:44:00<2:36:07, 2.49s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Youtube|FindQueryOnChannel|['channel_id': 'hearthstone legend', 'query': 'kabarety 2019 najnowsze']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 65%|██████▍ | 6701/10358 [3:47:35<2:04:56, 2.05s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Youtube|FindQueryOnChannel|['channel_id':'resourceniche', 'query': 'polska tahiti']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 66%|██████▌ | 6801/10358 [3:50:27<50:32, 1.17it/s]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Youtube|NotifyOnNewFromFollowing|[]\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 67%|██████▋ | 6901/10358 [3:53:37<4:42:36, 4.91s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Email|SendEmailToAddressWithSubjectAndWithMessage|['message': 'durante los próximos tres das o tendrás el 50 de descuento que','subject': 'durante los próximos tres das o tendrás el 50 de descuento que', 'to': 'chaya y soh mayer ir']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 68%|██████▊ | 7001/10358 [3:57:03<1:56:39, 2.09s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Calendar|CheckCalendarOnDate|['date': 'l'ultima matricola']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 69%|██████▊ | 7101/10358 [3:59:17<1:27:44, 1.62s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Airconditioner|SetTemperatureToValue|['value': '24 grados']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
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]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Calendar|CheckCalendarOnDate|['date': 'el 1 de agosto']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
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]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Contacts|ShowContactWithEmail|['email': 'luis@gmail.com']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 71%|███████▏ | 7401/10358 [4:07:49<2:17:35, 2.79s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Contacts|ShowContactWithNumberAndWithEmail|['email': 'rupert.yanni@apx.com', 'phone_number': '447 349 366']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 72%|███████▏ | 7501/10358 [4:11:31<39:56, 1.19it/s]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Email|SendEmail|[]\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 73%|███████▎ | 7601/10358 [4:14:15<1:51:32, 2.43s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Email|SendEmailToAddressWithSubject|['subject': 'fwd more than their fair share', 'to': 'eugenio']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 74%|███████▍ | 7701/10358 [4:19:24<3:01:40, 4.10s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Email|SendEmailToAddressWithSubjectAndWithMessage|['message': 'ahora viene mi chico','subject': 'Te gustara vivir la mejor experiencia de tu vida?', 'to': 'kena']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 75%|███████▌ | 7801/10358 [4:26:21<2:56:35, 4.14s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Email|SendEmailToAddressWithSubjectAndWithMessage|['message': 'que poquito que falta para cataratas','subject':'southwest into california', 'to': 'chuy']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 76%|███████▋ | 7901/10358 [4:33:16<2:56:17, 4.31s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Email|SendEmailToAddressWithSubjectAndWithMessage|['message': 'rt si te gusta trollear','subject': 'here is the complete email', 'to':'montse mendizabal']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 77%|███████▋ | 8001/10358 [4:40:05<2:35:14, 3.95s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Email|SendEmailToAddressWithSubjectAndWithMessage|['message': 'lo acabo y me voy a dormir','subject': 'ceo letter', 'to':'salva@hotmail.es']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
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]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Email|SendEmailToAddressWithSubjectAndWithMessage|['message': 'hoy a tomar tere con milaa','subject': 'have played here a few times', 'to': 'jenelle@hotmail.com']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 79%|███████▉ | 8201/10358 [4:53:58<2:35:31, 4.33s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Email|SendEmailToAddressWithSubjectAndWithMessage|['message': 'cada vez mas viejas lisitas','subject': 'the new dublin campus next year', 'to': 'roselyn@proton.com']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
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]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Email|ShowEmailWithSubject|['subject':'summarizing all media coverage']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 81%|████████ | 8401/10358 [5:01:03<1:53:03, 3.47s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Facebook|PostPictureWithUrlAndWithCaption|['caption': 'le pregunté a mi perro cómo me veo por eso lo amo tanto', 'picture_url': 'bit.ly/NIAGyW3']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 82%|████████▏ | 8501/10358 [5:03:38<37:21, 1.21s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Facebook|PostAlbumOfTypeByArtist|['album': 'vacaciones en argentina 2019']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 83%|████████▎ | 8601/10358 [5:05:33<42:13, 1.44s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Instagram|ShowPicturesWithFilter|['filter': 'gingham']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 84%|████████▍ | 8701/10358 [5:07:50<55:57, 2.03s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" News|NotifyWhenPortalUpdatesInSection|['portal': 'la voz de galicia','section': 'deporte']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 85%|████████▍ | 8801/10358 [5:11:03<49:30, 1.91s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" News|ShowNewsFromSection|['portal': 'el pas','section': 'deporte']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 86%|████████▌ | 8901/10358 [5:14:16<51:23, 2.12s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Slack|SendMessageToChannel|['channel': 'citibank','message': 'excelente fin de semana']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 87%|████████▋ | 9001/10358 [5:16:36<52:36, 2.33s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Spotify|AddSongWithNameToPlaylistWithName|['playlist':'soulfull disco','song': 'how i see it']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 88%|████████▊ | 9101/10358 [5:18:57<20:56, 1.00it/s]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Spotify|PlaySong|['song':'silent edge']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 89%|████████▉ | 9201/10358 [5:21:15<40:07, 2.08s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Spotify|PlaySongByArtist|['artist': 'jonathan larson','song':'song x']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 90%|████████▉ | 9301/10358 [5:24:38<34:06, 1.94s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Spotify|PlaySongByArtist|['artist': 'hard stance','song': 'on my toes']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 91%|█████████ | 9401/10358 [5:28:07<49:29, 3.10s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Translate|TranslateTextFromLanguageToLanguage|['src_lang': 'fránces', 'text':'marrón', 'trg_lang': 'espaol']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 92%|█████████▏| 9501/10358 [5:33:07<21:48, 1.53s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Calendar|CheckCalendarOnDate|['date': 'fase de la luna para croacia']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 93%|█████████▎| 9601/10358 [5:35:48<24:26, 1.94s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Websearch|SearchImagesWithTextOnEngineWithWidthAndWithHeight|['img_query':'mechas balayage']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 94%|█████████▎| 9701/10358 [5:39:09<21:16, 1.94s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Websearch|SearchImagesWithTextOnEngineWithWidthAndWithHeight|['img_query': 'la roca village']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 95%|█████████▍| 9801/10358 [5:42:42<20:17, 2.19s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Websearch|SearchImagesWithTextOnEngineWithWidthAndWithHeightAndWithCondition|['img_query': 'calendario febrero 2020 para imprimir']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 96%|█████████▌| 9901/10358 [5:46:23<15:14, 2.00s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Websearch|SearchImagesWithTextOnEngineWithWidthAndWithHeightAndWithCondition|['img_query': 'formacion']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 97%|█████████▋| 10001/10358 [5:50:01<12:44, 2.14s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Websearch|SearchImagesWithTextOnEngineWithWidthAndWithHeightAndWithCondition|['img_query':'media melena']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 98%|█████████▊| 10101/10358 [5:53:41<10:01, 2.34s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Websearch|SearchImagesWithTextOnEngineWithWidthAndWithHeightAndWithCondition|['img_query':'memes graciosos']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 98%|█████████▊| 10201/10358 [5:57:18<05:42, 2.18s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Websearch|SearchImagesWithTextOnEngineWithWidthAndWithHeightAndWithCondition|['img_query': 'rita maestre']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 99%|█████████▉| 10301/10358 [6:00:14<01:35, 1.67s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Yelp|SearchByQueryInLocation|['location': 'aulesti', 'query': 'cerveceras artesanales y pubs']\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 10358/10358 [6:01:25<00:00, 2.09s/it]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# format predictions to target shape\n",
"predictions = [prediction.replace('|', '\\t', 3).replace('[', '{').replace(']', '}').strip() for prediction in predictions]"
],
"metadata": {
"id": "DIUEgt2hnovi"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"with open('/content/cnlps-caiccaic/test-A/out_v2.tsv', 'w', encoding='utf-8') as out:\n",
" for pred in predictions:\n",
" out.write(pred + '\\n')"
],
"metadata": {
"id": "LnPDPN0OjgLA"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import locale\n",
"locale.getpreferredencoding = lambda: \"UTF-8\""
],
"metadata": {
"id": "msQz6KlHKO30"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!cp /content/cnlps-caiccaic/test-A/out_v2.tsv /content/drive/MyDrive/"
],
"metadata": {
"id": "sjP3d1sus2ec"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from google.colab import runtime\n",
"runtime.unassign()"
],
"metadata": {
"id": "d56gzuPA1SdY"
},
"execution_count": null,
"outputs": []
}
]
}
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