wehub-resource-sync
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634 lines
22 KiB
Plaintext
Executable File
634 lines
22 KiB
Plaintext
Executable File
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5f93b7d1",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-05-30T08:37:58.711225Z",
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"start_time": "2023-05-30T08:37:56.881307Z"
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}
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},
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"outputs": [],
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"source": "import os\n\nimport torch\nfrom transformers import AutoModelForSeq2SeqLM, AutoTokenizer, default_data_collator, get_linear_schedule_with_warmup\nfrom peft import get_peft_model, PromptTuningConfig, TaskType, PromptTuningInit\nfrom torch.utils.data import DataLoader\nfrom tqdm import tqdm\nfrom datasets import load_dataset\n\nos.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n\ndevice = torch.accelerator.current_accelerator().type if hasattr(torch, \"accelerator\") else \"cuda\"\nmodel_name_or_path = \"t5-large\"\ntokenizer_name_or_path = \"t5-large\"\n\ntext_column = \"text\"\nlabel_column = \"text_label\"\nmax_length = 128\nlr = 1\nnum_epochs = 5\nbatch_size = 8"
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "8d0850ac",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-05-30T08:38:12.413984Z",
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"start_time": "2023-05-30T08:38:04.601042Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"trainable params: 40,960 || all params: 737,709,056 || trainable%: 0.0056\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"PeftModelForSeq2SeqLM(\n",
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" (base_model): T5ForConditionalGeneration(\n",
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" (shared): Embedding(32128, 1024)\n",
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" (encoder): T5Stack(\n",
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" (embed_tokens): Embedding(32128, 1024)\n",
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" (block): ModuleList(\n",
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" (0): T5Block(\n",
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" (layer): ModuleList(\n",
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" (0): T5LayerSelfAttention(\n",
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" (SelfAttention): T5Attention(\n",
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" (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
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" (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
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" (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
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" (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
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" (relative_attention_bias): Embedding(32, 16)\n",
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" )\n",
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" (layer_norm): T5LayerNorm()\n",
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" (dropout): Dropout(p=0.1, inplace=False)\n",
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" )\n",
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" (1): T5LayerFF(\n",
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" (DenseReluDense): T5DenseActDense(\n",
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" (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
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" (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
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" (dropout): Dropout(p=0.1, inplace=False)\n",
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" (act): ReLU()\n",
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" )\n",
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" (layer_norm): T5LayerNorm()\n",
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" (dropout): Dropout(p=0.1, inplace=False)\n",
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" )\n",
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" )\n",
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" )\n",
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" (1-23): 23 x T5Block(\n",
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" (layer): ModuleList(\n",
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" (0): T5LayerSelfAttention(\n",
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" (SelfAttention): T5Attention(\n",
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" (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
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" (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
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" (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
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" (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
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" )\n",
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" (layer_norm): T5LayerNorm()\n",
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" (dropout): Dropout(p=0.1, inplace=False)\n",
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" )\n",
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" (1): T5LayerFF(\n",
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" (DenseReluDense): T5DenseActDense(\n",
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" (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
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" (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
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" (dropout): Dropout(p=0.1, inplace=False)\n",
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" (act): ReLU()\n",
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" )\n",
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" (layer_norm): T5LayerNorm()\n",
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" (dropout): Dropout(p=0.1, inplace=False)\n",
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" )\n",
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" )\n",
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" )\n",
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" )\n",
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" (final_layer_norm): T5LayerNorm()\n",
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" (dropout): Dropout(p=0.1, inplace=False)\n",
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" )\n",
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" (decoder): T5Stack(\n",
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" (embed_tokens): Embedding(32128, 1024)\n",
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" (block): ModuleList(\n",
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" (0): T5Block(\n",
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" (layer): ModuleList(\n",
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" (0): T5LayerSelfAttention(\n",
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" (SelfAttention): T5Attention(\n",
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" (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
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" (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
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" (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
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" (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
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" (relative_attention_bias): Embedding(32, 16)\n",
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" )\n",
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" (layer_norm): T5LayerNorm()\n",
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" (dropout): Dropout(p=0.1, inplace=False)\n",
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" )\n",
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" (1): T5LayerCrossAttention(\n",
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" (EncDecAttention): T5Attention(\n",
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" (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
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" (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
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" (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
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" (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
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" )\n",
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" (layer_norm): T5LayerNorm()\n",
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" (dropout): Dropout(p=0.1, inplace=False)\n",
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" )\n",
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" (2): T5LayerFF(\n",
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" (DenseReluDense): T5DenseActDense(\n",
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" (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
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" (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
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" (dropout): Dropout(p=0.1, inplace=False)\n",
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" (act): ReLU()\n",
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" )\n",
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" (layer_norm): T5LayerNorm()\n",
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" (dropout): Dropout(p=0.1, inplace=False)\n",
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" )\n",
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" )\n",
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" )\n",
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" (1-23): 23 x T5Block(\n",
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" (layer): ModuleList(\n",
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" (0): T5LayerSelfAttention(\n",
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" (SelfAttention): T5Attention(\n",
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" (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
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" (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
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" (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
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" (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
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" )\n",
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" (layer_norm): T5LayerNorm()\n",
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" (dropout): Dropout(p=0.1, inplace=False)\n",
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" )\n",
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" (1): T5LayerCrossAttention(\n",
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" (EncDecAttention): T5Attention(\n",
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" (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
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" (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
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" (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
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" (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
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" )\n",
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" (layer_norm): T5LayerNorm()\n",
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" (dropout): Dropout(p=0.1, inplace=False)\n",
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" )\n",
|
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" (2): T5LayerFF(\n",
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" (DenseReluDense): T5DenseActDense(\n",
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" (wi): Linear(in_features=1024, out_features=4096, bias=False)\n",
|
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" (wo): Linear(in_features=4096, out_features=1024, bias=False)\n",
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" (dropout): Dropout(p=0.1, inplace=False)\n",
|
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" (act): ReLU()\n",
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" )\n",
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" (layer_norm): T5LayerNorm()\n",
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" (dropout): Dropout(p=0.1, inplace=False)\n",
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" )\n",
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" )\n",
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" )\n",
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" )\n",
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" (final_layer_norm): T5LayerNorm()\n",
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" (dropout): Dropout(p=0.1, inplace=False)\n",
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" )\n",
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" (lm_head): Linear(in_features=1024, out_features=32128, bias=False)\n",
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" )\n",
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" (prompt_encoder): ModuleDict(\n",
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" (default): PromptEmbedding(\n",
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" (embedding): Embedding(40, 1024)\n",
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" )\n",
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" )\n",
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" (word_embeddings): Embedding(32128, 1024)\n",
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")"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# creating model\n",
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"peft_config = PromptTuningConfig(\n",
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" task_type=TaskType.SEQ_2_SEQ_LM,\n",
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" prompt_tuning_init=PromptTuningInit.TEXT,\n",
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" num_virtual_tokens=20,\n",
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" prompt_tuning_init_text=\"What is the sentiment of this article?\\n\",\n",
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" inference_mode=False,\n",
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" tokenizer_name_or_path=model_name_or_path,\n",
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")\n",
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"\n",
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"model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path)\n",
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"model = get_peft_model(model, peft_config)\n",
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"model.print_trainable_parameters()\n",
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"model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "4ee2babf",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-05-30T08:38:18.759143Z",
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"start_time": "2023-05-30T08:38:17.881621Z"
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}
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Using the latest cached version of the dataset since financial_phrasebank couldn't be found on the Hugging Face Hub\n",
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"Found the latest cached dataset configuration 'sentences_allagree' at /root/.cache/huggingface/datasets/financial_phrasebank/sentences_allagree/1.0.0/550bde12e6c30e2674da973a55f57edde5181d53f5a5a34c1531c53f93b7e141 (last modified on Thu Jul 31 06:37:38 2025).\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "2b64258700bd40548ddcd626f3920c9a",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Map: 0%| | 0/2037 [00:00<?, ? examples/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "d95de9b1dc3d417da35118c14d02b986",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Map: 0%| | 0/227 [00:00<?, ? examples/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"{'sentence': 'The 2500-passenger ferry will have dimensions of 185 m length overall , 170 m length between perpendiculars , 27.70 m breadth and 6.55 m design draught .',\n",
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" 'label': 1,\n",
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" 'text_label': 'neutral'}"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# loading dataset\n",
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"dataset = load_dataset(\"zeroshot/twitter-financial-news-sentiment\")\n",
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"dataset = dataset[\"train\"].train_test_split(test_size=0.1)\n",
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"dataset[\"validation\"] = dataset[\"test\"]\n",
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"del dataset[\"test\"]\n",
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"\n",
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"if hasattr(dataset[\"train\"].features[\"label\"], \"names\"):\n",
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" classes = dataset[\"train\"].features[\"label\"].names\n",
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"else:\n",
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" classes = [\"Bearish\", \"Bullish\", \"Neutral\"]\n",
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"dataset = dataset.map(\n",
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" lambda x: {\"text_label\": [classes[label] for label in x[\"label\"]]},\n",
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" batched=True,\n",
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" num_proc=1,\n",
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")\n",
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"\n",
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"dataset[\"train\"][0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "adf9608c",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-05-30T08:38:21.132266Z",
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"start_time": "2023-05-30T08:38:20.340722Z"
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}
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},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "a78b4b1a041546248b8e6703eaec0969",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Running tokenizer on dataset: 0%| | 0/2037 [00:00<?, ? examples/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "bbe2962a25144b5488c297e186df824f",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Running tokenizer on dataset: 0%| | 0/227 [00:00<?, ? examples/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
|
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"# data preprocessing\n",
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"tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)\n",
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"target_max_length = max([len(tokenizer(class_label)[\"input_ids\"]) for class_label in classes])\n",
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"\n",
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"\n",
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"def preprocess_function(examples):\n",
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" inputs = examples[text_column]\n",
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" targets = examples[label_column]\n",
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" model_inputs = tokenizer(inputs, max_length=max_length, padding=\"max_length\", truncation=True, return_tensors=\"pt\")\n",
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" labels = tokenizer(\n",
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" targets, max_length=target_max_length, padding=\"max_length\", truncation=True, return_tensors=\"pt\"\n",
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" )\n",
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" labels = labels[\"input_ids\"]\n",
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" labels[labels == tokenizer.pad_token_id] = -100\n",
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" model_inputs[\"labels\"] = labels\n",
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" return model_inputs\n",
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"\n",
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"\n",
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"processed_datasets = dataset.map(\n",
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" preprocess_function,\n",
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" batched=True,\n",
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" num_proc=1,\n",
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" remove_columns=dataset[\"train\"].column_names,\n",
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" load_from_cache_file=False,\n",
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" desc=\"Running tokenizer on dataset\",\n",
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")\n",
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"\n",
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"train_dataset = processed_datasets[\"train\"]\n",
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"eval_dataset = processed_datasets[\"validation\"]\n",
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"\n",
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"train_dataloader = DataLoader(\n",
|
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" train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True\n",
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")\n",
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"eval_dataloader = DataLoader(eval_dataset, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True)"
|
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]
|
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},
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{
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"cell_type": "code",
|
|
"execution_count": 5,
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|
"id": "f733a3c6",
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|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2023-05-30T08:38:22.907922Z",
|
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"start_time": "2023-05-30T08:38:22.901057Z"
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}
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},
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"outputs": [],
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"source": [
|
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"# optimizer and lr scheduler\n",
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"optimizer = torch.optim.AdamW(model.parameters(), lr=lr)\n",
|
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"lr_scheduler = get_linear_schedule_with_warmup(\n",
|
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" optimizer=optimizer,\n",
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" num_warmup_steps=0,\n",
|
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" num_training_steps=(len(train_dataloader) * num_epochs),\n",
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")"
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]
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},
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{
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"cell_type": "code",
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|
"execution_count": null,
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|
"id": "6b3a4090",
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|
"metadata": {
|
|
"ExecuteTime": {
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|
"end_time": "2023-05-30T08:42:29.409070Z",
|
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"start_time": "2023-05-30T08:38:50.102263Z"
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}
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},
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"outputs": [],
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"source": [
|
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"# training and evaluation\n",
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"model = model.to(device)\n",
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"\n",
|
|
"for epoch in range(num_epochs):\n",
|
|
" model.train()\n",
|
|
" total_loss = 0\n",
|
|
" for step, batch in enumerate(tqdm(train_dataloader)):\n",
|
|
" batch = {k: v.to(device) for k, v in batch.items()}\n",
|
|
" outputs = model(**batch)\n",
|
|
" loss = outputs.loss\n",
|
|
" total_loss += loss.detach().float()\n",
|
|
" loss.backward()\n",
|
|
" optimizer.step()\n",
|
|
" lr_scheduler.step()\n",
|
|
" optimizer.zero_grad()\n",
|
|
"\n",
|
|
" model.eval()\n",
|
|
" eval_loss = 0\n",
|
|
" eval_preds = []\n",
|
|
" for step, batch in enumerate(tqdm(eval_dataloader)):\n",
|
|
" batch = {k: v.to(device) for k, v in batch.items()}\n",
|
|
" with torch.no_grad():\n",
|
|
" outputs = model(**batch)\n",
|
|
" loss = outputs.loss\n",
|
|
" eval_loss += loss.detach().float()\n",
|
|
" eval_preds.extend(\n",
|
|
" tokenizer.batch_decode(torch.argmax(outputs.logits, -1).detach().cpu().numpy(), skip_special_tokens=True)\n",
|
|
" )\n",
|
|
"\n",
|
|
" eval_epoch_loss = eval_loss / len(eval_dataloader)\n",
|
|
" eval_ppl = torch.exp(eval_epoch_loss)\n",
|
|
" train_epoch_loss = total_loss / len(train_dataloader)\n",
|
|
" train_ppl = torch.exp(train_epoch_loss)\n",
|
|
" print(f\"{epoch=}: {train_ppl=} {train_epoch_loss=} {eval_ppl=} {eval_epoch_loss=}\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"id": "6cafa67b",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2023-05-30T08:42:42.844671Z",
|
|
"start_time": "2023-05-30T08:42:42.840447Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"accuracy=85.46255506607929 % on the evaluation dataset\n",
|
|
"eval_preds[:10]=['neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'positive', 'neutral', 'negative', 'neutral', 'positive']\n",
|
|
"dataset['validation']['text_label'][:10]=['neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'positive', 'neutral', 'negative', 'positive', 'neutral']\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# print accuracy\n",
|
|
"correct = 0\n",
|
|
"total = 0\n",
|
|
"for pred, true in zip(eval_preds, dataset[\"validation\"][\"text_label\"]):\n",
|
|
" if pred.strip() == true.strip():\n",
|
|
" correct += 1\n",
|
|
" total += 1\n",
|
|
"accuracy = correct / total * 100\n",
|
|
"print(f\"{accuracy=} % on the evaluation dataset\")\n",
|
|
"print(f\"{eval_preds[:10]=}\")\n",
|
|
"print(f\"{dataset['validation']['text_label'][:10]=}\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"id": "a8de6005",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2023-05-30T08:42:45.752765Z",
|
|
"start_time": "2023-05-30T08:42:45.742397Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# saving model\n",
|
|
"peft_model_id = f\"{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}\"\n",
|
|
"model.save_pretrained(peft_model_id)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "bd20cd4c",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2023-05-30T08:42:47.660873Z",
|
|
"start_time": "2023-05-30T08:42:47.488293Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"164K\tt5-large_PROMPT_TUNING_SEQ_2_SEQ_LM/adapter_model.safetensors\r\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"ckpt = f\"{peft_model_id}/adapter_model.safetensors\"\n",
|
|
"!du -h $ckpt"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"id": "76c2fc29",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2023-05-30T08:42:56.721990Z",
|
|
"start_time": "2023-05-30T08:42:49.060700Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"from peft import PeftModel, PeftConfig\n",
|
|
"\n",
|
|
"peft_model_id = f\"{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}\"\n",
|
|
"\n",
|
|
"config = PeftConfig.from_pretrained(peft_model_id)\n",
|
|
"model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path)\n",
|
|
"model = PeftModel.from_pretrained(model, peft_model_id)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"id": "d997f1cc",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2023-05-30T08:42:59.600916Z",
|
|
"start_time": "2023-05-30T08:42:58.961468Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Danske Bank is Denmark 's largest bank with 3.5 million customers .\n",
|
|
"tensor([[ 3039, 1050, 1925, 19, 18001, 3, 31, 7, 2015, 2137,\n",
|
|
" 28, 3, 9285, 770, 722, 3, 5, 1]])\n",
|
|
"tensor([[ 0, 7163, 1]])\n",
|
|
"['neutral']\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"model.eval()\n",
|
|
"i = 107\n",
|
|
"input_ids = tokenizer(dataset[\"validation\"][text_column][i], return_tensors=\"pt\").input_ids\n",
|
|
"print(dataset[\"validation\"][text_column][i])\n",
|
|
"print(input_ids)\n",
|
|
"\n",
|
|
"with torch.no_grad():\n",
|
|
" outputs = model.generate(input_ids=input_ids, max_new_tokens=10)\n",
|
|
" print(outputs)\n",
|
|
" print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"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": "3.11.13"
|
|
},
|
|
"toc": {
|
|
"base_numbering": 1,
|
|
"nav_menu": {},
|
|
"number_sections": true,
|
|
"sideBar": true,
|
|
"skip_h1_title": false,
|
|
"title_cell": "Table of Contents",
|
|
"title_sidebar": "Contents",
|
|
"toc_cell": false,
|
|
"toc_position": {},
|
|
"toc_section_display": true,
|
|
"toc_window_display": false
|
|
},
|
|
"varInspector": {
|
|
"cols": {
|
|
"lenName": 16,
|
|
"lenType": 16,
|
|
"lenVar": 40
|
|
},
|
|
"kernels_config": {
|
|
"python": {
|
|
"delete_cmd_postfix": "",
|
|
"delete_cmd_prefix": "del ",
|
|
"library": "var_list.py",
|
|
"varRefreshCmd": "print(var_dic_list())"
|
|
},
|
|
"r": {
|
|
"delete_cmd_postfix": ") ",
|
|
"delete_cmd_prefix": "rm(",
|
|
"library": "var_list.r",
|
|
"varRefreshCmd": "cat(var_dic_list()) "
|
|
}
|
|
},
|
|
"types_to_exclude": [
|
|
"module",
|
|
"function",
|
|
"builtin_function_or_method",
|
|
"instance",
|
|
"_Feature"
|
|
],
|
|
"window_display": false
|
|
},
|
|
"vscode": {
|
|
"interpreter": {
|
|
"hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
|
|
}
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
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}
|