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File size: 59,514 Bytes
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"id": "3ece795d",
"metadata": {
"cellId": "icbn5fcdkdjmv2xo6f1uym"
},
"outputs": [],
"source": [
"#!g1.1\n",
"from sklearn.preprocessing import LabelEncoder\n",
"import transformers\n",
"import torch\n",
"import nltk\n",
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "2383e35c",
"metadata": {
"cellId": "r7277d47zkhjj04zr4od8g"
},
"outputs": [
{
"data": {
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"model_id": "a3fbc0c0072b4198bb84d870b39a6c74",
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"HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1202.0, style=ProgressStyle(descriptionโฆ"
]
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"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using custom data configuration default\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading and preparing dataset json/default-71bc0cd49f840871 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /tmp/xdg_cache/huggingface/datasets/json/default-71bc0cd49f840871/0.0.0/70d89ed4db1394f028c651589fcab6d6b28dddcabbe39d3b21b4d41f9a708514...\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
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"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f3df0e8c4d2a48968429ac5320020316",
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"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "71a715a43bcd4f4a859c247b1f375e51",
"version_major": 2,
"version_minor": 0
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"text/plain": [
"HBox(children=(FloatProgress(value=1.0, bar_style='info', layout=Layout(width='20px'), max=1.0), HTML(value=''โฆ"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset json downloaded and prepared to /tmp/xdg_cache/huggingface/datasets/json/default-71bc0cd49f840871/0.0.0/70d89ed4db1394f028c651589fcab6d6b28dddcabbe39d3b21b4d41f9a708514. Subsequent calls will reuse this data.\n"
]
}
],
"source": [
"#!g1.1\n",
"from datasets import load_dataset\n",
"\n",
"dataset_train_test_val = load_dataset('json', \n",
" data_files={'train': 'train_dataset.json', 'test': 'test_dataset.json', 'val': 'val_dataset.json'})"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "5affcf2d",
"metadata": {
"cellId": "d3dqrbyaerahlxtoqhusl"
},
"outputs": [
{
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['labels', 'input_ids', 'attention_mask'],\n",
" num_rows: 44928\n",
" })\n",
" test: Dataset({\n",
" features: ['labels', 'input_ids', 'attention_mask'],\n",
" num_rows: 11981\n",
" })\n",
" val: Dataset({\n",
" features: ['labels', 'input_ids', 'attention_mask'],\n",
" num_rows: 14976\n",
" })\n",
"})"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#!g1.1\n",
"dataset_train_test_val"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "1a1956c6",
"metadata": {
"cellId": "iv6a51fd9tlbrs4he3kizo"
},
"outputs": [],
"source": [
"#!g1.1\n",
"train_dataset = dataset_train_test_val['train']\n",
"val_dataset = dataset_train_test_val['val']\n",
"test_dataset = dataset_train_test_val['test']"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "c161630b",
"metadata": {
"cellId": "t9fridyqfq20q78rkgitt"
},
"outputs": [],
"source": [
"#!g1.1\n",
"train_dataset.set_format(\"torch\")\n",
"val_dataset.set_format(\"torch\")\n",
"test_dataset.set_format(\"torch\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "7ee3ce1c",
"metadata": {
"cellId": "1y1jaan8t8gjs3masmvulu"
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
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"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"#!g1.1\n",
"from datasets import load_metric\n",
"\n",
"metric = load_metric(\"accuracy\")\n",
"def compute_metrics(eval_pred):\n",
" logits, labels = eval_pred\n",
" predictions = np.argmax(logits, axis=-1)\n",
" return metric.compute(predictions=predictions, references=labels)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "c5d12dc8",
"metadata": {
"cellId": "6eds6is9lek1hcs87cizgy"
},
"outputs": [
{
"data": {
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"model_id": "4de02bce2bd448efa4d6e7c1e02c427a",
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},
"metadata": {},
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},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e8f550b59f4b418094cbcb1d13c5dd97",
"version_major": 2,
"version_minor": 0
},
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"HBox(children=(FloatProgress(value=0.0, description='Downloading', max=267967963.0, style=ProgressStyle(descriโฆ"
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},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_layer_norm.weight', 'vocab_projector.weight', 'vocab_projector.bias', 'vocab_transform.bias', 'vocab_layer_norm.bias', 'vocab_transform.weight']\n",
"- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
"- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
"Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.weight', 'pre_classifier.weight', 'classifier.bias', 'pre_classifier.bias']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
}
],
"source": [
"#!g1.1\n",
"from transformers import Trainer, TrainingArguments, AutoModelForSequenceClassification\n",
"\n",
"device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
"\n",
"model = AutoModelForSequenceClassification.from_pretrained(\"distilbert-base-uncased\", num_labels=8)\n",
"model = model.to(device)\n",
"\n",
"trainer = Trainer(\n",
" model=model, \n",
" train_dataset=train_dataset, \n",
" eval_dataset=val_dataset,\n",
" compute_metrics=compute_metrics,\n",
" args=TrainingArguments(\n",
" output_dir=\"./my_saved_model\", overwrite_output_dir=True,\n",
" num_train_epochs=4, per_device_train_batch_size=32,\n",
" save_steps=10000, save_total_limit=2),\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "59b4c995",
"metadata": {
"cellId": "enykeyqh04h85cnkvsnyvr"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"***** Running training *****\n",
" Num examples = 44928\n",
" Num Epochs = 4\n",
" Instantaneous batch size per device = 32\n",
" Total train batch size (w. parallel, distributed & accumulation) = 32\n",
" Gradient Accumulation steps = 1\n",
" Total optimization steps = 5616\n"
]
},
{
"data": {
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"\n",
" <div>\n",
" \n",
" <progress value='5616' max='5616' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [5616/5616 53:29, Epoch 4/4]\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>0.068200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1000</td>\n",
" <td>0.065100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1500</td>\n",
" <td>0.069500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2000</td>\n",
" <td>0.064600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2500</td>\n",
" <td>0.070400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3000</td>\n",
" <td>0.069800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3500</td>\n",
" <td>0.066200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4000</td>\n",
" <td>0.070000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4500</td>\n",
" <td>0.060200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5000</td>\n",
" <td>0.064800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5500</td>\n",
" <td>0.072600</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
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"metadata": {},
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{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"\n",
"Training completed. Do not forget to share your model on huggingface.co/models =)\n",
"\n",
"\n"
]
},
{
"data": {
"text/plain": [
"TrainOutput(global_step=5616, training_loss=0.06720576955382301, metrics={'train_runtime': 3209.8809, 'train_samples_per_second': 55.987, 'train_steps_per_second': 1.75, 'total_flos': 2.380852842253517e+16, 'train_loss': 0.06720576955382301, 'epoch': 4.0})"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/kernel/lib/python3.8/site-packages/ml_kernel/kernel.py:872: UserWarning: The following variables cannot be serialized: trainer\n",
" warnings.warn(message)\n"
]
}
],
"source": [
"#!g1.1\n",
"trainer.train()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "930c6dfc",
"metadata": {
"cellId": "sqt27hulgn6e3st0pa1jx"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"***** Running Evaluation *****\n",
" Num examples = 14976\n",
" Batch size = 8\n"
]
},
{
"data": {
"text/plain": [
"{'eval_loss': 0.5749701261520386,\n",
" 'eval_accuracy': 0.8629807692307693,\n",
" 'eval_runtime': 122.7376,\n",
" 'eval_samples_per_second': 122.016,\n",
" 'eval_steps_per_second': 15.252,\n",
" 'epoch': 4.0}"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/kernel/lib/python3.8/site-packages/ml_kernel/kernel.py:872: UserWarning: The following variables cannot be serialized: trainer\n",
" warnings.warn(message)\n"
]
}
],
"source": [
"#!g1.1\n",
"trainer.evaluate()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "4ef33ef9",
"metadata": {
"cellId": "jizblzfc2jjq76b0kfppy"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"loading configuration file https://huggingface.co/distilbert-base-uncased/resolve/main/config.json from cache at /tmp/xdg_cache/huggingface/transformers/23454919702d26495337f3da04d1655c7ee010d5ec9d77bdb9e399e00302c0a1.91b885ab15d631bf9cee9dc9d25ece0afd932f2f5130eba28f2055b2220c0333\n",
"Model config DistilBertConfig {\n",
" \"_name_or_path\": \"distilbert-base-uncased\",\n",
" \"activation\": \"gelu\",\n",
" \"architectures\": [\n",
" \"DistilBertForMaskedLM\"\n",
" ],\n",
" \"attention_dropout\": 0.1,\n",
" \"dim\": 768,\n",
" \"dropout\": 0.1,\n",
" \"hidden_dim\": 3072,\n",
" \"initializer_range\": 0.02,\n",
" \"max_position_embeddings\": 512,\n",
" \"model_type\": \"distilbert\",\n",
" \"n_heads\": 12,\n",
" \"n_layers\": 6,\n",
" \"pad_token_id\": 0,\n",
" \"qa_dropout\": 0.1,\n",
" \"seq_classif_dropout\": 0.2,\n",
" \"sinusoidal_pos_embds\": false,\n",
" \"tie_weights_\": true,\n",
" \"transformers_version\": \"4.17.0\",\n",
" \"vocab_size\": 30522\n",
"}\n",
"\n",
"loading file https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt from cache at /tmp/xdg_cache/huggingface/transformers/0e1bbfda7f63a99bb52e3915dcf10c3c92122b827d92eb2d34ce94ee79ba486c.d789d64ebfe299b0e416afc4a169632f903f693095b4629a7ea271d5a0cf2c99\n",
"loading file https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json from cache at /tmp/xdg_cache/huggingface/transformers/75abb59d7a06f4f640158a9bfcde005264e59e8d566781ab1415b139d2e4c603.7f2721073f19841be16f41b0a70b600ca6b880c8f3df6f3535cbc704371bdfa4\n",
"loading file https://huggingface.co/distilbert-base-uncased/resolve/main/added_tokens.json from cache at None\n",
"loading file https://huggingface.co/distilbert-base-uncased/resolve/main/special_tokens_map.json from cache at None\n",
"loading file https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer_config.json from cache at /tmp/xdg_cache/huggingface/transformers/8c8624b8ac8aa99c60c912161f8332de003484428c47906d7ff7eb7f73eecdbb.20430bd8e10ef77a7d2977accefe796051e01bc2fc4aa146bc862997a1a15e79\n",
"loading configuration file https://huggingface.co/distilbert-base-uncased/resolve/main/config.json from cache at /tmp/xdg_cache/huggingface/transformers/23454919702d26495337f3da04d1655c7ee010d5ec9d77bdb9e399e00302c0a1.91b885ab15d631bf9cee9dc9d25ece0afd932f2f5130eba28f2055b2220c0333\n",
"Model config DistilBertConfig {\n",
" \"_name_or_path\": \"distilbert-base-uncased\",\n",
" \"activation\": \"gelu\",\n",
" \"architectures\": [\n",
" \"DistilBertForMaskedLM\"\n",
" ],\n",
" \"attention_dropout\": 0.1,\n",
" \"dim\": 768,\n",
" \"dropout\": 0.1,\n",
" \"hidden_dim\": 3072,\n",
" \"initializer_range\": 0.02,\n",
" \"max_position_embeddings\": 512,\n",
" \"model_type\": \"distilbert\",\n",
" \"n_heads\": 12,\n",
" \"n_layers\": 6,\n",
" \"pad_token_id\": 0,\n",
" \"qa_dropout\": 0.1,\n",
" \"seq_classif_dropout\": 0.2,\n",
" \"sinusoidal_pos_embds\": false,\n",
" \"tie_weights_\": true,\n",
" \"transformers_version\": \"4.17.0\",\n",
" \"vocab_size\": 30522\n",
"}\n",
"\n"
]
}
],
"source": [
"#!g1.1\n",
"from transformers import AutoTokenizer\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "8d8db99a",
"metadata": {
"cellId": "n92sgrt3hao4qyq6sxedcn"
},
"outputs": [],
"source": [
"#!g1.1\n",
"dict_label = {'math': 3,\n",
" 'physics': 4,\n",
" 'q-bio': 5,\n",
" 'cs': 0,\n",
" 'q-fin': 6,\n",
" 'stat': 7,\n",
" 'eess': 2,\n",
" 'econ': 1}"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "fdbdddec",
"metadata": {
"cellId": "fr2cvkd1hljgj72kq6zja"
},
"outputs": [
{
"data": {
"text/plain": [
"{3: 'math',\n",
" 4: 'physics',\n",
" 5: 'q-bio',\n",
" 0: 'cs',\n",
" 6: 'q-fin',\n",
" 7: 'stat',\n",
" 2: 'eess',\n",
" 1: 'econ'}"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#!g1.1\n",
"inv_map = {v: k for k, v in dict_label.items()}\n",
"inv_map"
]
},
{
"cell_type": "code",
"execution_count": 99,
"id": "029703f7",
"metadata": {
"cellId": "u0seb2c1ukrj9n8fdlh"
},
"outputs": [
{
"data": {
"text/plain": [
"array([1.15543455e-02, 2.51640333e-03, 2.27772980e-03, 9.46712017e-01,\n",
" 4.27448237e-03, 8.11084581e-04, 2.39739451e-03, 2.94565000e-02],\n",
" dtype=float32)"
]
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/kernel/lib/python3.8/site-packages/ml_kernel/kernel.py:872: UserWarning: The following variables cannot be serialized: trainer\n",
" warnings.warn(message)\n"
]
}
],
"source": [
"#!g1.1\n",
"text = \"\"\"mathematics\"\"\"\n",
"tokens = tokenizer.encode(text)\n",
"with torch.no_grad():\n",
" logits = model(torch.as_tensor([tokens], device=device))[0]\n",
" probs = torch.softmax(logits[-1, :], dim=-1).data.cpu().numpy()\n",
"probs"
]
},
{
"cell_type": "code",
"execution_count": 100,
"id": "e7997e90",
"metadata": {
"cellId": "0xkwtgvl4kugzdw4d5ce4u"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.946712, 0.0294565]\n",
"['math', 'stat']\n"
]
}
],
"source": [
"#!g1.1\n",
"idx_label = np.argsort(probs)[::-1]\n",
"\n",
"sum_probs = 0\n",
"prediction_probs = []\n",
"prediction_classes = []\n",
"\n",
"idx = 0\n",
"while sum_probs < 0.95:\n",
" cur_predict = inv_map[idx_label[idx]]\n",
" cur_probs = probs[idx_label[idx]]\n",
" \n",
" sum_probs += cur_probs\n",
" \n",
" prediction_probs.append(cur_probs)\n",
" prediction_classes.append(cur_predict)\n",
" \n",
" idx += 1\n",
"\n",
"print(prediction_probs)\n",
"print(prediction_classes)"
]
},
{
"cell_type": "code",
"execution_count": 117,
"id": "eb1554c2",
"metadata": {
"cellId": "db2fcd21obqf3e3atfpa9"
},
"outputs": [],
"source": [
"#!g1.1\n",
"def predict_label(title, summary, tokenizer, model, inv_map):\n",
" abstract = title.lower() + '. ' + summary.lower()\n",
" token_text = tokenizer.encode(abstract)\n",
" \n",
" with torch.no_grad():\n",
" logits = model(torch.as_tensor([token_text], device=device))[0]\n",
" probs = torch.softmax(logits[-1, :], dim=-1).data.cpu().numpy()\n",
" \n",
" idx_label = np.argsort(probs)[::-1]\n",
"\n",
" sum_probs = 0\n",
" prediction_probs = []\n",
" prediction_classes = []\n",
"\n",
" idx = 0\n",
" while sum_probs < 0.95:\n",
" cur_predict = inv_map[idx_label[idx]]\n",
" cur_probs = probs[idx_label[idx]]\n",
" \n",
" sum_probs += cur_probs\n",
" \n",
" prediction_probs.append(cur_probs)\n",
" prediction_classes.append(cur_predict)\n",
" \n",
" idx += 1\n",
" \n",
" return prediction_classes, prediction_probs, probs"
]
},
{
"cell_type": "code",
"execution_count": 148,
"id": "55d10e54",
"metadata": {
"cellId": "ucqqhln4ocv503bn1tnrl"
},
"outputs": [],
"source": [
"#!g1.1\n",
"title = \"strategic behaviour and indicative price diffusion in paris stock exchange auctions\"\n",
"summary = \"we report statistical regularities of the opening and closing auctions of french equities, focusing on the diffusive properties of the indicative auction price. two mechanisms are at play as the auction end time nears: the typical price change magnitude decreases, favoring underdiffusion, while the rate of these events increases, potentially leading to overdiffusion. a third mechanism, caused by the strategic behavior of traders, is needed to produce nearly diffusive prices: waiting to submit buy orders until sell orders have decreased the indicative price and vice-versa.\"\n",
"\n",
"prediction_classes, prediction_probs, probs = predict_label(title, summary, tokenizer, model, inv_map)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6beee65e",
"metadata": {
"cellId": "g78808bpr6o71yop72329p"
},
"outputs": [],
"source": [
"#!g1.1\n",
"prediction_classes, prediction_probs, probs = predict_label(title, summary, tokenizer, model, inv_map)\n",
" \n",
"data = pd.DataFrame({'Categories' : tag, 'Probs' : probs})\n",
"data = data.sort_values(by='Probs', ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "19a82d5c",
"metadata": {
"cellId": "aclb6f96707kgmg5h42ctp"
},
"outputs": [],
"source": [
"#!g1.1\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dffcbc7c",
"metadata": {
"cellId": "cxc30s516nw6j01mqya0q"
},
"outputs": [],
"source": [
"#!g1.1\n",
"ะทะด"
]
},
{
"cell_type": "code",
"execution_count": 140,
"id": "aec20ffa",
"metadata": {
"cellId": "q0bj4c9toe6hr4gozk03y"
},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 140,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#!g1.1\n",
"import seaborn as sns\n",
"\n",
"tag = ['CS', 'Econ', 'EESS', \n",
" 'Math', 'Physics', 'Q-bio', 'Q-fin', 'Stat']\n",
"\n",
"sns.barplot(x=tag, y=probs)"
]
},
{
"cell_type": "code",
"execution_count": 149,
"id": "d28282e7",
"metadata": {
"cellId": "nr2s8dbt2tv474bv8e43"
},
"outputs": [
{
"data": {
"text/plain": [
"'โโโฎโโโโญโโโโญโณโโฎโ\\u2003\\nโโโโโโโโโโโฐโปโฎโโ\\u2003\\nโโโฐโฏโโฐโฏโฃโโโโฎโโโ\\u2003\\nโโโโโฒโโโโโญโโฃโฏโโ\\u2003\\nโโฐโณโฐโโฏโณโฏโญโโโฃโโฏโ\\u2003\\nโโโฐโฏโโฐโฏโโฐโโโฏโโโ'"
]
},
"execution_count": 149,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#!g1.1\n"
]
},
{
"cell_type": "code",
"execution_count": 145,
"id": "aae99fb0",
"metadata": {
"cellId": "xi82ygn14o9peav0j49tk"
},
"outputs": [
{
"ename": "AttributeError",
"evalue": "module 'matplotlib.pyplot' has no attribute 'pyplot'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-eae06c26c813>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtag\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprobs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: module 'matplotlib.pyplot' has no attribute 'pyplot'"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/kernel/lib/python3.8/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
" return array(a, dtype, copy=False, order=order)\n"
]
},
{
"ename": "ValueError",
"evalue": "setting an array element with a sequence.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;31mTypeError\u001b[0m: only size-1 arrays can be converted to Python scalars",
"\nThe above exception was the direct cause of the following exception:\n",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/kernel/lib/python3.8/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 339\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 340\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 341\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 342\u001b[0m \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/kernel/lib/python3.8/site-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m 246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 247\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 248\u001b[0;31m \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 249\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 250\u001b[0m \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/kernel/lib/python3.8/site-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m 130\u001b[0m \u001b[0mFigureCanvasBase\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 131\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 133\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 134\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/kernel/lib/python3.8/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m 2191\u001b[0m else suppress())\n\u001b[1;32m 2192\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2193\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2194\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2195\u001b[0m bbox_inches = self.figure.get_tightbbox(\n",
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"\u001b[0;32m/kernel/lib/python3.8/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 1861\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1862\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1863\u001b[0;31m mimage._draw_list_compositing_images(\n\u001b[0m\u001b[1;32m 1864\u001b[0m renderer, self, artists, self.suppressComposite)\n\u001b[1;32m 1865\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/kernel/lib/python3.8/site-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36mfrom_extents\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m 820\u001b[0m \u001b[0mThe\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0maxis\u001b[0m \u001b[0mincreases\u001b[0m \u001b[0mupwards\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 821\u001b[0m \"\"\"\n\u001b[0;32m--> 822\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mBbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 823\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 824\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__format__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfmt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/kernel/lib/python3.8/site-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, points, **kwargs)\u001b[0m\n\u001b[1;32m 772\u001b[0m \"\"\"\n\u001b[1;32m 773\u001b[0m \u001b[0mBboxBase\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 774\u001b[0;31m \u001b[0mpoints\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpoints\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 775\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mpoints\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 776\u001b[0m raise ValueError('Bbox points must be of the form '\n",
"\u001b[0;32m/kernel/lib/python3.8/site-packages/numpy/core/_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m 81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 82\u001b[0m \"\"\"\n\u001b[0;32m---> 83\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 84\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 85\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: setting an array element with a sequence."
]
},
{
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#!g1.1\n",
"import matplotlib.pyplot as plt\n",
"\n",
"fig, ax = plt.subplots()\n",
"ax.hist(x=tag, y=probs, bins=20)\n",
"\n",
"plt.pyplot(fig)"
]
},
{
"cell_type": "code",
"execution_count": 144,
"id": "38d8c3b7",
"metadata": {
"cellId": "kehpd4xdcpjpjfptvfspg"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Configuration saved in my_beautiful_model/config.json\n",
"Model weights saved in my_beautiful_model/pytorch_model.bin\n",
"/kernel/lib/python3.8/site-packages/ml_kernel/kernel.py:872: UserWarning: The following variables cannot be serialized: trainer\n",
" warnings.warn(message)\n"
]
}
],
"source": [
"#!g1.1\n",
"model.save_pretrained(\"my_beautiful_model\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Yandex DataSphere Kernel",
"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": "3.7.7"
},
"notebookId": "ee1ba0a4-4ed3-4508-aaee-3fe3cf7b2f0c",
"notebookPath": "Untitled (1).ipynb"
},
"nbformat": 4,
"nbformat_minor": 5
}
|