will33am commited on
Commit
455ae37
1 Parent(s): 9766e1e
.ipynb_checkpoints/AVA-checkpoint.py CHANGED
@@ -57,7 +57,7 @@ class AVA(datasets.GeneratorBasedBuilder):
57
  if path.endswith(".jpg"):
58
  # image filepath format: <IMAGE_FILE NAME>_<SYNSET_ID>.JPEG
59
  _id = int(os.path.splitext(path)[0].split('/')[-1])
60
- _metadata = self.DICT_METADATA[_id]
61
  ex = {"image": {"path": path, "bytes": file.read()},
62
  "rating_counts": _metadata[0],
63
  "text_tag0":_metadata[1],
 
57
  if path.endswith(".jpg"):
58
  # image filepath format: <IMAGE_FILE NAME>_<SYNSET_ID>.JPEG
59
  _id = int(os.path.splitext(path)[0].split('/')[-1])
60
+ _metadata = self.dict_metadata[_id]
61
  ex = {"image": {"path": path, "bytes": file.read()},
62
  "rating_counts": _metadata[0],
63
  "text_tag0":_metadata[1],
AVA.py CHANGED
@@ -57,7 +57,7 @@ class AVA(datasets.GeneratorBasedBuilder):
57
  if path.endswith(".jpg"):
58
  # image filepath format: <IMAGE_FILE NAME>_<SYNSET_ID>.JPEG
59
  _id = int(os.path.splitext(path)[0].split('/')[-1])
60
- _metadata = self.DICT_METADATA[_id]
61
  ex = {"image": {"path": path, "bytes": file.read()},
62
  "rating_counts": _metadata[0],
63
  "text_tag0":_metadata[1],
 
57
  if path.endswith(".jpg"):
58
  # image filepath format: <IMAGE_FILE NAME>_<SYNSET_ID>.JPEG
59
  _id = int(os.path.splitext(path)[0].split('/')[-1])
60
+ _metadata = self.dict_metadata[_id]
61
  ex = {"image": {"path": path, "bytes": file.read()},
62
  "rating_counts": _metadata[0],
63
  "text_tag0":_metadata[1],
notebooks/Test.ipynb CHANGED
@@ -2,7 +2,7 @@
2
  "cells": [
3
  {
4
  "cell_type": "code",
5
- "execution_count": 1,
6
  "id": "aef315bf",
7
  "metadata": {},
8
  "outputs": [],
@@ -12,19 +12,19 @@
12
  },
13
  {
14
  "cell_type": "code",
15
- "execution_count": 2,
16
  "id": "c0ed6498",
17
  "metadata": {},
18
  "outputs": [
19
  {
20
  "data": {
21
  "application/vnd.jupyter.widget-view+json": {
22
- "model_id": "0379eea50d7946bc95e74b9e38393a83",
23
  "version_major": 2,
24
  "version_minor": 0
25
  },
26
  "text/plain": [
27
- "Downloading builder script: 0%| | 0.00/2.25k [00:00<?, ?B/s]"
28
  ]
29
  },
30
  "metadata": {},
@@ -34,13 +34,13 @@
34
  "name": "stdout",
35
  "output_type": "stream",
36
  "text": [
37
- "Downloading and preparing dataset ava/default to /home/william/.cache/huggingface/datasets/will33am___ava/default/1.0.0/ce866a196bfdfabe8895e8b963c38dcc8fe5e85e20c47b83ea842e3459fe032a...\n"
38
  ]
39
  },
40
  {
41
  "data": {
42
  "application/vnd.jupyter.widget-view+json": {
43
- "model_id": "13f90839e6834e76ad009719192229f1",
44
  "version_major": 2,
45
  "version_minor": 0
46
  },
@@ -50,99 +50,6 @@
50
  },
51
  "metadata": {},
52
  "output_type": "display_data"
53
- },
54
- {
55
- "name": "stderr",
56
- "output_type": "stream",
57
- "text": [
58
- "Computing checksums of downloaded files. They can be used for integrity verification. You can disable this by passing ignore_verifications=True to load_dataset\n"
59
- ]
60
- },
61
- {
62
- "data": {
63
- "application/vnd.jupyter.widget-view+json": {
64
- "model_id": "523195fe98504ce98071f9fd39a2b9ca",
65
- "version_major": 2,
66
- "version_minor": 0
67
- },
68
- "text/plain": [
69
- "Computing checksums: 100%|##########| 1/1 [01:33<00:00, 93.72s/it]"
70
- ]
71
- },
72
- "metadata": {},
73
- "output_type": "display_data"
74
- },
75
- {
76
- "name": "stdout",
77
- "output_type": "stream",
78
- "text": [
79
- "Init loading Metadata\n"
80
- ]
81
- },
82
- {
83
- "data": {
84
- "application/vnd.jupyter.widget-view+json": {
85
- "model_id": "a7159023978844d0b3059697f046b975",
86
- "version_major": 2,
87
- "version_minor": 0
88
- },
89
- "text/plain": [
90
- "Downloading data: 0%| | 0.00/42.4M [00:00<?, ?B/s]"
91
- ]
92
- },
93
- "metadata": {},
94
- "output_type": "display_data"
95
- },
96
- {
97
- "name": "stdout",
98
- "output_type": "stream",
99
- "text": [
100
- "Finish loading Metadata\n"
101
- ]
102
- },
103
- {
104
- "data": {
105
- "application/vnd.jupyter.widget-view+json": {
106
- "model_id": "21ade8bfc87040989558d0778b21aeed",
107
- "version_major": 2,
108
- "version_minor": 0
109
- },
110
- "text/plain": [
111
- "Generating train split: 0 examples [00:00, ? examples/s]"
112
- ]
113
- },
114
- "metadata": {},
115
- "output_type": "display_data"
116
- },
117
- {
118
- "name": "stderr",
119
- "output_type": "stream",
120
- "text": [
121
- "\n",
122
- "0it [00:00, ?it/s]\u001b[A\n"
123
- ]
124
- },
125
- {
126
- "ename": "DatasetGenerationError",
127
- "evalue": "An error occurred while generating the dataset",
128
- "output_type": "error",
129
- "traceback": [
130
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
131
- "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
132
- "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/builder.py:1570\u001b[0m, in \u001b[0;36mGeneratorBasedBuilder._prepare_split_single\u001b[0;34m(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id)\u001b[0m\n\u001b[1;32m 1569\u001b[0m _time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[0;32m-> 1570\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, record \u001b[38;5;129;01min\u001b[39;00m generator:\n\u001b[1;32m 1571\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m max_shard_size \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m writer\u001b[38;5;241m.\u001b[39m_num_bytes \u001b[38;5;241m>\u001b[39m max_shard_size:\n",
133
- "File \u001b[0;32m~/.cache/huggingface/modules/datasets_modules/datasets/will33am--AVA/ce866a196bfdfabe8895e8b963c38dcc8fe5e85e20c47b83ea842e3459fe032a/AVA.py:62\u001b[0m, in \u001b[0;36mAVA._generate_examples\u001b[0;34m(self, archives, split)\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m path\u001b[38;5;241m.\u001b[39mendswith(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.jpg\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 61\u001b[0m \u001b[38;5;66;03m# image filepath format: <IMAGE_FILE NAME>_<SYNSET_ID>.JPEG\u001b[39;00m\n\u001b[0;32m---> 62\u001b[0m _id \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39msplitext(\u001b[43mb\u001b[49m[\u001b[38;5;241m0\u001b[39m])[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m'\u001b[39m)[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m])\n\u001b[1;32m 63\u001b[0m _metadata \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mDICT_METADATA[_id]\n",
134
- "\u001b[0;31mNameError\u001b[0m: name 'b' is not defined",
135
- "\nThe above exception was the direct cause of the following exception:\n",
136
- "\u001b[0;31mDatasetGenerationError\u001b[0m Traceback (most recent call last)",
137
- "File \u001b[0;32m<timed exec>:1\u001b[0m\n",
138
- "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/load.py:1757\u001b[0m, in \u001b[0;36mload_dataset\u001b[0;34m(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, **config_kwargs)\u001b[0m\n\u001b[1;32m 1754\u001b[0m try_from_hf_gcs \u001b[38;5;241m=\u001b[39m path \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m _PACKAGED_DATASETS_MODULES\n\u001b[1;32m 1756\u001b[0m \u001b[38;5;66;03m# Download and prepare data\u001b[39;00m\n\u001b[0;32m-> 1757\u001b[0m \u001b[43mbuilder_instance\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload_and_prepare\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1758\u001b[0m \u001b[43m \u001b[49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1759\u001b[0m \u001b[43m \u001b[49m\u001b[43mdownload_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1760\u001b[0m \u001b[43m \u001b[49m\u001b[43mignore_verifications\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_verifications\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1761\u001b[0m \u001b[43m \u001b[49m\u001b[43mtry_from_hf_gcs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtry_from_hf_gcs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1762\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_proc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_proc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1763\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1765\u001b[0m \u001b[38;5;66;03m# Build dataset for splits\u001b[39;00m\n\u001b[1;32m 1766\u001b[0m keep_in_memory \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 1767\u001b[0m keep_in_memory \u001b[38;5;28;01mif\u001b[39;00m keep_in_memory \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m is_small_dataset(builder_instance\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39mdataset_size)\n\u001b[1;32m 1768\u001b[0m )\n",
139
- "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/builder.py:860\u001b[0m, in \u001b[0;36mDatasetBuilder.download_and_prepare\u001b[0;34m(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)\u001b[0m\n\u001b[1;32m 858\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m num_proc \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 859\u001b[0m prepare_split_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnum_proc\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m num_proc\n\u001b[0;32m--> 860\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_download_and_prepare\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 861\u001b[0m \u001b[43m \u001b[49m\u001b[43mdl_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdl_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 862\u001b[0m \u001b[43m \u001b[49m\u001b[43mverify_infos\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverify_infos\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 863\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mprepare_split_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 864\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdownload_and_prepare_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 865\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 866\u001b[0m \u001b[38;5;66;03m# Sync info\u001b[39;00m\n\u001b[1;32m 867\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39mdataset_size \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msum\u001b[39m(split\u001b[38;5;241m.\u001b[39mnum_bytes \u001b[38;5;28;01mfor\u001b[39;00m split \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39msplits\u001b[38;5;241m.\u001b[39mvalues())\n",
140
- "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/builder.py:1611\u001b[0m, in \u001b[0;36mGeneratorBasedBuilder._download_and_prepare\u001b[0;34m(self, dl_manager, verify_infos, **prepare_splits_kwargs)\u001b[0m\n\u001b[1;32m 1610\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_download_and_prepare\u001b[39m(\u001b[38;5;28mself\u001b[39m, dl_manager, verify_infos, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mprepare_splits_kwargs):\n\u001b[0;32m-> 1611\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_download_and_prepare\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1612\u001b[0m \u001b[43m \u001b[49m\u001b[43mdl_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverify_infos\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_duplicate_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverify_infos\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mprepare_splits_kwargs\u001b[49m\n\u001b[1;32m 1613\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
141
- "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/builder.py:953\u001b[0m, in \u001b[0;36mDatasetBuilder._download_and_prepare\u001b[0;34m(self, dl_manager, verify_infos, **prepare_split_kwargs)\u001b[0m\n\u001b[1;32m 949\u001b[0m split_dict\u001b[38;5;241m.\u001b[39madd(split_generator\u001b[38;5;241m.\u001b[39msplit_info)\n\u001b[1;32m 951\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 952\u001b[0m \u001b[38;5;66;03m# Prepare split will record examples associated to the split\u001b[39;00m\n\u001b[0;32m--> 953\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_prepare_split\u001b[49m\u001b[43m(\u001b[49m\u001b[43msplit_generator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mprepare_split_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 954\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 955\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m(\n\u001b[1;32m 956\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot find data file. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 957\u001b[0m \u001b[38;5;241m+\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmanual_download_instructions \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 958\u001b[0m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mOriginal error:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 959\u001b[0m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(e)\n\u001b[1;32m 960\u001b[0m ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28mNone\u001b[39m\n",
142
- "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/builder.py:1449\u001b[0m, in \u001b[0;36mGeneratorBasedBuilder._prepare_split\u001b[0;34m(self, split_generator, check_duplicate_keys, file_format, num_proc, max_shard_size)\u001b[0m\n\u001b[1;32m 1447\u001b[0m gen_kwargs \u001b[38;5;241m=\u001b[39m split_generator\u001b[38;5;241m.\u001b[39mgen_kwargs\n\u001b[1;32m 1448\u001b[0m job_id \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m-> 1449\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m job_id, done, content \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_split_single(\n\u001b[1;32m 1450\u001b[0m gen_kwargs\u001b[38;5;241m=\u001b[39mgen_kwargs, job_id\u001b[38;5;241m=\u001b[39mjob_id, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m_prepare_split_args\n\u001b[1;32m 1451\u001b[0m ):\n\u001b[1;32m 1452\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m done:\n\u001b[1;32m 1453\u001b[0m result \u001b[38;5;241m=\u001b[39m content\n",
143
- "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/builder.py:1606\u001b[0m, in \u001b[0;36mGeneratorBasedBuilder._prepare_split_single\u001b[0;34m(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id)\u001b[0m\n\u001b[1;32m 1604\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e, SchemaInferenceError) \u001b[38;5;129;01mand\u001b[39;00m e\u001b[38;5;241m.\u001b[39m__context__ \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1605\u001b[0m e \u001b[38;5;241m=\u001b[39m e\u001b[38;5;241m.\u001b[39m__context__\n\u001b[0;32m-> 1606\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m DatasetGenerationError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAn error occurred while generating the dataset\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m 1608\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m job_id, \u001b[38;5;28;01mTrue\u001b[39;00m, (total_num_examples, total_num_bytes, writer\u001b[38;5;241m.\u001b[39m_features, num_shards, shard_lengths)\n",
144
- "\u001b[0;31mDatasetGenerationError\u001b[0m: An error occurred while generating the dataset"
145
- ]
146
  }
147
  ],
148
  "source": [
@@ -153,7 +60,7 @@
153
  {
154
  "cell_type": "code",
155
  "execution_count": null,
156
- "id": "aa863a32",
157
  "metadata": {},
158
  "outputs": [],
159
  "source": []
 
2
  "cells": [
3
  {
4
  "cell_type": "code",
5
+ "execution_count": 3,
6
  "id": "aef315bf",
7
  "metadata": {},
8
  "outputs": [],
 
12
  },
13
  {
14
  "cell_type": "code",
15
+ "execution_count": null,
16
  "id": "c0ed6498",
17
  "metadata": {},
18
  "outputs": [
19
  {
20
  "data": {
21
  "application/vnd.jupyter.widget-view+json": {
22
+ "model_id": "f4ed52d19cfd40329ebbc4d9e6b9b8ff",
23
  "version_major": 2,
24
  "version_minor": 0
25
  },
26
  "text/plain": [
27
+ "Downloading builder script: 0%| | 0.00/2.16k [00:00<?, ?B/s]"
28
  ]
29
  },
30
  "metadata": {},
 
34
  "name": "stdout",
35
  "output_type": "stream",
36
  "text": [
37
+ "Downloading and preparing dataset ava/default to /home/william/.cache/huggingface/datasets/will33am___ava/default/1.0.0/b25331909ca9a0584688517c5b03689144393394f220cb4466e16bca4f72400e...\n"
38
  ]
39
  },
40
  {
41
  "data": {
42
  "application/vnd.jupyter.widget-view+json": {
43
+ "model_id": "d233ac0e5616412aae8ebec9fa9dc7be",
44
  "version_major": 2,
45
  "version_minor": 0
46
  },
 
50
  },
51
  "metadata": {},
52
  "output_type": "display_data"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
53
  }
54
  ],
55
  "source": [
 
60
  {
61
  "cell_type": "code",
62
  "execution_count": null,
63
+ "id": "64d98db9",
64
  "metadata": {},
65
  "outputs": [],
66
  "source": []