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fix more examples
This commit is contained in:
Sourab Mangrulkar committed
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6 files changed
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+6
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@@ -48,70 +48,10 @@
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},
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"cell_type": "code",
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"execution_count": 2,
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"id": "e1a3648b",
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"metadata": {},
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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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"Found cached dataset raft (/home/sourab/.cache/huggingface/datasets/ought___raft/twitter_complaints/1.1.0/79c4de1312c1e3730043f7db07179c914f48403101f7124e2fe336f6f54d9f84)\n"
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "d1160ad661de452dbce6944a881a160b",
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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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" 0%| | 0/2 [00:00<?, ?it/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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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Loading cached processed dataset at /home/sourab/.cache/huggingface/datasets/ought___raft/twitter_complaints/1.1.0/79c4de1312c1e3730043f7db07179c914f48403101f7124e2fe336f6f54d9f84/cache-20a7622c86d80cdf.arrow\n",
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"Loading cached processed dataset at /home/sourab/.cache/huggingface/datasets/ought___raft/twitter_complaints/1.1.0/79c4de1312c1e3730043f7db07179c914f48403101f7124e2fe336f6f54d9f84/cache-5f1431311da05803.arrow\n"
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]
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},
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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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"['Unlabeled', 'complaint', 'no complaint']\n",
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"DatasetDict({\n",
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" train: Dataset({\n",
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" features: ['Tweet text', 'ID', 'Label', 'text_label'],\n",
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" num_rows: 50\n",
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" })\n",
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" test: Dataset({\n",
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" features: ['Tweet text', 'ID', 'Label', 'text_label'],\n",
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" num_rows: 3399\n",
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" })\n",
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"})\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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"{'Tweet text': '@HMRCcustomers No this is my first job',\n",
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" 'ID': 0,\n",
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" 'Label': 2,\n",
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" 'text_label': 'no complaint'}"
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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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"outputs": [],
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"source": [
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"from datasets import load_dataset\n",
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"\n",
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@@ -229,219 +169,10 @@
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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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"execution_count": null,
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"id": "2795b9d0",
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"metadata": {},
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"outputs": [
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"data": {
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"model_id": "293c2191c9674123afc58cfa22c0cf57",
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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%| | 0/1 [00:00<?, ?ba/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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"data": {
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"model_id": "c376f3397f574c9084946ab171f43c4f",
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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%| | 0/4 [00:00<?, ?ba/s]"
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]
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"metadata": {},
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|
||||
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|
||||
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|
||||
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def test_preprocess_function(examples):\n",
|
||||
" batch_size = len(examples[text_column])\n",
|
||||
@@ -671,131 +402,10 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"execution_count": null,
|
||||
"id": "a70802a3",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
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||||
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|
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|
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|
||||
"text/plain": [
|
||||
"['no complaint',\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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||||
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|
||||
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||||
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||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
" 'complaint',\n",
|
||||
" 'no complaint',\n",
|
||||
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|
||||
" 'no complaint',\n",
|
||||
" 'complaint',\n",
|
||||
" 'no complaint',\n",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
" 'no complaint',\n",
|
||||
" 'complaint',\n",
|
||||
" 'complaint',\n",
|
||||
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|
||||
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|
||||
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|
||||
" 'no complaint',\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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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
" 'no complaint',\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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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
" 'no complaint',\n",
|
||||
" 'no complaint']"
|
||||
]
|
||||
},
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model.eval()\n",
|
||||
"test_preds = []\n",
|
||||
|
||||
File diff suppressed because it is too large.
Load diff
File diff suppressed because it is too large.
Load diff
+9
-217
@@ -2,26 +2,10 @@
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"execution_count": null,
|
||||
"id": "71fbfca2",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"===================================BUG REPORT===================================\n",
|
||||
"Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
|
||||
"For effortless bug reporting copy-paste your error into this form: https://docs.google.com/forms/d/e/1FAIpQLScPB8emS3Thkp66nvqwmjTEgxp8Y9ufuWTzFyr9kJ5AoI47dQ/viewform?usp=sf_link\n",
|
||||
"================================================================================\n",
|
||||
"CUDA SETUP: CUDA runtime path found: /home/sourab/miniconda3/envs/ml/lib/libcudart.so\n",
|
||||
"CUDA SETUP: Highest compute capability among GPUs detected: 7.5\n",
|
||||
"CUDA SETUP: Detected CUDA version 117\n",
|
||||
"CUDA SETUP: Loading binary /home/sourab/miniconda3/envs/ml/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda117.so...\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from transformers import AutoModelForSeq2SeqLM\n",
|
||||
"from peft import PeftModel, PeftConfig\n",
|
||||
@@ -59,70 +43,10 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"execution_count": null,
|
||||
"id": "e1a3648b",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Found cached dataset raft (/home/sourab/.cache/huggingface/datasets/ought___raft/twitter_complaints/1.1.0/79c4de1312c1e3730043f7db07179c914f48403101f7124e2fe336f6f54d9f84)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "0bf24d09ce9642ec804aa0f208f54e1d",
|
||||
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|
||||
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|
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||||
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||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Loading cached processed dataset at /home/sourab/.cache/huggingface/datasets/ought___raft/twitter_complaints/1.1.0/79c4de1312c1e3730043f7db07179c914f48403101f7124e2fe336f6f54d9f84/cache-20a7622c86d80cdf.arrow\n",
|
||||
"Loading cached processed dataset at /home/sourab/.cache/huggingface/datasets/ought___raft/twitter_complaints/1.1.0/79c4de1312c1e3730043f7db07179c914f48403101f7124e2fe336f6f54d9f84/cache-5f1431311da05803.arrow\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"['Unlabeled', 'complaint', 'no complaint']\n",
|
||||
"DatasetDict({\n",
|
||||
" train: Dataset({\n",
|
||||
" features: ['Tweet text', 'ID', 'Label', 'text_label'],\n",
|
||||
" num_rows: 50\n",
|
||||
" })\n",
|
||||
" test: Dataset({\n",
|
||||
" features: ['Tweet text', 'ID', 'Label', 'text_label'],\n",
|
||||
" num_rows: 3399\n",
|
||||
" })\n",
|
||||
"})\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'Tweet text': '@HMRCcustomers No this is my first job',\n",
|
||||
" 'ID': 0,\n",
|
||||
" 'Label': 2,\n",
|
||||
" 'text_label': 'no complaint'}"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from datasets import load_dataset\n",
|
||||
"\n",
|
||||
@@ -142,19 +66,10 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"execution_count": null,
|
||||
"id": "fe12d4d3",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Loading cached processed dataset at /home/sourab/.cache/huggingface/datasets/ought___raft/twitter_complaints/1.1.0/79c4de1312c1e3730043f7db07179c914f48403101f7124e2fe336f6f54d9f84/cache-ad68bb6ecb83fbb7.arrow\n",
|
||||
"Loading cached processed dataset at /home/sourab/.cache/huggingface/datasets/ought___raft/twitter_complaints/1.1.0/79c4de1312c1e3730043f7db07179c914f48403101f7124e2fe336f6f54d9f84/cache-bc43a32cf6316354.arrow\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
|
||||
"target_max_length = max([len(tokenizer(class_label)[\"input_ids\"]) for class_label in classes])\n",
|
||||
@@ -291,133 +206,10 @@
|
||||
},
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
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|
||||
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|
||||
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|
||||
"text": [
|
||||
" 0%| | 0/425 [00:00<?, ?it/s]/home/sourab/transformers/src/transformers/generation/utils.py:1359: UserWarning: You are calling .generate() with the `input_ids` being on a device type different than your model's device. `input_ids` is on cpu, whereas the model is on cuda. You may experience unexpected behaviors or slower generation. Please make sure that you have put `input_ids` to the correct device by calling for example input_ids = input_ids.to('cuda') before running `.generate()`.\n",
|
||||
" warnings.warn(\n",
|
||||
" 3%|██▌ | 12/425 [00:20<11:57, 1.74s/it]\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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|
||||
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|
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|
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|
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||||
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||||
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||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
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||||
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|
||||
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|
||||
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|
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|
||||
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|
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|
||||
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
||||
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|
||||
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|
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model.eval()\n",
|
||||
"test_preds = []\n",
|
||||
@@ -450,7 +242,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.4"
|
||||
"version": "3.10.5 (v3.10.5:f377153967, Jun 6 2022, 12:36:10) [Clang 13.0.0 (clang-1300.0.29.30)]"
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
|
||||
@@ -58,58 +58,10 @@
|
||||
},
|
||||
{
|
||||
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|
||||
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|
||||
"execution_count": null,
|
||||
"id": "328161b7",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
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|
||||
"model_id": "5eafd408382e433fa904adcaad256551",
|
||||
"version_major": 2,
|
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||||
},
|
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||||
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|
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]
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|
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{
|
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"text": [
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"/home/sourab/transformers/src/transformers/models/clip/feature_extraction_clip.py:28: FutureWarning: The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use CLIPImageProcessor instead.\n",
|
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" warnings.warn(\n"
|
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]
|
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},
|
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truncated
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||||
"text/plain": [
|
||||
"<PIL.Image.Image image mode=RGB size=512x512>"
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pipe = StableDiffusionPipeline.from_pretrained(MODEL_NAME, torch_dtype=torch.float16).to(\"cuda\")\n",
|
||||
"\n",
|
||||
@@ -166,36 +118,10 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"execution_count": null,
|
||||
"id": "e50da571",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
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"version_major": 2,
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"version_minor": 0
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},
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"metadata": {},
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|
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},
|
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{
|
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"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<PIL.Image.Image image mode=RGB size=512x512>"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"prompt = \"a photo of sks dog\"\n",
|
||||
"negative_prompt = \"low quality, blurry, unfinished\"\n",
|
||||
@@ -243,36 +169,10 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"execution_count": null,
|
||||
"id": "200d3358",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "b669f753a13244d1884b7956ad0ccaf4",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
" 0%| | 0/50 [00:00<?, ?it/s]"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<PIL.Image.Image image mode=RGB size=512x512>"
|
||||
]
|
||||
},
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"prompt = \"sks dog with Eiffel Tower in the background\"\n",
|
||||
"image = pipe(prompt, num_inference_steps=50, \n",
|
||||
@@ -283,36 +183,10 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"execution_count": null,
|
||||
"id": "0518421e",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "6e4e70ffd53d46b79955b068403ac229",
|
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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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" 0%| | 0/50 [00:00<?, ?it/s]"
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]
|
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},
|
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"metadata": {},
|
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"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<PIL.Image.Image image mode=RGB size=512x512>"
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"prompt = \"sks dog swimming in th pool with sunglasses\"\n",
|
||||
"image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5, negative_prompt=negative_prompt).images[0]\n",
|
||||
@@ -321,36 +195,10 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"execution_count": null,
|
||||
"id": "cca5106c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "d9beb64979364f10b5cd9bba50d9caba",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
" 0%| | 0/50 [00:00<?, ?it/s]"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<PIL.Image.Image image mode=RGB size=512x512>"
|
||||
]
|
||||
},
|
||||
"execution_count": 28,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"prompt = \"sks dog weaing red sweater\"\n",
|
||||
"image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5, negative_prompt=negative_prompt).images[0]\n",
|
||||
|
||||
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Load diff
Reference in new issue
Block a user