174 lines
4.4 KiB
Plaintext
174 lines
4.4 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "9dbad513",
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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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"/home/firouzi/embedding_model/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\n",
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"Downloading readme: 100%|██████████| 419/419 [00:00<00:00, 1.18MB/s]\n",
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"Downloading data: 100%|██████████| 1.59M/1.59M [00:01<00:00, 1.03MB/s]\n",
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"Generating train split: 100%|██████████| 7000/7000 [00:00<00:00, 175360.77 examples/s]\n"
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]
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}
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],
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"source": [
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"from datasets import load_dataset\n",
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"\n",
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"ds = load_dataset(\"virattt/financial-qa-10K\", split=\"train\")"
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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": 2,
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"id": "7330f385",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'query': 'What area did NVIDIA initially focus on before expanding to other computationally intensive fields?',\n",
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" 'pos': 'Since our original focus on PC graphics, we have expanded to several other large and important computationally intensive fields.',\n",
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" 'id': '0'}"
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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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"ds = ds.select_columns(column_names=[\"question\", \"context\"])\n",
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"ds = ds.rename_column(\"question\", \"query\")\n",
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"ds = ds.rename_column(\"context\", \"pos\")\n",
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"ds = ds.add_column(\"id\", [str(i) for i in range(len(ds))])\n",
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"ds[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": 3,
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"id": "5ba361dd",
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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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"Map: 100%|██████████| 7000/7000 [00:00<00:00, 19176.72 examples/s]\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"\n",
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"np.random.seed(520)\n",
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"neg_num = 10\n",
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"\n",
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"def str_to_lst(data):\n",
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" data[\"pos\"] = [data[\"pos\"]]\n",
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" return data\n",
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"\n",
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"# sample negative texts\n",
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"new_col = []\n",
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"for i in range(len(ds)):\n",
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" ids = np.random.randint(0, len(ds), size=neg_num)\n",
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" while i in ids:\n",
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" ids = np.random.randint(0, len(ds), size=neg_num)\n",
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" neg = [ds[i.item()][\"pos\"] for i in ids]\n",
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" new_col.append(neg)\n",
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"ds = ds.add_column(\"neg\", new_col)\n",
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"\n",
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"# change the key of 'pos' to a list\n",
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"ds = ds.map(str_to_lst)"
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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": "bf3241ca",
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"metadata": {},
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"outputs": [],
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"source": [
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"instruction = \"Represent this sentence for searching relevant passages: \"\n",
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"ds = ds.add_column(\"prompt\", [instruction]*len(ds))"
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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": 5,
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"id": "a35c1466",
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"metadata": {},
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"outputs": [],
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"source": [
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"split = ds.train_test_split(test_size=0.1, shuffle=True, seed=520)\n",
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"train = split[\"train\"]\n",
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"test = split[\"test\"]"
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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": 6,
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"id": "24f3f7fb",
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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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"Creating json from Arrow format: 100%|██████████| 7/7 [00:00<00:00, 26.22ba/s]\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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"16583481"
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]
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},
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"execution_count": 6,
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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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"train.to_json(\"training.json\")"
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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": "c5cc42ed",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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