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4253 lines
129 KiB
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4253 lines
129 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "qAkXdLL2D25p"
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},
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"source": [
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"## Peft model evaluation using [lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness)\n",
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"\n",
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"In this notebook, we are going to learn how to evaluate the finetuned lora model on the hellaswag task using lm-eval-harness toolkit."
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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": 1,
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||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
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||
},
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"id": "o52TJHcYD25q",
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||
"outputId": "c5482c79-ff56-4ffa-d20c-46c3d30d2cd5"
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||
},
|
||
"outputs": [
|
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[33m DEPRECATION: Building 'rouge-score' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'rouge-score'. Discussion can be found at https://github.com/pypa/pip/issues/6334\u001b[0m\u001b[33m\n",
|
||
"\u001b[0m\u001b[33m DEPRECATION: Building 'sqlitedict' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'sqlitedict'. Discussion can be found at https://github.com/pypa/pip/issues/6334\u001b[0m\u001b[33m\n",
|
||
"\u001b[0m\u001b[33m DEPRECATION: Building 'word2number' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'word2number'. Discussion can be found at https://github.com/pypa/pip/issues/6334\u001b[0m\u001b[33m\n",
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||
"\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\n",
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"\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.1.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.2\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n"
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]
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}
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],
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"source": [
|
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"# Install LM-Eval\n",
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"!pip install -q datasets evaluate lm_eval"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "uhUflrJXD25q"
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},
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"source": [
|
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"### First we will check the accuracy score on the hellaswag task for the base bert without finetuning"
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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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"metadata": {
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"colab": {
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||
"base_uri": "https://localhost:8080/"
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},
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"id": "hwJIYD5KD25q",
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"outputId": "51e69f81-d048-46b2-9699-658d3ffc5f08"
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},
|
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"outputs": [
|
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"If you want to use `BertLMHeadModel` as a standalone, add `is_decoder=True.`\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "7b1ea8948a0747bc98795d6459270044",
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"version_minor": 0
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"text/plain": [
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"README.md: 0.00B [00:00, ?B/s]"
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},
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{
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"data": {
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"model_id": "4ec51e06812446899b66826c41697f8d",
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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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"data/train-00000-of-00001.parquet: 0%| | 0.00/24.4M [00:00<?, ?B/s]"
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]
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"metadata": {},
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"data": {
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"version_minor": 0
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"text/plain": [
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"data/test-00000-of-00001.parquet: 0%| | 0.00/6.11M [00:00<?, ?B/s]"
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"data": {
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"model_id": "0c887d06a56a410eae563e11a3080a52",
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"version_minor": 0
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"text/plain": [
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"data/validation-00000-of-00001.parquet: 0%| | 0.00/6.32M [00:00<?, ?B/s]"
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"metadata": {},
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "adb9c60c23f74f7d9af72ea2e34bc22c",
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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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"Generating train split: 0%| | 0/39905 [00:00<?, ? examples/s]"
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]
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"metadata": {},
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "b3418259aeaf4d459d5ed4fe9b8434fc",
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"version_major": 2,
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"version_minor": 0
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"text/plain": [
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]
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"metadata": {},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "da448b2a7a534fec9045c086fbda5d0d",
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "83f04bb57ab94be58080e8c67675a4e5",
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "f08ebbc81eaa45818c85e51169ac48cf",
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"version_major": 2,
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]
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|█████████████████████████████████████████████████████████████████████████████████████████████| 10042/10042 [00:02<00:00, 4111.19it/s]\n",
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"Running loglikelihood requests: 0%| | 0/40168 [00:00<?, ?it/s]We strongly recommend passing in an `attention_mask` since your input_ids may be padded. See https://huggingface.co/docs/transformers/troubleshooting#incorrect-output-when-padding-tokens-arent-masked.\n",
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"Running loglikelihood requests: 100%|██████████████████████████████████████████████████████████████| 40168/40168 [02:40<00:00, 250.28it/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": [
|
||
"{'hellaswag': {'alias': 'hellaswag',\n",
|
||
" 'acc,none': 0.24915355506871142,\n",
|
||
" 'acc_stderr,none': 0.004316389476434537,\n",
|
||
" 'acc_norm,none': 0.244672376020713,\n",
|
||
" 'acc_norm_stderr,none': 0.004290142029921662}}"
|
||
]
|
||
},
|
||
"execution_count": 3,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"import torch\n",
|
||
"import lm_eval\n",
|
||
"\n",
|
||
"\n",
|
||
"device = torch.accelerator.current_accelerator().type if hasattr(torch, \"accelerator\") else \"cuda\"\n",
|
||
"output = lm_eval.simple_evaluate(model = 'hf',\n",
|
||
" model_args = {\n",
|
||
" 'pretrained' : 'bert-base-cased',\n",
|
||
" 'dtype' : 'bfloat16'},\n",
|
||
" tasks = 'hellaswag',\n",
|
||
" device = device,\n",
|
||
" batch_size = 128,\n",
|
||
" log_samples = False)\n",
|
||
"output[\"results\"]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "EZk-1JT7D25r"
|
||
},
|
||
"source": [
|
||
"### Now lets try to finetune the bert on the imdb dataset (this is for demonstration and finetuning on imdb may not increase the scores on hellaswag task)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"metadata": {
|
||
"id": "FmtVeh7QD25r"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Import necessary libraries\n",
|
||
"import evaluate\n",
|
||
"import numpy as np\n",
|
||
"from datasets import load_dataset\n",
|
||
"from transformers import AutoTokenizer, BertForSequenceClassification, Trainer, TrainingArguments\n",
|
||
"\n",
|
||
"from peft import LoraConfig, TaskType, get_peft_model"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "rHF7tzN9D25r",
|
||
"outputId": "352ad9ab-2efc-41f8-c3d5-a7da05d9529b"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
|
||
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
|
||
"The 8-bit optimizer is not available on your device, only available on CUDA for now.\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"trainable params: 296,450 || all params: 108,608,260 || trainable%: 0.2730\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Configure LoRA for Sequence Classification\n",
|
||
"lora_config = LoraConfig(\n",
|
||
" task_type=TaskType.SEQ_CLS, # Set task type to sequence classification\n",
|
||
" target_modules=[\"query\", \"key\"] # Specify target modules for LoRA tuning\n",
|
||
")\n",
|
||
"\n",
|
||
"# Initialize the BERT model for sequence classification\n",
|
||
"model = BertForSequenceClassification.from_pretrained(\n",
|
||
" 'bert-base-cased',\n",
|
||
" num_labels = 2\n",
|
||
")\n",
|
||
"\n",
|
||
"# Wrap the model with LoRA configuration\n",
|
||
"model = get_peft_model(model, lora_config)\n",
|
||
"\n",
|
||
"model.print_trainable_parameters()\n",
|
||
"\n",
|
||
"tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"metadata": {
|
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 337,
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"text/plain": [
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]
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},
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"metadata": {},
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"output_type": "display_data"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"model_id": "c6147cbc8124478ea9eb921a2789d20e",
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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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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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]
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},
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"metadata": {},
|
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"output_type": "display_data"
|
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},
|
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{
|
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "1c6d66613cfc44d391d9a8bb3c58e732",
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"version_major": 2,
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"version_minor": 0
|
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},
|
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"text/plain": [
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"Map: 0%| | 0/25000 [00:00<?, ? examples/s]"
|
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]
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},
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"metadata": {},
|
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"output_type": "display_data"
|
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},
|
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{
|
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "26681307716846d68eca88409b126248",
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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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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
|
||
"# load the dataset\n",
|
||
"dataset = load_dataset(\"imdb\")\n",
|
||
"\n",
|
||
"def tokenize_function(row):\n",
|
||
" return tokenizer(row[\"text\"], padding=\"max_length\", truncation = True)\n",
|
||
"\n",
|
||
"tokenized_datasets = dataset.map(tokenize_function, batched = True)\n",
|
||
"\n",
|
||
"train_dataset = tokenized_datasets[\"train\"]\n",
|
||
"eval_dataset = tokenized_datasets[\"test\"]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"metadata": {
|
||
"id": "bA3k0iVED25r"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Define a function to compute evaluation metrics\n",
|
||
"\n",
|
||
"def compute_metrics(eval_pred):\n",
|
||
" logits, labels = eval_pred\n",
|
||
" predictions = np.argmax(logits, axis=-1)\n",
|
||
" metric = evaluate.load(\"accuracy\")\n",
|
||
" return metric.compute(predictions = predictions, references = labels)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 380
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||
},
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"id": "DFG74c3kD25s",
|
||
"outputId": "5dd9f988-95db-4efb-e632-5f741801910a"
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},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"No label_names provided for model class `PeftModelForSequenceClassification`. Since `PeftModel` hides base models input arguments, if label_names is not given, label_names can't be set automatically within `Trainer`. Note that empty label_names list will be used instead.\n"
|
||
]
|
||
},
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{
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"data": {
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"text/html": [
|
||
"\n",
|
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" <div>\n",
|
||
" \n",
|
||
" <progress value='3910' max='3910' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
|
||
" [3910/3910 40:13, Epoch 5/5]\n",
|
||
" </div>\n",
|
||
" <table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: left;\">\n",
|
||
" <th>Epoch</th>\n",
|
||
" <th>Training Loss</th>\n",
|
||
" <th>Validation Loss</th>\n",
|
||
" <th>Accuracy</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.353800</td>\n",
|
||
" <td>0.261258</td>\n",
|
||
" <td>0.901160</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>2</td>\n",
|
||
" <td>0.277400</td>\n",
|
||
" <td>0.221651</td>\n",
|
||
" <td>0.912480</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>3</td>\n",
|
||
" <td>0.244500</td>\n",
|
||
" <td>0.216107</td>\n",
|
||
" <td>0.918200</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>4</td>\n",
|
||
" <td>0.197000</td>\n",
|
||
" <td>0.215257</td>\n",
|
||
" <td>0.920040</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>5</td>\n",
|
||
" <td>0.157700</td>\n",
|
||
" <td>0.215050</td>\n",
|
||
" <td>0.923240</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table><p>"
|
||
],
|
||
"text/plain": [
|
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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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"text/plain": [
|
||
"Downloading builder script: 0.00B [00:00, ?B/s]"
|
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]
|
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|
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"metadata": {},
|
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|
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},
|
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{
|
||
"data": {
|
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"version_minor": 0
|
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"output_type": "display_data"
|
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},
|
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{
|
||
"data": {
|
||
"text/plain": [
|
||
"TrainOutput(global_step=3910, training_loss=0.24082870385835847, metrics={'train_runtime': 2416.0772, 'train_samples_per_second': 51.737, 'train_steps_per_second': 1.618, 'total_flos': 3.300271872e+16, 'train_loss': 0.24082870385835847, 'epoch': 5.0})"
|
||
]
|
||
},
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Configure training arguments\n",
|
||
"training_args = TrainingArguments(\"bert-lora-imdb\",\n",
|
||
" eval_strategy=\"epoch\",\n",
|
||
" per_device_train_batch_size=32, # decrease this for OOM error\n",
|
||
" per_device_eval_batch_size=64,\n",
|
||
" save_strategy=\"epoch\",\n",
|
||
" learning_rate=2e-3,\n",
|
||
" num_train_epochs=5,\n",
|
||
" weight_decay=0.01,\n",
|
||
" load_best_model_at_end=True,\n",
|
||
" do_eval=True,\n",
|
||
" do_predict=True,\n",
|
||
" metric_for_best_model=\"accuracy\",\n",
|
||
" report_to=\"none\")\n",
|
||
"\n",
|
||
"# Initialize the Trainer for the model training loop\n",
|
||
"trainer = Trainer(\n",
|
||
" model=model,\n",
|
||
" args=training_args,\n",
|
||
" train_dataset=train_dataset,\n",
|
||
" eval_dataset=eval_dataset,\n",
|
||
" compute_metrics=compute_metrics,\n",
|
||
")\n",
|
||
"\n",
|
||
"#start training\n",
|
||
"trainer.train()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "34h3g_eED25s"
|
||
},
|
||
"source": [
|
||
"### Now take the finetuned lora checkpoint and check the accuracy score on hellaswag task."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "7tgAq7nLD25s"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# use the path of your checkpoint here\n",
|
||
"output = lm_eval.simple_evaluate(model = 'hf',\n",
|
||
" model_args = {\n",
|
||
" 'pretrained' : 'bert-base-cased',\n",
|
||
" 'peft' : './bert-lora-imdb/checkpoint-3910',\n",
|
||
" 'dtype' : 'bfloat16'},\n",
|
||
" tasks = 'hellaswag',\n",
|
||
" device = device,\n",
|
||
" batch_size = 128,\n",
|
||
" log_samples = False)\n",
|
||
"\n",
|
||
"output[\"results\"]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
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"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
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"metadata": {
|
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"accelerator": "GPU",
|
||
"colab": {
|
||
"gpuType": "T4",
|
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|
||
},
|
||
"kaggle": {
|
||
"accelerator": "nvidiaTeslaT4",
|
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"dataSources": [],
|
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"dockerImageVersionId": 30787,
|
||
"isGpuEnabled": true,
|
||
"isInternetEnabled": true,
|
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"language": "python",
|
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"sourceType": "notebook"
|
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},
|
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"kernelspec": {
|
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"display_name": "Python 3 (ipykernel)",
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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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},
|
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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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