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https://gitee.com/fastnlp/fastNLP.git
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add example-12 lxr 220526
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lxr-tech
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1ec3337a49
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}
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],
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"source": [
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"import sys\n",
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"sys.path.append('..')\n",
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"\n",
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"from fastNLP import Trainer\n",
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"\n",
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"trainer = Trainer(\n",
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@ -613,11 +616,11 @@
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{
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"data": {
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'acc#acc'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.41</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'total#acc'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">100.0</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'correct#acc'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">41.0</span><span style=\"font-weight: bold\">}</span>\n",
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'acc#acc'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.37</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'total#acc'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">100.0</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'correct#acc'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">37.0</span><span style=\"font-weight: bold\">}</span>\n",
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"</pre>\n"
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],
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"text/plain": [
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"\u001b[1m{\u001b[0m\u001b[32m'acc#acc'\u001b[0m: \u001b[1;36m0.41\u001b[0m, \u001b[32m'total#acc'\u001b[0m: \u001b[1;36m100.0\u001b[0m, \u001b[32m'correct#acc'\u001b[0m: \u001b[1;36m41.0\u001b[0m\u001b[1m}\u001b[0m\n"
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"\u001b[1m{\u001b[0m\u001b[32m'acc#acc'\u001b[0m: \u001b[1;36m0.37\u001b[0m, \u001b[32m'total#acc'\u001b[0m: \u001b[1;36m100.0\u001b[0m, \u001b[32m'correct#acc'\u001b[0m: \u001b[1;36m37.0\u001b[0m\u001b[1m}\u001b[0m\n"
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]
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},
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"metadata": {},
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@ -626,7 +629,7 @@
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{
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"data": {
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"text/plain": [
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"{'acc#acc': 0.41, 'total#acc': 100.0, 'correct#acc': 41.0}"
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"{'acc#acc': 0.37, 'total#acc': 100.0, 'correct#acc': 37.0}"
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]
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},
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"execution_count": 9,
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{
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"data": {
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"text/plain": [
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"{'acc#acc': 0.46, 'total#acc': 100.0, 'correct#acc': 46.0}"
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"{'acc#acc': 0.47, 'total#acc': 100.0, 'correct#acc': 47.0}"
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]
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},
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"execution_count": 12,
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@ -793,7 +796,7 @@
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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": "3.7.4"
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"version": "3.7.13"
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},
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"pycharm": {
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"stem_cell": {
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888
tutorials/fastnlp_tutorial_e1.ipynb
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888
tutorials/fastnlp_tutorial_e1.ipynb
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@ -0,0 +1,888 @@
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{
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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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"source": [
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"# E1. 使用 DistilBert 完成 SST2 分类"
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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": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
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"</pre>\n"
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],
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"text/plain": [
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"\n"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"4.18.0\n"
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]
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}
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],
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"source": [
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"import torch\n",
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"import torch.nn as nn\n",
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"from torch.optim import AdamW\n",
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"from torch.utils.data import DataLoader, Dataset\n",
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"\n",
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"import transformers\n",
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"from transformers import AutoTokenizer\n",
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"from transformers import AutoModelForSequenceClassification\n",
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"\n",
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"import sys\n",
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"sys.path.append('..')\n",
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"\n",
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"import fastNLP\n",
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"from fastNLP import Trainer\n",
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"from fastNLP.core.utils.utils import dataclass_to_dict\n",
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"from fastNLP.core.metrics import Accuracy\n",
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"\n",
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"print(transformers.__version__)"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"GLUE_TASKS = [\"cola\", \"mnli\", \"mnli-mm\", \"mrpc\", \"qnli\", \"qqp\", \"rte\", \"sst2\", \"stsb\", \"wnli\"]\n",
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"\n",
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"task = \"sst2\"\n",
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"model_checkpoint = \"distilbert-base-uncased\""
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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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"scrolled": false
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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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"Using the latest cached version of the module from /remote-home/xrliu/.cache/huggingface/modules/datasets_modules/datasets/glue/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad (last modified on Thu May 26 15:30:15 2022) since it couldn't be found locally at glue., or remotely on the Hugging Face Hub.\n",
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"Reusing dataset glue (/remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\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": "253d79d7a67e4dc88338448b5bcb3fb9",
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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%| | 0/3 [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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],
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"source": [
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"from datasets import load_dataset, load_metric\n",
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"\n",
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"dataset = load_dataset(\"glue\", \"mnli\" if task == \"mnli-mm\" else task)"
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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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"metadata": {},
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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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"{'input_ids': [101, 7592, 1010, 2023, 2028, 6251, 999, 102, 1998, 2023, 6251, 3632, 2007, 2009, 1012, 102], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\n"
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]
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}
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],
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"source": [
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"tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True)\n",
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"\n",
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"print(tokenizer(\"Hello, this one sentence!\", \"And this sentence goes with it.\"))"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"task_to_keys = {\n",
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" \"cola\": (\"sentence\", None),\n",
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" \"mnli\": (\"premise\", \"hypothesis\"),\n",
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" \"mnli-mm\": (\"premise\", \"hypothesis\"),\n",
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" \"mrpc\": (\"sentence1\", \"sentence2\"),\n",
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" \"qnli\": (\"question\", \"sentence\"),\n",
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" \"qqp\": (\"question1\", \"question2\"),\n",
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" \"rte\": (\"sentence1\", \"sentence2\"),\n",
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" \"sst2\": (\"sentence\", None),\n",
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" \"stsb\": (\"sentence1\", \"sentence2\"),\n",
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" \"wnli\": (\"sentence1\", \"sentence2\"),\n",
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"}\n",
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"\n",
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"sentence1_key, sentence2_key = task_to_keys[task]"
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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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"metadata": {},
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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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"Sentence: hide new secretions from the parental units \n"
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]
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}
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],
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"source": [
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"if sentence2_key is None:\n",
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" print(f\"Sentence: {dataset['train'][0][sentence1_key]}\")\n",
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"else:\n",
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" print(f\"Sentence 1: {dataset['train'][0][sentence1_key]}\")\n",
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" print(f\"Sentence 2: {dataset['train'][0][sentence2_key]}\")"
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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": 7,
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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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"Loading cached processed dataset at /remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-ca1fbe5e8eb059f3.arrow\n",
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"Loading cached processed dataset at /remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-03661263fbf302f5.arrow\n",
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"Loading cached processed dataset at /remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-fbe8e7a4e4f18f45.arrow\n"
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]
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}
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],
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"source": [
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"def preprocess_function(examples):\n",
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" if sentence2_key is None:\n",
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" return tokenizer(examples[sentence1_key], truncation=True)\n",
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" return tokenizer(examples[sentence1_key], examples[sentence2_key], truncation=True)\n",
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"\n",
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"encoded_dataset = dataset.map(preprocess_function, batched=True)"
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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": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"class ClassModel(nn.Module):\n",
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" def __init__(self, num_labels, model_checkpoint):\n",
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" nn.Module.__init__(self)\n",
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" self.num_labels = num_labels\n",
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" self.back_bone = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, \n",
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" num_labels=num_labels)\n",
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" self.loss_fn = nn.CrossEntropyLoss()\n",
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"\n",
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" def forward(self, input_ids, attention_mask):\n",
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" return self.back_bone(input_ids, attention_mask)\n",
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"\n",
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" def train_step(self, input_ids, attention_mask, labels):\n",
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" pred = self(input_ids, attention_mask).logits\n",
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" return {\"loss\": self.loss_fn(pred, labels)}\n",
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"\n",
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" def evaluate_step(self, input_ids, attention_mask, labels):\n",
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" pred = self(input_ids, attention_mask).logits\n",
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" pred = torch.max(pred, dim=-1)[1]\n",
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" return {\"pred\": pred, \"target\": labels}"
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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": 9,
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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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"Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_projector.weight', 'vocab_layer_norm.bias', 'vocab_transform.bias', 'vocab_projector.bias', 'vocab_layer_norm.weight', 'vocab_transform.weight']\n",
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"- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
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"- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
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"Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.weight', 'classifier.weight', 'classifier.bias', 'pre_classifier.bias']\n",
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"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
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]
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}
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],
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"source": [
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"num_labels = 3 if task.startswith(\"mnli\") else 1 if task==\"stsb\" else 2\n",
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"\n",
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"model = ClassModel(num_labels=num_labels, model_checkpoint=model_checkpoint)\n",
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"\n",
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"optimizers = AdamW(params=model.parameters(), lr=5e-5)"
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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": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"class TestDistilBertDataset(Dataset):\n",
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" def __init__(self, dataset):\n",
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" super(TestDistilBertDataset, self).__init__()\n",
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" self.dataset = dataset\n",
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"\n",
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" def __len__(self):\n",
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" return len(self.dataset)\n",
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"\n",
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" def __getitem__(self, item):\n",
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" item = self.dataset[item]\n",
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" return item[\"input_ids\"], item[\"attention_mask\"], [item[\"label\"]] "
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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": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"def test_bert_collate_fn(batch):\n",
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" input_ids, atten_mask, labels = [], [], []\n",
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" max_length = [0] * 3\n",
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" for each_item in batch:\n",
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" input_ids.append(each_item[0])\n",
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" max_length[0] = max(max_length[0], len(each_item[0]))\n",
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" atten_mask.append(each_item[1])\n",
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" max_length[1] = max(max_length[1], len(each_item[1]))\n",
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" labels.append(each_item[2])\n",
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" max_length[2] = max(max_length[2], len(each_item[2]))\n",
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"\n",
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" for i in range(3):\n",
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" each = (input_ids, atten_mask, labels)[i]\n",
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" for item in each:\n",
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" item.extend([0] * (max_length[i] - len(item)))\n",
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" return {\"input_ids\": torch.cat([torch.tensor([item]) for item in input_ids], dim=0),\n",
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" \"attention_mask\": torch.cat([torch.tensor([item]) for item in atten_mask], dim=0),\n",
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" \"labels\": torch.cat([torch.tensor(item) for item in labels], dim=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": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"dataset_train = TestDistilBertDataset(encoded_dataset[\"train\"])\n",
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"dataloader_train = DataLoader(dataset=dataset_train, \n",
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" batch_size=32, shuffle=True, collate_fn=test_bert_collate_fn)\n",
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"dataset_valid = TestDistilBertDataset(encoded_dataset[\"validation\"])\n",
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"dataloader_valid = DataLoader(dataset=dataset_valid, \n",
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" batch_size=32, shuffle=False, collate_fn=test_bert_collate_fn)"
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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": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"trainer = Trainer(\n",
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" model=model,\n",
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" driver='torch',\n",
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" device='cuda',\n",
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" n_epochs=10,\n",
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" optimizers=optimizers,\n",
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" train_dataloader=dataloader_train,\n",
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" evaluate_dataloaders=dataloader_valid,\n",
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" metrics={'acc': Accuracy()}\n",
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")"
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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": 14,
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"metadata": {},
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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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"data": {
|
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"text/html": [
|
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|
||||
],
|
||||
"text/plain": []
|
||||
},
|
||||
"metadata": {},
|
||||
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|
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|
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|
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"data": {
|
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|
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|
||||
"</pre>\n"
|
||||
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|
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|
||||
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|
||||
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|
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},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"trainer.run(num_eval_batch_per_dl=10)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.13"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
888
tutorials/fastnlp_tutorial_e2.ipynb
Normal file
888
tutorials/fastnlp_tutorial_e2.ipynb
Normal file
@ -0,0 +1,888 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# E2. 使用 PrefixTuning 完成 SST2 分类"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
|
||||
"</pre>\n"
|
||||
],
|
||||
"text/plain": [
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"4.18.0\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import torch\n",
|
||||
"import torch.nn as nn\n",
|
||||
"from torch.optim import AdamW\n",
|
||||
"from torch.utils.data import DataLoader, Dataset\n",
|
||||
"\n",
|
||||
"import transformers\n",
|
||||
"from transformers import AutoTokenizer\n",
|
||||
"from transformers import AutoModelForSequenceClassification\n",
|
||||
"\n",
|
||||
"import sys\n",
|
||||
"sys.path.append('..')\n",
|
||||
"\n",
|
||||
"import fastNLP\n",
|
||||
"from fastNLP import Trainer\n",
|
||||
"from fastNLP.core.utils.utils import dataclass_to_dict\n",
|
||||
"from fastNLP.core.metrics import Accuracy\n",
|
||||
"\n",
|
||||
"print(transformers.__version__)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"GLUE_TASKS = [\"cola\", \"mnli\", \"mnli-mm\", \"mrpc\", \"qnli\", \"qqp\", \"rte\", \"sst2\", \"stsb\", \"wnli\"]\n",
|
||||
"\n",
|
||||
"task = \"sst2\"\n",
|
||||
"model_checkpoint = \"distilbert-base-uncased\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Using the latest cached version of the module from /remote-home/xrliu/.cache/huggingface/modules/datasets_modules/datasets/glue/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad (last modified on Thu May 26 15:30:15 2022) since it couldn't be found locally at glue., or remotely on the Hugging Face Hub.\n",
|
||||
"Reusing dataset glue (/remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "253d79d7a67e4dc88338448b5bcb3fb9",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
" 0%| | 0/3 [00:00<?, ?it/s]"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from datasets import load_dataset, load_metric\n",
|
||||
"\n",
|
||||
"dataset = load_dataset(\"glue\", \"mnli\" if task == \"mnli-mm\" else task)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"{'input_ids': [101, 7592, 1010, 2023, 2028, 6251, 999, 102, 1998, 2023, 6251, 3632, 2007, 2009, 1012, 102], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True)\n",
|
||||
"\n",
|
||||
"print(tokenizer(\"Hello, this one sentence!\", \"And this sentence goes with it.\"))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"task_to_keys = {\n",
|
||||
" \"cola\": (\"sentence\", None),\n",
|
||||
" \"mnli\": (\"premise\", \"hypothesis\"),\n",
|
||||
" \"mnli-mm\": (\"premise\", \"hypothesis\"),\n",
|
||||
" \"mrpc\": (\"sentence1\", \"sentence2\"),\n",
|
||||
" \"qnli\": (\"question\", \"sentence\"),\n",
|
||||
" \"qqp\": (\"question1\", \"question2\"),\n",
|
||||
" \"rte\": (\"sentence1\", \"sentence2\"),\n",
|
||||
" \"sst2\": (\"sentence\", None),\n",
|
||||
" \"stsb\": (\"sentence1\", \"sentence2\"),\n",
|
||||
" \"wnli\": (\"sentence1\", \"sentence2\"),\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"sentence1_key, sentence2_key = task_to_keys[task]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Sentence: hide new secretions from the parental units \n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"if sentence2_key is None:\n",
|
||||
" print(f\"Sentence: {dataset['train'][0][sentence1_key]}\")\n",
|
||||
"else:\n",
|
||||
" print(f\"Sentence 1: {dataset['train'][0][sentence1_key]}\")\n",
|
||||
" print(f\"Sentence 2: {dataset['train'][0][sentence2_key]}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Loading cached processed dataset at /remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-ca1fbe5e8eb059f3.arrow\n",
|
||||
"Loading cached processed dataset at /remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-03661263fbf302f5.arrow\n",
|
||||
"Loading cached processed dataset at /remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-fbe8e7a4e4f18f45.arrow\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"def preprocess_function(examples):\n",
|
||||
" if sentence2_key is None:\n",
|
||||
" return tokenizer(examples[sentence1_key], truncation=True)\n",
|
||||
" return tokenizer(examples[sentence1_key], examples[sentence2_key], truncation=True)\n",
|
||||
"\n",
|
||||
"encoded_dataset = dataset.map(preprocess_function, batched=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class ClassModel(nn.Module):\n",
|
||||
" def __init__(self, num_labels, model_checkpoint):\n",
|
||||
" nn.Module.__init__(self)\n",
|
||||
" self.num_labels = num_labels\n",
|
||||
" self.back_bone = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, \n",
|
||||
" num_labels=num_labels)\n",
|
||||
" self.loss_fn = nn.CrossEntropyLoss()\n",
|
||||
"\n",
|
||||
" def forward(self, input_ids, attention_mask):\n",
|
||||
" return self.back_bone(input_ids, attention_mask)\n",
|
||||
"\n",
|
||||
" def train_step(self, input_ids, attention_mask, labels):\n",
|
||||
" pred = self(input_ids, attention_mask).logits\n",
|
||||
" return {\"loss\": self.loss_fn(pred, labels)}\n",
|
||||
"\n",
|
||||
" def evaluate_step(self, input_ids, attention_mask, labels):\n",
|
||||
" pred = self(input_ids, attention_mask).logits\n",
|
||||
" pred = torch.max(pred, dim=-1)[1]\n",
|
||||
" return {\"pred\": pred, \"target\": labels}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_projector.weight', 'vocab_layer_norm.bias', 'vocab_transform.bias', 'vocab_projector.bias', 'vocab_layer_norm.weight', 'vocab_transform.weight']\n",
|
||||
"- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
|
||||
"- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
|
||||
"Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.weight', 'classifier.weight', 'classifier.bias', 'pre_classifier.bias']\n",
|
||||
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"num_labels = 3 if task.startswith(\"mnli\") else 1 if task==\"stsb\" else 2\n",
|
||||
"\n",
|
||||
"model = ClassModel(num_labels=num_labels, model_checkpoint=model_checkpoint)\n",
|
||||
"\n",
|
||||
"optimizers = AdamW(params=model.parameters(), lr=5e-5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class TestDistilBertDataset(Dataset):\n",
|
||||
" def __init__(self, dataset):\n",
|
||||
" super(TestDistilBertDataset, self).__init__()\n",
|
||||
" self.dataset = dataset\n",
|
||||
"\n",
|
||||
" def __len__(self):\n",
|
||||
" return len(self.dataset)\n",
|
||||
"\n",
|
||||
" def __getitem__(self, item):\n",
|
||||
" item = self.dataset[item]\n",
|
||||
" return item[\"input_ids\"], item[\"attention_mask\"], [item[\"label\"]] "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def test_bert_collate_fn(batch):\n",
|
||||
" input_ids, atten_mask, labels = [], [], []\n",
|
||||
" max_length = [0] * 3\n",
|
||||
" for each_item in batch:\n",
|
||||
" input_ids.append(each_item[0])\n",
|
||||
" max_length[0] = max(max_length[0], len(each_item[0]))\n",
|
||||
" atten_mask.append(each_item[1])\n",
|
||||
" max_length[1] = max(max_length[1], len(each_item[1]))\n",
|
||||
" labels.append(each_item[2])\n",
|
||||
" max_length[2] = max(max_length[2], len(each_item[2]))\n",
|
||||
"\n",
|
||||
" for i in range(3):\n",
|
||||
" each = (input_ids, atten_mask, labels)[i]\n",
|
||||
" for item in each:\n",
|
||||
" item.extend([0] * (max_length[i] - len(item)))\n",
|
||||
" return {\"input_ids\": torch.cat([torch.tensor([item]) for item in input_ids], dim=0),\n",
|
||||
" \"attention_mask\": torch.cat([torch.tensor([item]) for item in atten_mask], dim=0),\n",
|
||||
" \"labels\": torch.cat([torch.tensor(item) for item in labels], dim=0)}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"dataset_train = TestDistilBertDataset(encoded_dataset[\"train\"])\n",
|
||||
"dataloader_train = DataLoader(dataset=dataset_train, \n",
|
||||
" batch_size=32, shuffle=True, collate_fn=test_bert_collate_fn)\n",
|
||||
"dataset_valid = TestDistilBertDataset(encoded_dataset[\"validation\"])\n",
|
||||
"dataloader_valid = DataLoader(dataset=dataset_valid, \n",
|
||||
" batch_size=32, shuffle=False, collate_fn=test_bert_collate_fn)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"trainer = Trainer(\n",
|
||||
" model=model,\n",
|
||||
" driver='torch',\n",
|
||||
" device='cuda',\n",
|
||||
" n_epochs=10,\n",
|
||||
" optimizers=optimizers,\n",
|
||||
" train_dataloader=dataloader_train,\n",
|
||||
" evaluate_dataloaders=dataloader_valid,\n",
|
||||
" metrics={'acc': Accuracy()}\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# help(model.back_bone.forward)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
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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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}
|
||||
Loading…
Reference in New Issue
Block a user