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GTimothee/reproduce-bug.ipynb

Created April 8, 2025 05:29
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reproduce-bug.ipynb
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},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/GTimothee/4be39baadc2c4f4bdbcade557417d1fc/reproduce-bug.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"# Setup"
],
"metadata": {
"id": "--IUzPVu0RSF"
}
},
{
"cell_type": "code",
"source": [
"!git clone https://github.com/GTimothee/transformers.git"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "BtxjZksR1POk",
"outputId": "1fef3968-d369-4407-876c-daca48e79fdd"
},
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Cloning into 'transformers'...\n",
"remote: Enumerating objects: 217899, done.\u001b[K\n",
"remote: Total 217899 (delta 0), reused 0 (delta 0), pack-reused 217899 (from 1)\u001b[K\n",
"Receiving objects: 100% (217899/217899), 247.16 MiB | 11.16 MiB/s, done.\n",
"Resolving deltas: 100% (159804/159804), done.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"%cd transformers"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "CFq2Rn2O1VAJ",
"outputId": "01761b75-8f4f-4fad-af5d-f8c3df8956bf"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"/content/transformers\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!git checkout sentencetransformers_load_peft\n",
"!git pull\n",
"%pip install ."
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ESg4BBCi1-vK",
"outputId": "d13651c5-7d95-49ca-cf06-1a55621d9909"
},
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Branch 'sentencetransformers_load_peft' set up to track remote branch 'sentencetransformers_load_peft' from 'origin'.\n",
"Switched to a new branch 'sentencetransformers_load_peft'\n",
"Already up to date.\n",
"Processing /content/transformers\n",
" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from transformers==4.51.0.dev0) (3.18.0)\n",
"Requirement already satisfied: huggingface-hub<1.0,>=0.26.0 in /usr/local/lib/python3.11/dist-packages (from transformers==4.51.0.dev0) (0.30.1)\n",
"Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.11/dist-packages (from transformers==4.51.0.dev0) (2.0.2)\n",
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from transformers==4.51.0.dev0) (24.2)\n",
"Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.11/dist-packages (from transformers==4.51.0.dev0) (6.0.2)\n",
"Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.11/dist-packages (from transformers==4.51.0.dev0) (2024.11.6)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from transformers==4.51.0.dev0) (2.32.3)\n",
"Requirement already satisfied: tokenizers<0.22,>=0.21 in /usr/local/lib/python3.11/dist-packages (from transformers==4.51.0.dev0) (0.21.1)\n",
"Requirement already satisfied: safetensors>=0.4.3 in /usr/local/lib/python3.11/dist-packages (from transformers==4.51.0.dev0) (0.5.3)\n",
"Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.11/dist-packages (from transformers==4.51.0.dev0) (4.67.1)\n",
"Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub<1.0,>=0.26.0->transformers==4.51.0.dev0) (2025.3.2)\n",
"Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub<1.0,>=0.26.0->transformers==4.51.0.dev0) (4.13.1)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests->transformers==4.51.0.dev0) (3.4.1)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests->transformers==4.51.0.dev0) (3.10)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests->transformers==4.51.0.dev0) (2.3.0)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests->transformers==4.51.0.dev0) (2025.1.31)\n",
"Building wheels for collected packages: transformers\n",
" Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for transformers: filename=transformers-4.51.0.dev0-py3-none-any.whl size=11129363 sha256=c69d38e8ec15ba32d8f06647514fa652a87d651417687406b1b79e43a8915060\n",
" Stored in directory: /tmp/pip-ephem-wheel-cache-atnwu2p0/wheels/9f/62/72/77fdff469e8308ad837268261590df9cabff9926cc4ab177c0\n",
"Successfully built transformers\n",
"Installing collected packages: transformers\n",
" Attempting uninstall: transformers\n",
" Found existing installation: transformers 4.50.3\n",
" Uninstalling transformers-4.50.3:\n",
" Successfully uninstalled transformers-4.50.3\n",
"Successfully installed transformers-4.51.0.dev0\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"%pip install datasets sentence-transformers wandb"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "H_VhZcR42wVY",
"outputId": "6f179705-ef9a-45fd-b96c-39c06e76cacc"
},
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting datasets\n",
" Downloading datasets-3.5.0-py3-none-any.whl.metadata (19 kB)\n",
"Requirement already satisfied: sentence-transformers in /usr/local/lib/python3.11/dist-packages (3.4.1)\n",
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"Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.11/dist-packages (from datasets) (18.1.0)\n",
"Collecting dill<0.3.9,>=0.3.0 (from datasets)\n",
" Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)\n",
"Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (from datasets) (2.2.2)\n",
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"Requirement already satisfied: tqdm>=4.66.3 in /usr/local/lib/python3.11/dist-packages (from datasets) (4.67.1)\n",
"Collecting xxhash (from datasets)\n",
" Downloading xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
"Collecting multiprocess<0.70.17 (from datasets)\n",
" Downloading multiprocess-0.70.16-py311-none-any.whl.metadata (7.2 kB)\n",
"Collecting fsspec<=2024.12.0,>=2023.1.0 (from fsspec[http]<=2024.12.0,>=2023.1.0->datasets)\n",
" Downloading fsspec-2024.12.0-py3-none-any.whl.metadata (11 kB)\n",
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"\u001b[?25hInstalling collected packages: xxhash, nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, fsspec, dill, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, nvidia-cusolver-cu12, datasets\n",
" Attempting uninstall: nvidia-nvjitlink-cu12\n",
" Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n",
" Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n",
" Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n",
" Attempting uninstall: nvidia-curand-cu12\n",
" Found existing installation: nvidia-curand-cu12 10.3.6.82\n",
" Uninstalling nvidia-curand-cu12-10.3.6.82:\n",
" Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n",
" Attempting uninstall: nvidia-cufft-cu12\n",
" Found existing installation: nvidia-cufft-cu12 11.2.3.61\n",
" Uninstalling nvidia-cufft-cu12-11.2.3.61:\n",
" Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n",
" Attempting uninstall: nvidia-cuda-runtime-cu12\n",
" Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n",
" Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n",
" Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n",
" Attempting uninstall: nvidia-cuda-nvrtc-cu12\n",
" Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n",
" Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n",
" Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n",
" Attempting uninstall: nvidia-cuda-cupti-cu12\n",
" Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n",
" Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n",
" Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n",
" Attempting uninstall: nvidia-cublas-cu12\n",
" Found existing installation: nvidia-cublas-cu12 12.5.3.2\n",
" Uninstalling nvidia-cublas-cu12-12.5.3.2:\n",
" Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n",
" Attempting uninstall: fsspec\n",
" Found existing installation: fsspec 2025.3.2\n",
" Uninstalling fsspec-2025.3.2:\n",
" Successfully uninstalled fsspec-2025.3.2\n",
" Attempting uninstall: nvidia-cusparse-cu12\n",
" Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n",
" Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n",
" Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n",
" Attempting uninstall: nvidia-cudnn-cu12\n",
" Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n",
" Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n",
" Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n",
" Attempting uninstall: nvidia-cusolver-cu12\n",
" Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n",
" Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n",
" Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n",
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"gcsfs 2025.3.2 requires fsspec==2025.3.2, but you have fsspec 2024.12.0 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[0mSuccessfully installed datasets-3.5.0 dill-0.3.8 fsspec-2024.12.0 multiprocess-0.70.16 nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127 xxhash-3.5.0\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# Prepare data\n"
],
"metadata": {
"id": "7uZFsDi5if_z"
}
},
{
"cell_type": "markdown",
"source": [
"code mostly copied from https://www.philschmid.de/fine-tune-embedding-model-for-rag"
],
"metadata": {
"id": "gZ_uW1DVnbZW"
}
},
{
"cell_type": "code",
"source": [
"from datasets import load_dataset\n",
"\n",
"# Load dataset from the hub\n",
"dataset = load_dataset(\"philschmid/finanical-rag-embedding-dataset\", split=\"train\")\n",
"\n",
"# rename columns\n",
"dataset = dataset.rename_column(\"question\", \"anchor\")\n",
"dataset = dataset.rename_column(\"context\", \"positive\")\n",
"\n",
"# Add an id column to the dataset\n",
"dataset = dataset.add_column(\"id\", range(len(dataset)))\n",
"\n",
"# split dataset into a 10% test set\n",
"dataset = dataset.train_test_split(test_size=0.1)\n",
"\n",
"# save datasets to disk\n",
"dataset[\"train\"].to_json(\"train_dataset.json\", orient=\"records\")\n",
"dataset[\"test\"].to_json(\"test_dataset.json\", orient=\"records\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 194,
"referenced_widgets": [
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},
"id": "KhCNyIWpiKzq",
"outputId": "77663b81-ea88-4bdb-cab3-f736e9efae66"
},
"execution_count": 7,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"README.md: 0%| | 0.00/882 [00:00<?, ?B/s]"
],
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},
{
"output_type": "display_data",
"data": {
"text/plain": [
"train-00000-of-00001.parquet: 0%| | 0.00/1.09M [00:00<?, ?B/s]"
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"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Generating train split: 0%| | 0/7000 [00:00<?, ? examples/s]"
],
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},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Creating json from Arrow format: 0%| | 0/7 [00:00<?, ?ba/s]"
],
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"version_major": 2,
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"metadata": {}
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{
"output_type": "display_data",
"data": {
"text/plain": [
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"metadata": {}
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{
"output_type": "execute_result",
"data": {
"text/plain": [
"243699"
]
},
"metadata": {},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"source": [
"from datasets import load_dataset, concatenate_datasets\n",
"train_dataset = load_dataset(\"json\", data_files=\"train_dataset.json\")['train'].select(range(500))\n",
"val_dataset = load_dataset(\"json\", data_files=\"test_dataset.json\")['train']\n",
"corpus_dataset = concatenate_datasets([train_dataset, val_dataset])\n",
"\n",
"corpus = dict(\n",
" zip(corpus_dataset[\"id\"], corpus_dataset[\"positive\"])\n",
") # Our corpus (cid => document)\n",
"queries = dict(\n",
" zip(val_dataset[\"id\"], val_dataset[\"anchor\"])\n",
") # Our queries (qid => question)\n",
"relevant_docs = {} # Query ID to relevant documents (qid => set([relevant_cids])\n",
"for q_id in queries:\n",
" relevant_docs[q_id] = [q_id]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 81,
"referenced_widgets": [
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]
},
"id": "IocKs1k-iqXe",
"outputId": "07bae16f-c402-42ce-e123-69e19bbbbfb8"
},
"execution_count": 8,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Generating train split: 0 examples [00:00, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "42674ed05d964c58b86a18e1fa6bd14c"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Generating train split: 0 examples [00:00, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "4ed71371153f4b4e96ab1b43339140f8"
}
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"from sentence_transformers.evaluation import (\n",
" InformationRetrievalEvaluator\n",
")\n",
"from sentence_transformers.util import cos_sim\n",
"ir_evaluator = InformationRetrievalEvaluator(\n",
" queries=queries,\n",
" corpus=corpus,\n",
" relevant_docs=relevant_docs,\n",
" score_functions={\"cosine\": cos_sim},\n",
" batch_size=32,\n",
" corpus_chunk_size=100,\n",
" show_progress_bar=True\n",
")"
],
"metadata": {
"id": "GAluOJ46i_ck"
},
"execution_count": 9,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Load model"
],
"metadata": {
"id": "tPUedvSS0iTQ"
}
},
{
"cell_type": "code",
"source": [
"from sentence_transformers import SentenceTransformer\n",
"from peft import LoraConfig, TaskType\n",
"import torch\n",
"\n",
"model = SentenceTransformer(\"all-MiniLM-L6-v2\", device=\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"peft_config = LoraConfig(\n",
" task_type=TaskType.FEATURE_EXTRACTION,\n",
" inference_mode=False,\n",
" r=8,\n",
" lora_alpha=32,\n",
" lora_dropout=0.1,\n",
")\n",
"model.add_adapter(peft_config)\n",
"\n",
"embedding = model.encode(\"[bla] my name is [blub]\")\n",
"print(embedding[:10])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 458,
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},
"id": "qQq-7SDHIhIA",
"outputId": "20c6c044-95f2-46b3-8c8f-35184f9156fe"
},
"execution_count": 10,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"modules.json: 0%| | 0.00/349 [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
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}
},
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},
{
"output_type": "display_data",
"data": {
"text/plain": [
"config_sentence_transformers.json: 0%| | 0.00/116 [00:00<?, ?B/s]"
],
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},
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},
{
"output_type": "display_data",
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"README.md: 0%| | 0.00/10.5k [00:00<?, ?B/s]"
],
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},
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},
{
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"text/plain": [
"sentence_bert_config.json: 0%| | 0.00/53.0 [00:00<?, ?B/s]"
],
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{
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}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\n",
"WARNING:huggingface_hub.file_download:Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"model.safetensors: 0%| | 0.00/90.9M [00:00<?, ?B/s]"
],
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"version_major": 2,
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}
},
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},
{
"output_type": "display_data",
"data": {
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"tokenizer_config.json: 0%| | 0.00/350 [00:00<?, ?B/s]"
],
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"version_major": 2,
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}
},
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},
{
"output_type": "display_data",
"data": {
"text/plain": [
"vocab.txt: 0%| | 0.00/232k [00:00<?, ?B/s]"
],
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"version_major": 2,
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}
},
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},
{
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],
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"version_major": 2,
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}
},
"metadata": {}
},
{
"output_type": "display_data",
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],
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"version_major": 2,
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}
},
"metadata": {}
},
{
"output_type": "display_data",
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"config.json: 0%| | 0.00/190 [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "b783d6ba6c874bd2a28c30ef841777db"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"[-0.05346014 -0.02178512 -0.00050324 -0.02896873 -0.09822001 0.00544652\n",
" 0.19883959 0.00645556 -0.04425739 -0.09591077]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from sentence_transformers import SentenceTransformerTrainingArguments\n",
"from sentence_transformers.training_args import BatchSamplers\n",
"\n",
"args = SentenceTransformerTrainingArguments(\n",
" output_dir=\"training_trial\",\n",
" num_train_epochs=.1,\n",
" per_device_train_batch_size=2,\n",
" gradient_accumulation_steps=1,\n",
" per_device_eval_batch_size=.5,\n",
" warmup_ratio=0.1,\n",
"\n",
" learning_rate=1e-5,\n",
" weight_decay=0.001,\n",
" fp16=True,\n",
" lr_scheduler_type='reduce_lr_on_plateau',\n",
" report_to=None,\n",
"\n",
" batch_sampler=BatchSamplers.NO_DUPLICATES,\n",
" eval_strategy=\"epoch\",\n",
" save_strategy=\"epoch\",\n",
" logging_steps=.2,\n",
" save_total_limit=3,\n",
" load_best_model_at_end=True,\n",
" metric_for_best_model=\"eval_cosine_ndcg@10\",\n",
")"
],
"metadata": {
"id": "y_cs61pEjaZl"
},
"execution_count": 11,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## it fails here:"
],
"metadata": {
"id": "SX6QzdOx34Bz"
}
},
{
"cell_type": "code",
"source": [
"from sentence_transformers import SentenceTransformerTrainer\n",
"from sentence_transformers.losses import MultipleNegativesRankingLoss\n",
"\n",
"trainer = SentenceTransformerTrainer(\n",
" model=model,\n",
" args=args,\n",
" train_dataset=train_dataset.select_columns(\n",
" [\"anchor\", \"positive\"]\n",
" ),\n",
" loss=MultipleNegativesRankingLoss(model),\n",
" evaluator=ir_evaluator\n",
")"
],
"metadata": {
"id": "c8Tz9Ea5jjqC",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 356
},
"outputId": "12f60e6a-ff33-4e3f-a31f-8f054a21cda7"
},
"execution_count": 12,
"outputs": [
{
"output_type": "error",
"ename": "AttributeError",
"evalue": "'SentenceTransformer' object has no attribute 'get_base_model'",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-12-83114cf871e2>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msentence_transformers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlosses\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mMultipleNegativesRankingLoss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m trainer = SentenceTransformerTrainer(\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/sentence_transformers/trainer.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, model, args, train_dataset, eval_dataset, loss, evaluator, data_collator, tokenizer, model_init, compute_metrics, callbacks, optimizers, preprocess_logits_for_metrics)\u001b[0m\n\u001b[1;32m 238\u001b[0m )\n\u001b[1;32m 239\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0msuper_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 241\u001b[0m \u001b[0;31m# If the eval_dataset is \"dummy\", then we set it back to None\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 242\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval_dataset\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"dummy\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/utils/deprecation.py\u001b[0m in \u001b[0;36mwrapped_func\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 170\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFutureWarning\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 171\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 172\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 173\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 174\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped_func\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, model, args, data_collator, train_dataset, eval_dataset, processing_class, model_init, compute_loss_func, compute_metrics, callbacks, optimizers, optimizer_cls_and_kwargs, preprocess_logits_for_metrics)\u001b[0m\n\u001b[1;32m 630\u001b[0m \u001b[0munwrapped_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 631\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0m_is_peft_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munwrapped_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 632\u001b[0;31m \u001b[0;32melse\u001b[0m \u001b[0munwrapped_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_base_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 633\u001b[0m )\n\u001b[1;32m 634\u001b[0m \u001b[0mforward_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minspect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_forward\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 1926\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodules\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1927\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mmodules\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1928\u001b[0;31m raise AttributeError(\n\u001b[0m\u001b[1;32m 1929\u001b[0m \u001b[0;34mf\"'{type(self).__name__}' object has no attribute '{name}'\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1930\u001b[0m )\n",
"\u001b[0;31mAttributeError\u001b[0m: 'SentenceTransformer' object has no attribute 'get_base_model'"
]
}
]
},
{
"cell_type": "code",
"source": [
"from transformers.utils.import_utils import is_peft_available\n",
"is_peft_available()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "2C1cttO1u-5z",
"outputId": "576d447a-f6af-4b75-f762-c128b552e321"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"True"
]
},
"metadata": {},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"source": [
"from packaging import version\n",
"import importlib\n",
"version.parse(importlib.metadata.version(\"peft\"))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "cRTVhKaLv9eI",
"outputId": "05a0438a-dc3c-462f-b56f-52e07258c216"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<Version('0.13.2')>"
]
},
"metadata": {},
"execution_count": 12
}
]
},
{
"cell_type": "code",
"source": [
"from transformers.integrations.peft import PeftAdapterMixin\n",
"print(isinstance(trainer.model, PeftAdapterMixin))\n",
"isinstance(trainer.model[0].auto_model, PeftAdapterMixin)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "tLwJvGx-vl_Z",
"outputId": "a582739d-13ba-42a0-ca01-321ee2a90df4"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"False\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"True"
]
},
"metadata": {},
"execution_count": 13
}
]
},
{
"cell_type": "code",
"source": [
"import os\n",
"os.environ[\"WANDB_MODE\"] = \"disabled\"\n",
"trainer.train()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000,
"referenced_widgets": [
"8e736d64da2c40e5a2cdc7589ca129ba",
"45eda66b57e94ec4aba3bd207bb36024",
"8c9e7db359d84090a8bd40a4664ba1ca",
"10df292c4e804c82a15b1d6d8b7950dd",
"fcbaa423da80492ea781ddc13f71991b",
"eaebcd4f49aa4167a7b2862b0465945a",
"65e68864fa69427595a929886198dc67",
"4f9487074ff94ddda63a840b99f04681",
"27d59d6e963e4d5aa4635dd09140c2cb",
"bf5b0d4783a84598b2b8b3a9ce5c4e45",
"64e8d8b617bd4f1fb32a8898ecd794ff",
"ca71283331214ab0afdc8a77dac9454c",
"86defa7dabde4c41858571fcda32f460",
"17a669cf751749f18b9ee20968484339",
"01c20836ac924fbab7bae9b142dd863d",
"ec6f2f88cc2848ae976f215f793c37ff",
"ea22eb89e678487e8afeef2e6a09e7fd",
"7f13cd279a38462eb725628d19386946",
"d762286f0e88437782492b4bf03a200a",
"9906453841434fcc816c847684c9bb3c",
"bdc478922407479e9ae2954f8ad6d26b",
"67bd57f216b344fba7bc692e6e9ab609"
]
},
"id": "FRBTKuNXjlND",
"outputId": "5134d9fb-84f8-41ff-9c09-edf540dab7dd"
},
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The `run_name` is currently set to the same value as `TrainingArguments.output_dir`. If this was not intended, please specify a different run name by setting the `TrainingArguments.run_name` parameter.\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='26' max='25' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [25/25 00:01, Epoch 0.10/1]\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>Cosine Accuracy@1</th>\n",
" <th>Cosine Accuracy@3</th>\n",
" <th>Cosine Accuracy@5</th>\n",
" <th>Cosine Accuracy@10</th>\n",
" <th>Cosine Precision@1</th>\n",
" <th>Cosine Precision@3</th>\n",
" <th>Cosine Precision@5</th>\n",
" <th>Cosine Precision@10</th>\n",
" <th>Cosine Recall@1</th>\n",
" <th>Cosine Recall@3</th>\n",
" <th>Cosine Recall@5</th>\n",
" <th>Cosine Recall@10</th>\n",
" <th>Cosine Ndcg@10</th>\n",
" <th>Cosine Mrr@10</th>\n",
" <th>Cosine Map@100</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>0.000300</td>\n",
" <td>No log</td>\n",
" <td>0.744286</td>\n",
" <td>0.865714</td>\n",
" <td>0.907143</td>\n",
" <td>0.942857</td>\n",
" <td>0.744286</td>\n",
" <td>0.288571</td>\n",
" <td>0.181429</td>\n",
" <td>0.094286</td>\n",
" <td>0.744286</td>\n",
" <td>0.865714</td>\n",
" <td>0.907143</td>\n",
" <td>0.942857</td>\n",
" <td>0.846290</td>\n",
" <td>0.814958</td>\n",
" <td>0.817527</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Batches: 0%| | 0/22 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "8e736d64da2c40e5a2cdc7589ca129ba"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Corpus Chunks: 100%|██████████| 12/12 [00:01<00:00, 9.54it/s]\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Computing widget examples: 0%| | 0/1 [00:00<?, ?example/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "ca71283331214ab0afdc8a77dac9454c"
}
},
"metadata": {}
},
{
"output_type": "error",
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: 'training_trial/checkpoint-25/pytorch_model.bin'",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-10-af73f3eb6fa7>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menviron\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"WANDB_MODE\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"disabled\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m 2243\u001b[0m \u001b[0mhf_hub_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_progress_bars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2244\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2245\u001b[0;31m return inner_training_loop(\n\u001b[0m\u001b[1;32m 2246\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2247\u001b[0m \u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m 2673\u001b[0m \u001b[0msmp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbarrier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2674\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2675\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_load_best_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2676\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2677\u001b[0m \u001b[0;31m# add remaining tr_loss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/content/sentence-transformers/sentence_transformers/trainer.py\u001b[0m in \u001b[0;36m_load_best_model\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 511\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 512\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 513\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_load_best_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 514\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 515\u001b[0m \u001b[0mloaded_auto_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_load_best_model\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 3005\u001b[0m \u001b[0mstate_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msafetensors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbest_safe_model_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"cpu\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3006\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3007\u001b[0;31m state_dict = torch.load(\n\u001b[0m\u001b[1;32m 3008\u001b[0m \u001b[0mbest_model_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3009\u001b[0m \u001b[0mmap_location\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"cpu\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(f, map_location, pickle_module, weights_only, mmap, **pickle_load_args)\u001b[0m\n\u001b[1;32m 1423\u001b[0m \u001b[0mpickle_load_args\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"encoding\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"utf-8\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1424\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1425\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0m_open_file_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mopened_file\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1426\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m_is_zipfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopened_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1427\u001b[0m \u001b[0;31m# The zipfile reader is going to advance the current file position.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36m_open_file_like\u001b[0;34m(name_or_buffer, mode)\u001b[0m\n\u001b[1;32m 749\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_open_file_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 750\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m_is_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname_or_buffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 751\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_open_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 752\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 753\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m\"w\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, name, mode)\u001b[0m\n\u001b[1;32m 730\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0m_open_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_opener\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 731\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 732\u001b[0;31m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 733\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 734\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__exit__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'training_trial/checkpoint-25/pytorch_model.bin'"
]
}
]
},
{
"cell_type": "code",
"source": [
"trainer.model[0].auto_model.active_adapters()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "U00MrJ936jv4",
"outputId": "8c53633f-1626-48e7-a158-863de0e234b1"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['default']"
]
},
"metadata": {},
"execution_count": 15
}
]
},
{
"cell_type": "code",
"source": [
"# # Save the adapter itself\n",
"# model.save_pretrained(\"all-MiniLM-L6-v2-adapter\")\n",
"# # Load the adapter directly\n",
"# loaded_model = SentenceTransformer(\"all-MiniLM-L6-v2-adapter\")\n",
"# embedding = loaded_model.encode(\"[bla] my name is [blub]\")\n",
"# print(embedding[:10])"
],
"metadata": {
"id": "vuCJvdjIIZOk"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "npfoGXz9Iqn6"
},
"execution_count": null,
"outputs": []
}
]
}
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