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GTimothee/load-and-save-embed-model.ipynb

Created November 14, 2024 17:09
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load-and-save-embed-model.ipynb
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"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
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},
"source": [
"<a href=\"https://colab.research.google.com/gist/GTimothee/cb3551ba6eb14b04f7a06d63ea4616f9/load-and-save-embed-model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"source": [
"!pip install datasets\n",
"!pip install --upgrade sentence-transformers"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"collapsed": true,
"id": "pa9wkGU7i21H",
"outputId": "d2b055b2-70a2-4b79-b57a-7e0e2aad8f1e"
},
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting datasets\n",
" Downloading datasets-3.1.0-py3-none-any.whl.metadata (20 kB)\n",
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" Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)\n",
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" Downloading xxhash-3.5.0-cp310-cp310-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-py310-none-any.whl.metadata (7.2 kB)\n",
"Collecting fsspec<=2024.9.0,>=2023.1.0 (from fsspec[http]<=2024.9.0,>=2023.1.0->datasets)\n",
" Downloading fsspec-2024.9.0-py3-none-any.whl.metadata (11 kB)\n",
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"\u001b[?25hInstalling collected packages: xxhash, fsspec, dill, multiprocess, datasets\n",
" Attempting uninstall: fsspec\n",
" Found existing installation: fsspec 2024.10.0\n",
" Uninstalling fsspec-2024.10.0:\n",
" Successfully uninstalled fsspec-2024.10.0\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 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[0mSuccessfully installed datasets-3.1.0 dill-0.3.8 fsspec-2024.9.0 multiprocess-0.70.16 xxhash-3.5.0\n",
"Requirement already satisfied: sentence-transformers in /usr/local/lib/python3.10/dist-packages (3.2.1)\n",
"Collecting sentence-transformers\n",
" Downloading sentence_transformers-3.3.0-py3-none-any.whl.metadata (10 kB)\n",
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"Downloading sentence_transformers-3.3.0-py3-none-any.whl (268 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.7/268.7 kB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hInstalling collected packages: sentence-transformers\n",
" Attempting uninstall: sentence-transformers\n",
" Found existing installation: sentence-transformers 3.2.1\n",
" Uninstalling sentence-transformers-3.2.1:\n",
" Successfully uninstalled sentence-transformers-3.2.1\n",
"Successfully installed sentence-transformers-3.3.0\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# case where we want to add tokens"
],
"metadata": {
"id": "vEd-C3T8mwWJ"
}
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "9liyq3bLICqc"
},
"outputs": [],
"source": [
"# from sentence_transformers import SentenceTransformer\n",
"# from peft import LoraConfig, TaskType\n",
"\n",
"# test_new_tokens = [\"[bla]\", \"[blub]\", \"[bloo]\", \"[blib]\"]\n",
"\n",
"# # Create a resized base model\n",
"# model = SentenceTransformer(\"all-MiniLM-L6-v2\")\n",
"# model.tokenizer.add_tokens(test_new_tokens)\n",
"# model[0].auto_model.resize_token_embeddings(len(model.tokenizer))\n",
"# model.save_pretrained(\"all-MiniLM-L6-v2-resized\")"
]
},
{
"cell_type": "markdown",
"source": [
"# Training"
],
"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": 215,
"referenced_widgets": [
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"b1a91a365f3a45f7b44b79ccb2ce77ba",
"fd8ecc5a054942149654358cf9e2ec17",
"5fd450cbada54c62bbefc6f4734a89c5",
"efe02cf429ad4d44ae818b0dac9d92ca",
"b5e7f9ca0cfe4415a7f01dd209374146",
"041d5fe152ef4cab8ead5a9fc2726c61",
"29f27034291447c48314acdb49bf873d",
"09152f0b1434472682302a8f614ac43d",
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"a2055efc2050458ab84ebf61bc95ac98",
"cee1c4bbebce4f2a8e7f75edabdcd9ce",
"ae67ccd94a544b3f95e5f2c817e51b6c",
"1c50180af28d4bed84162aa4579a84d3",
"b736bddf47624d0caf41c6c10c0a7cc7",
"68e0d6fbcdbd449b8b929d870cb7c1a1",
"88b0f1189e484111be5429d4edf8e75c",
"12439f806a57479bba74bcc3a4c3866d",
"8cdcc4a75d4c4881829f7f0bd0762e98",
"62a80de00a2443e2a325f4ec0bb2ac9e",
"b6ce7b55d3d34218b950a429fd5c9126",
"85a6e4ec1a144e7481a84a947a47380f",
"0e89752fd2d145e7a9fa4c6196c4057b",
"38ea66bfbc8d43a4a8842016f14e6d80",
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"b1197cca631b42a6adbf1e9ada917a90",
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"be5692f3014c48ed83bb822a21f22626",
"ba0a764c7b9841ad87bbe1a367539423",
"e46320f6df5644ab9ef1ded1537f80db",
"5bca1e03a8324d36918f85493c8ac313",
"9532b0e03a414582b06849b01983da04",
"206af04e368c43f595d575c8cf5e17a7",
"152cdc33f25641fe86ca15cd68e3bfb2",
"b14b9845e4454d6b849dbccc939156b2",
"1a5346f7c92941e4ad3ecd96b774bb42",
"64a4c4af79cf42859d519fffaa86be8a",
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"3d211fa50f7e432aa0fe2fb9482c157d",
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"fd703f4046814e89ad550bf00a370dfc",
"c3035f020f6e430e840fb5ce998f2032",
"e683fe0b747849caacc8cf93106c1ea1",
"dd584dcdba8544fdabe5ebd0ac3130a6",
"3faa44446e684f3fbaec3622875c93aa"
]
},
"id": "KhCNyIWpiKzq",
"outputId": "47cc5d20-eaaa-45e6-c445-10d3a5063424"
},
"execution_count": 3,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"README.md: 0%| | 0.00/882 [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "9cbddb365876477396610bafe3114c00"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"train-00000-of-00001.parquet: 0%| | 0.00/1.09M [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "916b53848a2e45198d1ea3afdcd4e3c5"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Generating train split: 0%| | 0/7000 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "62a80de00a2443e2a325f4ec0bb2ac9e"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Creating json from Arrow format: 0%| | 0/7 [00:00<?, ?ba/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "f64e849e8ce742c0a68dfe942f1e8f7a"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Creating json from Arrow format: 0%| | 0/1 [00:00<?, ?ba/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "64a4c4af79cf42859d519fffaa86be8a"
}
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"242472"
]
},
"metadata": {},
"execution_count": 3
}
]
},
{
"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": 101,
"referenced_widgets": [
"80e92517c9f145a39a99e33e1e16e2d1",
"c4ba4c497256469aa2dec03033ddb4a9",
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"2d90e43a971f4eacab56ce3e494dfcde",
"de7e82f565a3499681b70e019c73419b",
"a1a0106be92c4e3299ccf69ed3f5ed53",
"d681e917c28246c0a21c2b119a3b7cdf",
"d934caa6fc994347b98a867277958890",
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]
},
"id": "IocKs1k-iqXe",
"outputId": "352b3cd7-0566-4bdc-90d6-876fb63350f1"
},
"execution_count": 4,
"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": "80e92517c9f145a39a99e33e1e16e2d1"
}
},
"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": "70b5083ce95a4a04b9f2f3d308af4ad1"
}
},
"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": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 87,
"referenced_widgets": [
"055a26e0b776423da6981e229d86c921",
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]
},
"id": "GAluOJ46i_ck",
"outputId": "edcfaec7-9a3a-4c4f-cffe-97f58616d24e"
},
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"0it [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "055a26e0b776423da6981e229d86c921"
}
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"from sentence_transformers import SentenceTransformer\n",
"from peft import LoraConfig, TaskType\n",
"import torch\n",
"\n",
"# Load the resized model and add an adapter\n",
"# model = SentenceTransformer(\"all-MiniLM-L6-v2-resized\")\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": 425,
"referenced_widgets": [
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]
},
"id": "qQq-7SDHIhIA",
"outputId": "2a5ba1d2-6400-409c-d54b-b89767f3a23d"
},
"execution_count": 7,
"outputs": [
{
"output_type": "display_data",
"data": {
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"modules.json: 0%| | 0.00/349 [00:00<?, ?B/s]"
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"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": [
"results = ir_evaluator(model)\n",
"results"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 357,
"referenced_widgets": [
"01df1dcefc054dedae762cb7f181ddbd",
"01774c8db33b42ba9198199c9b89e633",
"626320c80eb84c77bffd20385a2d53e2",
"21b7126ac4c643e4a971550b432d8e5b",
"23a4656e337e41b88da26c23c033e05c",
"b5cc6abce44a476196ac72a82d8e24da",
"0bc2ace14bb94596b93f374b85c53eab",
"a58717641b90407492a872bb3e616600",
"50953885582d495782924e5278512857",
"445f031729144ee6ac6279ee8e48ab45",
"5c7c2a39c5584fdcbf6dc6de323f0421"
]
},
"id": "IF63o9kbljdb",
"outputId": "9964243e-4e11-4d34-9ed5-1012d72d0bbe"
},
"execution_count": 8,
"outputs": [
{
"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": "01df1dcefc054dedae762cb7f181ddbd"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Corpus Chunks: 100%|██████████| 12/12 [00:01<00:00, 7.58it/s]\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'cosine_accuracy@1': 0.7842857142857143,\n",
" 'cosine_accuracy@3': 0.8914285714285715,\n",
" 'cosine_accuracy@5': 0.9171428571428571,\n",
" 'cosine_accuracy@10': 0.9457142857142857,\n",
" 'cosine_precision@1': 0.7842857142857143,\n",
" 'cosine_precision@3': 0.29714285714285715,\n",
" 'cosine_precision@5': 0.18342857142857139,\n",
" 'cosine_precision@10': 0.09457142857142856,\n",
" 'cosine_recall@1': 0.7842857142857143,\n",
" 'cosine_recall@3': 0.8914285714285715,\n",
" 'cosine_recall@5': 0.9171428571428571,\n",
" 'cosine_recall@10': 0.9457142857142857,\n",
" 'cosine_ndcg@10': 0.8678902907115743,\n",
" 'cosine_mrr@10': 0.842611111111111,\n",
" 'cosine_map@100': 0.8451141243804458}"
]
},
"metadata": {},
"execution_count": 8
}
]
},
{
"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": 9,
"outputs": []
},
{
"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"
},
"execution_count": 10,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import os\n",
"os.environ[\"WANDB_MODE\"] = \"disabled\"\n",
"trainer.train()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 598,
"referenced_widgets": [
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"f4884675f6f84016af0fadc9817a251c",
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"b43abb72c0654d6a862410bab00672ab",
"e0a2a1c4c10e4887bd6a27ae466f3651",
"409af25ed298495aba779d3989b0d52b",
"9b515ddee3b24540998dc6e2080ec5f1",
"842604dd1db9498fa7e826f375514479",
"5aa2596669754c8d81aa6bfedab007a9",
"1741b8aca8d34f2eafef7e0452efaa3c",
"b511fcad5c5b496283d7e1e05f8c6db7",
"b38f8c334d5d4d869d42df75e0a36499",
"c3e59487742446dca9c607090ef46f77",
"a365cc7737a84504839836a6b24da465",
"e676e64e9af445f7b9fbcd59a0c007e9",
"5c5161841c0c42a295f0ceefaebffa09",
"ddb20b36016e483f9549fdc1ebdebf26",
"246566b5287842a29dfde452936afc38"
]
},
"id": "FRBTKuNXjlND",
"outputId": "1673e970-4bf1-4ff9-af82-87b2dbb06694"
},
"execution_count": 11,
"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='251' max='250' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [250/250 00:12, Epoch 1/1]\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>1</td>\n",
" <td>0.012000</td>\n",
" <td>No log</td>\n",
" <td>0.781429</td>\n",
" <td>0.894286</td>\n",
" <td>0.917143</td>\n",
" <td>0.945714</td>\n",
" <td>0.781429</td>\n",
" <td>0.298095</td>\n",
" <td>0.183429</td>\n",
" <td>0.094571</td>\n",
" <td>0.781429</td>\n",
" <td>0.894286</td>\n",
" <td>0.917143</td>\n",
" <td>0.945714</td>\n",
" <td>0.867046</td>\n",
" <td>0.841435</td>\n",
" <td>0.844011</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/transformers/integrations/peft.py:418: FutureWarning: The `active_adapter` method is deprecated and will be removed in a future version.\n",
" warnings.warn(\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Computing widget examples: 0%| | 0/1 [00:00<?, ?example/s]"
],
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}
},
"metadata": {}
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{
"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": "842604dd1db9498fa7e826f375514479"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Corpus Chunks: 100%|██████████| 12/12 [00:01<00:00, 8.50it/s]\n",
"/usr/local/lib/python3.10/dist-packages/transformers/integrations/peft.py:418: FutureWarning: The `active_adapter` method is deprecated and will be removed in a future version.\n",
" warnings.warn(\n"
]
},
{
"output_type": "error",
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: 'training_trial/checkpoint-250/pytorch_model.bin'",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-11-af73f3eb6fa7>\u001b[0m in \u001b[0;36m<cell line: 3>\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/python3.10/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 2121\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 2122\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-> 2123\u001b[0;31m return inner_training_loop(\n\u001b[0m\u001b[1;32m 2124\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 2125\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/python3.10/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 2599\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 2600\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2601\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 2602\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2603\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/usr/local/lib/python3.10/dist-packages/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",
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"\u001b[0;32m/usr/local/lib/python3.10/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 1317\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 1318\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1319\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 1320\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 1321\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/python3.10/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 657\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 658\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--> 659\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 660\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 661\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/python3.10/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, name, mode)\u001b[0m\n\u001b[1;32m 638\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 639\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--> 640\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 641\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 642\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-250/pytorch_model.bin'"
]
}
]
},
{
"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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