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"import os\n",
"\n",
"# Set the \"OPENAI_API_KEY\" in the Python environment. Will be used by OpenAI client later.\n",
"os.environ[\"OPENAI_API_KEY\"] = \"\""
],
"metadata": {
"id": "RyE625lCtTsi"
},
"execution_count": 19,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Load the Dataset (Webpages)"
],
"metadata": {
"id": "OWgHCmejcCFV"
}
},
{
"cell_type": "markdown",
"source": [
"## Download"
],
"metadata": {
"id": "3R7P7GUmemIL"
}
},
{
"cell_type": "code",
"source": [
"TRAIN_URLs = [\n",
" \"https://towardsai.net/p/machine-learning/metas-llama-2-revolutionizing-open-source-language-models-for-commercial-use\",\n",
" \"https://towardsai.net/p/machine-learning/fine-tuning-a-llama-2-7b-model-for-python-code-generation\",\n",
" \"https://towardsai.net/p/machine-learning/how-to-create-llama-2-chatbot-with-gradio-and-hugging-face-in-free-colab\",\n",
" \"https://towardsai.net/p/machine-learning/meta-releases-llama-2-free-for-commercial-use\",\n",
" \"https://towardsai.net/p/machine-learning/gpt-4-llama-2-claude-how-different-language-models-react-to-prompts\",\n",
" \"https://towardsai.net/p/machine-learning/a-simple-hugging-face-guide-to-chatting-with-the-llama-2-7b-model-in-a-colab-notebook\",\n",
" \"https://towardsai.net/p/machine-learning/fine-tuning-a-llama-2-7b-model-for-python-code-generation\",\n",
" \"https://towardsai.net/p/machine-learning/llamaindex-last-version-from-basics-to-advanced-techniques-in-python-part-3\",\n",
" \"https://towardsai.net/p/machine-learning/meta-releases-llama-will-it-fail-too\",\n",
" \"https://towardsai.net/p/machine-learning/llama-by-meta-leaked-by-an-anonymous-forum-questions-arises-on-meta\"\n",
"]\n",
"VALIDATION_URLs = [\n",
" \"https://towardsai.net/p/machine-learning/deep-diving-into-llama-2-meta-ai-new-open-source-foundation-model\",\n",
" \"https://towardsai.net/p/machine-learning/gptq-quantization-on-a-llama-2-7b-fine-tuned-model-with-huggingface\",\n",
" \"https://towardsai.net/p/machine-learning/powerinfer-11x-speed-up-llama-ii-inference-on-a-local-gpu\",\n",
" \"https://towardsai.net/p/machine-learning/dense-x-retrieval-technique-in-langchain-and-llamaindex\",\n",
" \"https://towardsai.net/p/machine-learning/exploring-large-language-models-part-2\",\n",
" \"https://towardsai.net/p/machine-learning/inside-code-llama-meta-ais-entrance-in-the-code-llm-space\",\n",
" \"https://towardsai.net/p/machine-learning/llamaindex-use-the-power-of-llms-on-your-data\",\n",
" \"https://towardsai.net/p/l/inside-llama-meta-ai-new-large-language-model-that-outperforms-gpt-3-across-many-tasks\"\n",
"]"
],
"metadata": {
"id": "Gx4_5BF8B4tx"
},
"execution_count": 20,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Read the Page"
],
"metadata": {
"id": "6Lua8G8seyEx"
}
},
{
"cell_type": "code",
"source": [
"from llama_index.readers import SimpleWebPageReader\n",
"\n",
"# Read the content of webpage into lists. We need two sets of documents for Training, and Validation.\n",
"TRAIN_DOCs = SimpleWebPageReader(html_to_text=True).load_data(TRAIN_URLs)\n",
"VALIDATION_DOCs = SimpleWebPageReader(html_to_text=True).load_data(VALIDATION_URLs)\n",
"print( len(TRAIN_DOCs), len(VALIDATION_DOCs) )"
],
"metadata": {
"id": "lzzcVnWXB4rJ",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "03e17638-d4f8-4b8c-d5d0-e871120616fa"
},
"execution_count": 21,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"10 8\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# Chunking"
],
"metadata": {
"id": "TGbmUhUVezdU"
}
},
{
"cell_type": "code",
"source": [
"from llama_index.node_parser import SimpleNodeParser\n",
"\n",
"# Define a parser to perform the chunking process.\n",
"parser = SimpleNodeParser.from_defaults(chunk_size=512, chunk_overlap=20)\n",
"\n",
"# Apply chunking on the training/validation sets.\n",
"TRAIN_NODEs = parser.get_nodes_from_documents(TRAIN_DOCs)\n",
"VALIDATION_NODEs = parser.get_nodes_from_documents(VALIDATION_DOCs)\n",
"print( len( TRAIN_NODEs ), len( VALIDATION_NODEs ) )"
],
"metadata": {
"id": "9r_UzQ5BB4kG",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "d440f72a-9fa2-4b8c-f320-ecc4615b0f2c"
},
"execution_count": 22,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"274 222\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Use a subset of the dataset (5 samples) if testing.\n",
"if testing:\n",
" TRAIN_NODEs = TRAIN_NODEs [0:5]\n",
" VALIDATION_NODEs = VALIDATION_NODEs[0:5]"
],
"metadata": {
"id": "Y6iEHKfZsUiv"
},
"execution_count": 14,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Generate Question"
],
"metadata": {
"id": "LvKf6i9Lg_2G"
}
},
{
"cell_type": "markdown",
"source": [
"We use a Large Language Model (LLM) to produce questions for each chunk of the dataset. Then we can use these data to train the model to develop embeddings that more accurately represent the types of questions users may ask."
],
"metadata": {
"id": "1DMXD7nbhFsm"
}
},
{
"cell_type": "code",
"source": [
"# Use this block of code if you don't want to generate the questions for the dataset. (Avoid API call charges!)\n",
"# Uncomment the following code, and keep in mind to comment all the contents in the next coding block.\n",
"\n",
"# from llama_index.finetuning import EmbeddingQAFinetuneDataset\n",
"\n",
"# # Load the pre-generated questions json files.\n",
"# TRAIN_DATASET = EmbeddingQAFinetuneDataset.from_json(\"train_dataset.json\")\n",
"# VALIDATION_DATASET = EmbeddingQAFinetuneDataset.from_json(\"val_dataset.json\")"
],
"metadata": {
"id": "geqDZn-pB4fI"
},
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from llama_index.finetuning import generate_qa_embedding_pairs\n",
"from llama_index.llms import OpenAI\n",
"\n",
"# Load the OpenAI API with the \"gpt-3.5-turbo\" model\n",
"llm = OpenAI()\n",
"\n",
"# Generate questions for each chunk.\n",
"TRAIN_DATASET = generate_qa_embedding_pairs(TRAIN_NODEs, llm=llm)\n",
"VALIDATION_DATASET = generate_qa_embedding_pairs(VALIDATION_NODEs, llm=llm)\n",
"\n",
"TRAIN_DATASET.save_json(\"train_dataset.json\")\n",
"VALIDATION_DATASET.save_json(\"val_dataset.json\")"
],
"metadata": {
"id": "Es3rbmO-B4hv",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "7cb141ff-3ac2-47ac-8e05-6fe7720da5bf"
},
"execution_count": 17,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 273/273 [05:15<00:00, 1.16s/it]\n",
"100%|██████████| 222/222 [04:12<00:00, 1.14s/it]\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# Load an Embedding Model"
],
"metadata": {
"id": "Hthjh_SjlB-C"
}
},
{
"cell_type": "code",
"source": [
"from llama_index.embeddings import resolve_embed_model\n",
"\n",
"# Load an existing embedding model with a linear layer adopter on top.\n",
"base_embed_model = resolve_embed_model(\"local:BAAI/bge-small-en-v1.5\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 336,
"referenced_widgets": [
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},
"id": "ZHWPMZHUupNW",
"outputId": "9f731c0a-6406-48fd-c68c-5d8df8f12d64"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:72: UserWarning: \n",
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co./settings/tokens), set it as secret in your Google Colab and restart your session.\n",
"You will be able to reuse this secret in all of your notebooks.\n",
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
" warnings.warn(\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"config.json: 0%| | 0.00/743 [00:00 Prepared optimizer, scheduler, and loss model.\n",
"\u001b[0m"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Epoch: 0%| | 0/4 [00:00 [Epoch 0] Current loss: 1.6320068836212158\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 1.756566047668457\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 1.974733591079712\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 0.904658317565918\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 1.156891942024231\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 1.5102888345718384\n",
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"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 1.289185643196106\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 1.3490561246871948\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 1.3077905178070068\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 0.9143151044845581\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 1.1586413383483887\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 1.6070935726165771\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 1.464544415473938\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 1.2603508234024048\n",
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"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 1.3052990436553955\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 1.9211496114730835\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 2.0292797088623047\n",
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"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 1.4330310821533203\n",
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"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 1.305367350578308\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 1.123714804649353\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 1.2846038341522217\n",
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"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 1.296608805656433\n",
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"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 1.271284580230713\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 1.8778873682022095\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 1.8127195835113525\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 1.1267815828323364\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 1.0543017387390137\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 1.3711048364639282\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 1.5974634885787964\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 1.7278261184692383\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 0.9889880418777466\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 1.2172324657440186\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 1.2401028871536255\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 1.5869677066802979\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 1.6271196603775024\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 1.346295714378357\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 1.373318076133728\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 1.0156323909759521\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 0] Current loss: 0.9154008030891418\n",
"\u001b[0m"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Iteration: 0%| | 0/55 [00:00 [Epoch 1] Current loss: 1.5881574153900146\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.7358734607696533\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.9496583938598633\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 0.8745635747909546\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.111528754234314\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.4749138355255127\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.4362574815750122\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.2734665870666504\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.3329273462295532\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.286020040512085\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 0.880274772644043\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.1097452640533447\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.5711208581924438\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.4261044263839722\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.2453088760375977\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.0061759948730469\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.0995166301727295\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.3668005466461182\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.4034123420715332\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.1663644313812256\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.1195590496063232\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.2613608837127686\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.8848832845687866\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 2.0065250396728516\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.117928147315979\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.1036391258239746\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.4004104137420654\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 2.007164478302002\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.8199392557144165\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.2747118473052979\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.087892770767212\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.2408543825149536\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.672642707824707\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.4452815055847168\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.234491229057312\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.2770001888275146\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.1435420513153076\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.1348841190338135\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.2315013408660889\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.849600076675415\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.788346290588379\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.110386610031128\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.0332342386245728\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.3298530578613281\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.552140474319458\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.7029966115951538\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 0.9620378613471985\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.182659387588501\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.2092931270599365\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.5520379543304443\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.611222505569458\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.328142523765564\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 1.3351960182189941\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 0.9847389459609985\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 1] Current loss: 0.88980633020401\n",
"\u001b[0m"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Iteration: 0%| | 0/55 [00:00 [Epoch 2] Current loss: 1.550796389579773\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.7150386571884155\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.9233592748641968\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 0.8541108965873718\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.0777015686035156\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.4487472772598267\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.4066628217697144\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.2605724334716797\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.3200373649597168\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.2688066959381104\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 0.8603048324584961\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.0778127908706665\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.5469229221343994\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.4023314714431763\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.235028862953186\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 0.9840642809867859\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.0698928833007812\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.3380721807479858\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.3790022134780884\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.1522510051727295\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.0955487489700317\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.234580636024475\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.8616470098495483\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.9927787780761719\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.103163480758667\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.0805270671844482\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.3795859813690186\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.9900401830673218\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.8095190525054932\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.256723165512085\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.0671331882476807\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.2148537635803223\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.6437517404556274\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.4246234893798828\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.2227160930633545\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.2645256519317627\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.1250587701797485\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.1128017902374268\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.208459734916687\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.8320140838623047\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.7743244171142578\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.1003539562225342\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.0207250118255615\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.3060414791107178\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.5256621837615967\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.688199758529663\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 0.9477392435073853\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.1627063751220703\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.1920307874679565\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.5313647985458374\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.6015046834945679\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.3174530267715454\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 1.3139145374298096\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 0.967995285987854\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 2] Current loss: 0.8766116499900818\n",
"\u001b[0m"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Iteration: 0%| | 0/55 [00:00 [Epoch 3] Current loss: 1.5297434329986572\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.7028414011001587\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.9069995880126953\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 0.8438453674316406\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.0594712495803833\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.434706449508667\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.3906594514846802\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.2529596090316772\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.3124738931655884\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.2587229013442993\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 0.8505775332450867\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.0611050128936768\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.533494472503662\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.3900038003921509\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.229201078414917\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 0.9730359315872192\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.0548118352890015\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.3231446743011475\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.3664520978927612\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.1445457935333252\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.0835111141204834\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.2211554050445557\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.8491623401641846\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.9855743646621704\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.0956623554229736\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.069385290145874\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.368985652923584\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.9808393716812134\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.8045599460601807\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.2479966878890991\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.0574877262115479\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.20292067527771\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.630247712135315\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.4152036905288696\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.2168834209442139\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.2584458589553833\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.1165682077407837\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.1032503843307495\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.1985795497894287\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.8240630626678467\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.7680002450942993\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.0957413911819458\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.015473484992981\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.296223521232605\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.5147284269332886\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.6817430257797241\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 0.9423201680183411\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.154968500137329\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.1854857206344604\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.5231069326400757\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.5973836183547974\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.313131332397461\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 1.3062735795974731\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 0.9623059034347534\n",
"\u001b[0m\u001b[1;3;34m> [Epoch 3] Current loss: 0.87225341796875\n",
"\u001b[0m\u001b[1;3;34m> Finished training, saving to model_output_test\n",
"\u001b[0m"
]
}
]
},
{
"cell_type": "code",
"source": [
"embed_model = finetune_engine.get_finetuned_model()\n",
"\n",
"# Or, import model from the directory whenever required.\n",
"# from llama_index.embeddings import LinearAdapterEmbeddingModel\n",
"# embed_model = LinearAdapterEmbeddingModel(base_embed_model, \"model_output_test\")"
],
"metadata": {
"id": "JJOhEdlawqL7"
},
"execution_count": 7,
"outputs": []
},
{
"cell_type": "code",
"source": [
"embed_model"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9hjc_5c5w9xm",
"outputId": "dacc9d8d-baa1-468a-b457-b39baabae681"
},
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"AdapterEmbeddingModel(model_name='Adapter for BAAI/bge-small-en-v1.5', embed_batch_size=10, callback_manager=)"
]
},
"metadata": {},
"execution_count": 8
}
]
},
{
"cell_type": "markdown",
"source": [
"# Evaluate"
],
"metadata": {
"id": "vrj0FrzhygGD"
}
},
{
"cell_type": "markdown",
"source": [
"## Define the Evaluation Functions"
],
"metadata": {
"id": "yEj5ZAEioCss"
}
},
{
"cell_type": "markdown",
"source": [
"Hit-rate metric: For each (query, context) pair, we retrieve the top-k documents with the query. It’s a hit if the results contain the ground-truth context."
],
"metadata": {
"id": "C0g_0yQT0HlR"
}
},
{
"cell_type": "code",
"source": [
"from llama_index import ServiceContext, VectorStoreIndex\n",
"from llama_index.schema import TextNode\n",
"from tqdm.notebook import tqdm\n",
"\n",
"def evaluate( dataset, embed_model, top_k=5, verbose=False):\n",
" corpus = dataset.corpus\n",
" queries = dataset.queries\n",
" relevant_docs = dataset.relevant_docs\n",
"\n",
" # Chunking the documents and generating embeddings\n",
" service_context = ServiceContext.from_defaults(embed_model=embed_model)\n",
" nodes = [TextNode(id_=id_, text=text) for id_, text in corpus.items()]\n",
" index = VectorStoreIndex(\n",
" nodes, service_context=service_context, show_progress=True\n",
" )\n",
"\n",
" # Define a retrieveer to answer the questions\n",
" retriever = index.as_retriever(similarity_top_k=top_k)\n",
"\n",
" eval_results = []\n",
" # Look into the each response sources to see if the chunk that contains answer is retrieved.\n",
" for query_id, query in tqdm(queries.items()):\n",
" retrieved_nodes = retriever.retrieve(query)\n",
" retrieved_ids = [node.node.node_id for node in retrieved_nodes]\n",
" expected_id = relevant_docs[query_id][0]\n",
" is_hit = expected_id in retrieved_ids # assume 1 relevant doc\n",
"\n",
" eval_result = {\n",
" \"is_hit\": is_hit,\n",
" \"retrieved\": retrieved_ids,\n",
" \"expected\": expected_id,\n",
" \"query\": query_id,\n",
" }\n",
" eval_results.append(eval_result)\n",
" return eval_results"
],
"metadata": {
"id": "sbfgQIyIyhCZ"
},
"execution_count": 9,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## OpenAI"
],
"metadata": {
"id": "dKsxY6Vvy7M3"
}
},
{
"cell_type": "code",
"source": [
"from llama_index.embeddings import OpenAIEmbedding\n",
"\n",
"# Load the OpenAI Ada model and evaluate it.\n",
"ada = OpenAIEmbedding()\n",
"ada_val_results = evaluate(VALIDATION_DATASET, ada)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 81,
"referenced_widgets": [
"edf3dde9bfd84fb88e3ded4ae4d1d4ac",
"4f2dd9336b8d4f1aa53b5e6c03824503",
"d8c1a77ee31b440d8002a5dcce312d73",
"dd49c06016b14b259db335edb08dc97e",
"f1692aa786ae4b3191c1c9a2399ebf15",
"679fcceaf58a4ae3920b790c358c327f",
"a904f37359eb422e88d0ba0bc8b9fb0a",
"c3a04250d6db4942b89d971aa1c5b09b",
"11674f7fa1ec4c38b2836402e2bd29aa",
"f7ff8abe42d249d39405d5290b6b41a8",
"5e29cdbe931848a8aa2ad9f037c0b5fc",
"c0c49dceefb54d4ebb1b873b53aa45b5",
"a63bcb0b7430401bb57b131e5e3e8809",
"10495396b8334fa888c13fa1fc99c0be",
"03ffe5e5d9f94b92a3433aca8d2881ae",
"96f087c5d3444b4aac4252179512973a",
"fc037c5bac054295940acb68710d315a",
"11cfef1a88d2422580a16b4ad4d8f0d3",
"63a6bfeb1090449db39485b55d8746c8",
"b7b72e0bbbc944a8a0e13760c3201ed5",
"015e870c26544188913643377bb6ddff",
"f22e4441e60243d9b88657784824476d"
]
},
"id": "QpEDBv4cylM1",
"outputId": "a93621d8-028c-47cd-e23a-4eb918172a85"
},
"execution_count": 10,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Generating embeddings: 0%| | 0/222 [00:00