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{
"cells": [
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"import math\n",
"import numpy as np\n",
"from pathlib import Path\n",
"from typing import List, Union, Any\n",
"from tqdm import tqdm\n",
"from sentence_transformers import CrossEncoder\n",
"from langchain.chains import RetrievalQA\n",
"from langchain.embeddings import HuggingFaceEmbeddings, HuggingFaceInstructEmbeddings\n",
"from langchain.document_loaders import TextLoader\n",
"from langchain.indexes import VectorstoreIndexCreator\n",
"from langchain.text_splitter import CharacterTextSplitter\n",
"from langchain.vectorstores import FAISS\n",
"from sentence_transformers import CrossEncoder"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"class AverageInstructEmbeddings(HuggingFaceInstructEmbeddings):\n",
" max_length: int = None\n",
" def __init__(self, max_length: int = 512, **kwargs: Any):\n",
" super().__init__(**kwargs)\n",
" self.max_length = max_length\n",
" if self.max_length < 0:\n",
" print('max_length is not specified, using model default max_seq_length')\n",
"\n",
" def embed_documents(self, texts: List[str]) -> List[List[float]]:\n",
" all_embeddings = []\n",
" for text in tqdm(texts, desc=\"Embedding documents\"):\n",
" if len(text) > self.max_length and self.max_length > -1:\n",
" n_chunks = math.ceil(len(text)/self.max_length)\n",
" chunks = [\n",
" text[i*self.max_length:(i+1)*self.max_length]\n",
" for i in range(n_chunks)\n",
" ]\n",
" instruction_pairs = [[self.embed_instruction, chunk] for chunk in chunks]\n",
" chunk_embeddings = self.client.encode(instruction_pairs)\n",
" avg_embedding = np.mean(chunk_embeddings, axis=0)\n",
" all_embeddings.append(avg_embedding.tolist())\n",
" else:\n",
" instruction_pairs = [[self.embed_instruction, text]]\n",
" embeddings = self.client.encode(instruction_pairs)\n",
" all_embeddings.append(embeddings[0].tolist())\n",
"\n",
" return all_embeddings\n",
"\n",
"\n",
"class BenchDataST:\n",
" def __init__(self, path: str, percentage: float = 0.005, chunk_size: int = 512, chunk_overlap: int = 100):\n",
" self.path = path\n",
" self.percentage = percentage\n",
" self.docs = []\n",
" self.metadata = []\n",
" self.load()\n",
" self.text_splitter = CharacterTextSplitter(separator=\"\", chunk_size=chunk_size, chunk_overlap=chunk_overlap)\n",
" self.docs_processed = self.text_splitter.create_documents(self.docs, self.metadata)\n",
"\n",
" def load(self):\n",
" for p in Path(self.path).iterdir():\n",
" if not p.is_dir():\n",
" with open(p) as f:\n",
" source = f.readline().strip().replace('source: ', '')\n",
" self.docs.append(f.read())\n",
" self.metadata.append({\"source\": source})\n",
" self.docs = self.docs[:int(len(self.docs) * self.percentage)]\n",
" self.metadata = self.metadata[:int(len(self.metadata) * self.percentage)]\n",
"\n",
" def __len__(self):\n",
" return len(self.docs)\n",
"\n",
" def __getitem__(self, idx):\n",
" return self.docs[idx], self.metadata[idx]\n",
"\n",
" def __iter__(self):\n",
" for doc, metadata in zip(self.docs, self.metadata):\n",
" yield doc, metadata\n",
"\n",
" def __repr__(self):\n",
" return f'BenchDataST({len(self)} docs) at {self.path} with {self.percentage} percentage \\nSources: {self.metadata} \\nChunks: {self.text_splitter}'\n",
" \n",
"\n",
"class BenchmarkST:\n",
" def __init__(self, data: BenchDataST, baseline_model: Union[HuggingFaceEmbeddings, HuggingFaceInstructEmbeddings, AverageInstructEmbeddings], embedding_models: List[Union[HuggingFaceEmbeddings, HuggingFaceInstructEmbeddings, AverageInstructEmbeddings]]):\n",
" self.data = data\n",
" self.baseline_model = baseline_model\n",
" self.embedding_models = embedding_models\n",
" self.baseline_index, self.indexes = self.build_indexes()\n",
"\n",
" def build_indexes(self):\n",
" indexes = []\n",
" for model in [self.baseline_model] + self.embedding_models:\n",
" print(f\"Building index for {model}\")\n",
" index = FAISS.from_documents(self.data.docs_processed, model)\n",
" indexes.append(index)\n",
" return indexes[0], indexes[1:]\n",
" \n",
" def add_index(self, index: FAISS):\n",
" self.indexes.append(index)\n",
" \n",
" def evaluate(self, query: str, k: int = 3):\n",
" baseline_results = self.baseline_index.similarity_search_with_score(query, k=k)\n",
" results = []\n",
" for index in self.indexes:\n",
" results.append(index.similarity_search_with_score(query, k=k))\n",
" return baseline_results, results"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"load INSTRUCTOR_Transformer\n",
"max_seq_length 512\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"No sentence-transformers model found with name /Users/michalwilinski/.cache/torch/sentence_transformers/cross-encoder_ms-marco-MiniLM-L-12-v2. Creating a new one with MEAN pooling.\n",
"Some weights of the model checkpoint at /Users/michalwilinski/.cache/torch/sentence_transformers/cross-encoder_ms-marco-MiniLM-L-12-v2 were not used when initializing BertModel: ['classifier.bias', 'classifier.weight']\n",
"- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
"- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Building index for client=INSTRUCTOR(\n",
" (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: T5EncoderModel \n",
" (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})\n",
" (2): Dense({'in_features': 768, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})\n",
" (3): Normalize()\n",
") model_name='hkunlp/instructor-base' cache_folder=None model_kwargs={} encode_kwargs={} embed_instruction='Represent this piece of text for searching relevant information:' query_instruction='Query the most relevant piece of information from the Hugging Face documentation' max_length=512\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Embedding documents: 100%|██████████| 278/278 [00:19<00:00, 14.11it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Building index for client=SentenceTransformer(\n",
" (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel \n",
" (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})\n",
") model_name='cross-encoder/ms-marco-MiniLM-L-12-v2' cache_folder=None model_kwargs={} encode_kwargs={} multi_process=False\n"
]
}
],
"source": [
"data = BenchDataST(\n",
" path=\"./datasets/huggingface_docs/\",\n",
" percentage=0.005,\n",
" chunk_size=512,\n",
" chunk_overlap=100\n",
")\n",
"\n",
"baseline_embedding_model = AverageInstructEmbeddings(\n",
" model_name=\"hkunlp/instructor-base\",\n",
" embed_instruction=\"Represent this piece of text for searching relevant information:\",\n",
" query_instruction=\"Query the most relevant piece of information from the Hugging Face documentation\",\n",
" max_length=512,\n",
")\n",
"\n",
"embedding_model = HuggingFaceEmbeddings(\n",
" model_name=\"intfloat/e5-large-v2\",\n",
")\n",
"\n",
"cross_encoder = HuggingFaceEmbeddings(model_name=\"cross-encoder/ms-marco-MiniLM-L-12-v2\")\n",
"\n",
"benchmark = BenchmarkST(\n",
" data=data,\n",
" baseline_model=baseline_embedding_model,\n",
" embedding_models=[cross_encoder]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Baseline results:\n",
"{'source': 'https://github.com/huggingface/course/blob/main/chapters/en/chapter6/4.mdx'} 0.23610792\n",
"{'source': 'https://github.com/huggingface/course/blob/main/chapters/en/chapter6/4.mdx'} 0.24087097\n",
"{'source': 'https://github.com/huggingface/course/blob/main/chapters/en/chapter6/4.mdx'} 0.24181677\n",
"{'source': 'https://github.com/huggingface/course/blob/main/chapters/en/chapter6/4.mdx'} 0.24541612\n",
"{'source': 'https://github.com/huggingface/course/blob/main/chapters/en/chapter6/4.mdx'} 0.24639006\n",
"{'source': 'https://github.com/huggingface/course/blob/main/chapters/en/chapter6/4.mdx'} 0.24780047\n",
"{'source': 'https://github.com/huggingface/course/blob/main/chapters/en/chapter6/4.mdx'} 0.2535807\n",
"{'source': 'https://github.com/huggingface/optimum/blob/main/docs/source/exporters/onnx/usage_guides/export_a_model.mdx'} 0.25887597\n",
"{'source': 'https://github.com/huggingface/course/blob/main/chapters/en/chapter6/4.mdx'} 0.27293646\n",
"{'source': 'https://github.com/huggingface/course/blob/main/chapters/en/chapter6/4.mdx'} 0.27374876\n",
"{'source': 'https://github.com/huggingface/course/blob/main/chapters/en/chapter6/4.mdx'} 0.27710187\n",
"{'source': 'https://github.com/huggingface/course/blob/main/chapters/en/chapter6/4.mdx'} 0.28146794\n",
"{'source': 'https://github.com/huggingface/course/blob/main/chapters/en/chapter6/4.mdx'} 0.29536068\n",
"{'source': 'https://github.com/huggingface/course/blob/main/chapters/en/chapter6/4.mdx'} 0.29784447\n",
"{'source': 'https://github.com/huggingface/course/blob/main/chapters/en/chapter6/4.mdx'} 0.30452335\n",
"{'source': 'https://github.com/huggingface/course/blob/main/chapters/en/chapter6/4.mdx'} 0.3061711\n",
"{'source': 'https://github.com/huggingface/course/blob/main/chapters/en/chapter6/4.mdx'} 0.31600478\n",
"{'source': 'https://github.com/huggingface/course/blob/main/chapters/en/chapter6/4.mdx'} 0.3166225\n",
"{'source': 'https://github.com/huggingface/pytorch-image-models/blob/main/docs/changes.md'} 0.33345556\n",
"{'source': 'https://github.com/huggingface/optimum/blob/main/docs/source/exporters/onnx/usage_guides/export_a_model.mdx'} 0.3469957\n",
"{'source': 'https://github.com/huggingface/optimum/blob/main/docs/source/exporters/onnx/usage_guides/export_a_model.mdx'} 0.35222226\n",
"{'source': 'https://github.com/huggingface/course/blob/main/chapters/en/chapter6/4.mdx'} 0.36451602\n",
"{'source': 'https://github.com/huggingface/course/blob/main/chapters/en/chapter6/4.mdx'} 0.36925688\n",
"{'source': 'https://github.com/huggingface/course/blob/main/chapters/en/chapter6/4.mdx'} 0.37025565\n",
"{'source': 'https://github.com/huggingface/diffusers/blob/main/examples/textual_inversion/README.md'} 0.37112093\n",
"{'source': 'https://github.com/huggingface/optimum/blob/main/docs/source/exporters/onnx/usage_guides/export_a_model.mdx'} 0.37146708\n",
"{'source': 'https://github.com/huggingface/optimum/blob/main/docs/source/exporters/onnx/usage_guides/export_a_model.mdx'} 0.3766507\n",
"{'source': 'https://github.com/huggingface/optimum/blob/main/docs/source/exporters/onnx/usage_guides/export_a_model.mdx'} 0.37794292\n",
"{'source': 'https://github.com/huggingface/optimum/blob/main/docs/source/exporters/onnx/usage_guides/export_a_model.mdx'} 0.37923962\n",
"{'source': 'https://github.com/huggingface/pytorch-image-models/blob/main/docs/changes.md'} 0.38359642\n",
"{'source': 'https://github.com/huggingface/pytorch-image-models/blob/main/docs/changes.md'} 0.3878625\n",
"{'source': 'https://github.com/huggingface/optimum/blob/main/docs/source/exporters/onnx/usage_guides/export_a_model.mdx'} 0.39796114\n",
"{'source': 'https://github.com/huggingface/optimum/blob/main/docs/source/exporters/onnx/usage_guides/export_a_model.mdx'} 0.40057343\n",
"{'source': 'https://github.com/huggingface/pytorch-image-models/blob/main/docs/changes.md'} 0.40114868\n",
"{'source': 'https://github.com/huggingface/optimum/blob/main/docs/source/exporters/onnx/usage_guides/export_a_model.mdx'} 0.40156174\n",
"{'source': 'https://github.com/huggingface/pytorch-image-models/blob/main/docs/changes.md'} 0.40341228\n",
"{'source': 'https://github.com/huggingface/diffusers/blob/main/examples/textual_inversion/README.md'} 0.40720195\n",
"{'source': 'https://github.com/huggingface/optimum/blob/main/docs/source/exporters/onnx/usage_guides/export_a_model.mdx'} 0.41241395\n",
"{'source': 'https://github.com/huggingface/optimum/blob/main/docs/source/exporters/onnx/usage_guides/export_a_model.mdx'} 0.4134417\n",
"{'source': 'https://github.com/huggingface/pytorch-image-models/blob/main/docs/changes.md'} 0.4134435\n",
"{'source': 'https://github.com/huggingface/optimum/blob/main/docs/source/exporters/onnx/usage_guides/export_a_model.mdx'} 0.41754264\n",
"{'source': 'https://github.com/huggingface/optimum/blob/main/docs/source/exporters/onnx/usage_guides/export_a_model.mdx'} 0.41917825\n",
"{'source': 'https://github.com/huggingface/optimum/blob/main/docs/source/exporters/onnx/usage_guides/export_a_model.mdx'} 0.41928726\n",
"{'source': 'https://github.com/huggingface/optimum/blob/main/docs/source/exporters/onnx/usage_guides/export_a_model.mdx'} 0.41988587\n",
"{'source': 'https://github.com/huggingface/optimum/blob/main/docs/source/exporters/onnx/usage_guides/export_a_model.mdx'} 0.42029166\n",
"{'source': 'https://github.com/huggingface/pytorch-image-models/blob/main/docs/changes.md'} 0.42128915\n",
"{'source': 'https://github.com/huggingface/pytorch-image-models/blob/main/docs/changes.md'} 0.4226097\n",
"{'source': 'https://github.com/huggingface/pytorch-image-models/blob/main/docs/changes.md'} 0.42302307\n",
"{'source': 'https://github.com/gradio-app/gradio/blob/main/demo/stt_or_tts/run.ipynb'} 0.4252566\n",
"{'source': 'https://github.com/huggingface/diffusers/blob/main/examples/textual_inversion/README.md'} 0.42704937\n",
"{'source': 'https://github.com/huggingface/pytorch-image-models/blob/main/docs/changes.md'} 0.4297651\n",
"{'source': 'https://github.com/huggingface/pytorch-image-models/blob/main/docs/changes.md'} 0.43067485\n",
"{'source': 'https://github.com/huggingface/optimum/blob/main/docs/source/exporters/onnx/usage_guides/export_a_model.mdx'} 0.43116528\n",
"{'source': 'https://github.com/huggingface/blog/blob/main/bloom.md'} 0.43272027\n",
"{'source': 'https://github.com/huggingface/diffusers/blob/main/examples/instruct_pix2pix/README_sdxl.md'} 0.43434155\n",
"{'source': 'https://github.com/huggingface/optimum/blob/main/docs/source/exporters/onnx/usage_guides/export_a_model.mdx'} 0.43486434\n",
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"{'source': 'https://github.com/huggingface/optimum/blob/main/docs/source/exporters/onnx/usage_guides/export_a_model.mdx'} 0.43530554\n",
"{'source': 'https://github.com/huggingface/optimum/blob/main/docs/source/exporters/onnx/usage_guides/export_a_model.mdx'} 0.4371896\n",
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"{'source': 'https://github.com/huggingface/pytorch-image-models/blob/main/docs/changes.md'} 0.43824\n",
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"{'source': 'https://github.com/huggingface/blog/blob/main/accelerate-deepspeed.md'} 0.44034868\n",
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"{'source': 'https://github.com/gradio-app/gradio/blob/main/demo/stt_or_tts/run.ipynb'} 0.44683564\n",
"{'source': 'https://github.com/huggingface/blog/blob/main/accelerate-deepspeed.md'} 0.44743723\n",
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"{'source': 'https://github.com/huggingface/pytorch-image-models/blob/main/docs/changes.md'} 0.4477852\n",
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"{'source': 'https://github.com/huggingface/pytorch-image-models/blob/main/docs/changes.md'} 0.45155957\n",
"{'source': 'https://github.com/huggingface/pytorch-image-models/blob/main/docs/changes.md'} 0.45215163\n",
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"{'source': 'https://github.com/huggingface/transformers/blob/main/docs/source/en/model_doc/open-llama.md'} 0.45448524\n",
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"{'source': 'https://github.com/huggingface/pytorch-image-models/blob/main/docs/changes.md'} 0.45478693\n",
"{'source': 'https://github.com/huggingface/diffusers/blob/main/docs/source/en/api/schedulers/euler_ancestral.md'} 0.45494407\n",
"{'source': 'https://github.com/huggingface/transformers/blob/main/docs/source/en/model_doc/open-llama.md'} 0.45494407\n",
"{'source': 'https://github.com/gradio-app/gradio/blob/main/js/accordion/CHANGELOG.md'} 0.45520714\n",
"{'source': 'https://github.com/huggingface/pytorch-image-models/blob/main/docs/changes.md'} 0.4559689\n",
"{'source': 'https://github.com/huggingface/blog/blob/main/bloom.md'} 0.4568352\n",
"{'source': 'https://github.com/huggingface/optimum/blob/main/docs/source/exporters/onnx/usage_guides/export_a_model.mdx'} 0.4577096\n",
"{'source': 'https://github.com/huggingface/simulate/blob/main/docs/source/api/lights.mdx'} 0.4577096\n",
"{'source': 'https://github.com/huggingface/diffusers/blob/main/examples/instruct_pix2pix/README_sdxl.md'} 0.45773098\n",
"{'source': 'https://github.com/huggingface/blog/blob/main/bloom.md'} 0.45818624\n",
"{'source': 'https://github.com/huggingface/optimum/blob/main/docs/source/exporters/onnx/usage_guides/export_a_model.mdx'} 0.45871085\n",
"{'source': 'https://github.com/huggingface/blog/blob/main/bloom.md'} 0.4591412\n",
"{'source': 'https://github.com/huggingface/diffusers/blob/main/examples/instruct_pix2pix/README_sdxl.md'} 0.46033093\n",
"{'source': 'https://github.com/huggingface/blog/blob/main/accelerate-deepspeed.md'} 0.4605264\n",
"{'source': 'https://github.com/huggingface/pytorch-image-models/blob/main/docs/changes.md'} 0.46091354\n",
"{'source': 'https://github.com/huggingface/transformers/blob/main/docs/source/en/model_doc/open-llama.md'} 0.46182537\n",
"Cross encoder results:\n",
"{'source': 'https://github.com/huggingface/course/blob/main/chapters/en/chapter6/4.mdx'} 6.840022\n",
"{'source': 'https://github.com/huggingface/course/blob/main/chapters/en/chapter6/4.mdx'} -0.98426485\n",
"{'source': 'https://github.com/huggingface/course/blob/main/chapters/en/chapter6/4.mdx'} -1.9345549\n",
"bye\n"
]
}
],
"source": [
"query = \"textual inversion\"\n",
"k = 100\n",
"baseline_results, results = benchmark.evaluate(query=query, k=k)\n",
"print(\"Baseline results:\")\n",
"[print(doc.metadata,score) for (doc,score) in baseline_results]\n",
"cross_encoder = CrossEncoder(\"cross-encoder/ms-marco-MiniLM-L-12-v2\")\n",
"cross_encoder_results = cross_encoder.predict([(query, doc.page_content) for doc in data.docs_processed])\n",
"# rerank results\n",
"cross_encoder_results = sorted(zip(data.docs_processed, cross_encoder_results), key=lambda x: x[1], reverse=True)\n",
"print(\"Cross encoder results:\")\n",
"final_results = cross_encoder_results[:3]\n",
"[print(doc.metadata, score) for (doc,score) in final_results]\n",
"print(\"bye\")"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"es where the space character is not used (like Chinese or Japanese).\n",
"\n",
"The other main feature of SentencePiece is *reversible tokenization*: since there is no special treatment of spaces, decoding the tokens is done simply by concatenating them and replacing the `_`s with spaces -- this results in the normalized text. As we saw earlier, the BERT tokenizer removes repeating spaces, so its tokenization is not reversible.\n",
"\n",
"## Algorithm overview[[algorithm-overview]]\n",
"\n",
"In the following sections, we'll dive into t\n"
]
}
],
"source": [
"print(final_results[0][0].page_content)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "hf_qa_bot",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.3"
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|