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splade-single-encoder.ipynb
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"cell_type": "markdown",
"source": [
"# Sparse Vectors with Qdrant\n",
"\n",
"This is a detailed tutorial on 2 modern, SPLADE based models to make Sparse Vectors and how to use them with Qdrant.\n",
"\n",
"Here is what we'll do next:\n",
"\n",
"1. Create Sparse Vectors\n",
" - Single Model: SPLADE Ensembled Distil Model\n",
" - Document and Query Models: Efficient SPLADE VI-BT-Large\n",
"\n",
"2. Download Dataset with Both Sparse and Dense Vectors\n",
"\n",
"3. Setup Qdrant Cloud\n",
" - **[ACTION NEEDED]** Make a cluster and get a Qdrant URL and Key from [here](https://qdrant.to/cloud?utm_source=qdrant&utm_medium=website&utm_campaign=sparse-vectors&utm_content=article&utm_term=sparse-vectors)\n",
"\n",
"4. Upload: Transform Dataset into Qdrant Points\n",
" - Creating a new collection with write/upload efficiency in mind\n",
"\n",
"5. Example Search using Sparse Vectors\n",
" - Dot Distance with SPLADE\n",
"\n",
"6. Example Search using Dense Vectors\n",
" - Cosine Distance with OpenAI"
],
"metadata": {
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},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"id": "35kLMtuv1jcW"
},
"outputs": [],
"source": [
"!pip install -U transformers qdrant-client -qq"
]
},
{
"cell_type": "markdown",
"source": [
"# Creating Sparse Vector"
],
"metadata": {
"id": "FI1CFiQM3516"
}
},
{
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"source": [
"## Setting up SPLADE Ensembled Distil Model"
],
"metadata": {
"id": "rcXI3MPR5OV-"
}
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"source": [
"from transformers import AutoModelForMaskedLM, AutoTokenizer\n",
"\n",
"model_id = 'naver/splade-cocondenser-ensembledistil'\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
"model = AutoModelForMaskedLM.from_pretrained(model_id)"
],
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"outputId": "7572636c-4848-4d7a-e1e9-defbf6499d3f"
},
"execution_count": 2,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
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],
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{
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},
{
"cell_type": "code",
"source": [
"text = \"\"\"Arthur Robert Ashe Jr. (July 10, 1943 – February 6, 1993) was an American professional tennis player. He won three Grand Slam titles in singles and two in doubles.\"\"\""
],
"metadata": {
"id": "wrEdHytq2-cb"
},
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# tokenizer.convert_ids_to_tokens(tokens[\"input_ids\"][0])"
],
"metadata": {
"id": "eZDvnXt075tX"
},
"execution_count": 4,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Computing the Sparse Vector"
],
"metadata": {
"id": "Y6vyLerV5QME"
}
},
{
"cell_type": "code",
"source": [
"import torch\n",
"\n",
"def compute_vector(text, tokenizer, model):\n",
" \"\"\"\n",
" Computes a vector from logits and attention mask using ReLU, log, and max operations.\n",
"\n",
" Args:\n",
" logits (torch.Tensor): The logits output from a model.\n",
" attention_mask (torch.Tensor): The attention mask corresponding to the input tokens.\n",
"\n",
" Returns:\n",
" torch.Tensor: Computed vector.\n",
" \"\"\"\n",
" tokens = tokenizer(text, return_tensors=\"pt\")\n",
" output = model(**tokens)\n",
" logits, attention_mask = output.logits, tokens.attention_mask\n",
" relu_log = torch.log(1 + torch.relu(logits))\n",
" weighted_log = relu_log * attention_mask.unsqueeze(-1)\n",
" max_val, _ = torch.max(weighted_log, dim=1)\n",
" vec = max_val.squeeze()\n",
"\n",
" return vec, tokens\n",
"\n",
"\n",
"vec, tokens = compute_vector(text, tokenizer=tokenizer, model=model)\n",
"print(vec.shape)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9Bip2lGQ8ES2",
"outputId": "cf94b9c2-8ab3-4087-c440-4461f1f6b86d"
},
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"torch.Size([30522])\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"len(tokens.input_ids[0])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "oigEHi7tAOKN",
"outputId": "80b4bfa0-1d3f-4335-ae21-84a2ef2f088d"
},
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"38"
]
},
"metadata": {},
"execution_count": 6
}
]
},
{
"cell_type": "markdown",
"source": [
"## Interpreting Sparse Vector"
],
"metadata": {
"id": "n865dmXw4I4C"
}
},
{
"cell_type": "code",
"source": [
"def extract_and_map_sparse_vector(vector, tokenizer):\n",
" \"\"\"\n",
" Extracts non-zero elements from a given vector and maps these elements to their human-readable tokens using a tokenizer. The function creates and returns a sorted dictionary where keys are the tokens corresponding to non-zero elements in the vector, and values are the weights of these elements, sorted in descending order of weights.\n",
"\n",
" This function is useful in NLP tasks where you need to understand the significance of different tokens based on a model's output vector. It first identifies non-zero values in the vector, maps them to tokens, and sorts them by weight for better interpretability.\n",
"\n",
" Args:\n",
" vector (torch.Tensor): A PyTorch tensor from which to extract non-zero elements.\n",
" tokenizer: The tokenizer used for tokenization in the model, providing the mapping from tokens to indices.\n",
"\n",
" Returns:\n",
" dict: A sorted dictionary mapping human-readable tokens to their corresponding non-zero weights.\n",
" \"\"\"\n",
"\n",
" # Extract indices and values of non-zero elements in the vector\n",
" cols = vector.nonzero().squeeze().cpu().tolist()\n",
" weights = vector[cols].cpu().tolist()\n",
"\n",
" # Map indices to tokens and create a dictionary\n",
" idx2token = {idx: token for token, idx in tokenizer.get_vocab().items()}\n",
" token_weight_dict = {idx2token[idx]: round(weight, 2) for idx, weight in zip(cols, weights)}\n",
"\n",
" # Sort the dictionary by weights in descending order\n",
" sorted_token_weight_dict = {k: v for k, v in sorted(token_weight_dict.items(), key=lambda item: item[1], reverse=True)}\n",
"\n",
" return sorted_token_weight_dict\n",
"\n",
"# Usage example\n",
"sorted_tokens = extract_and_map_sparse_vector(vec, tokenizer)\n",
"sorted_tokens"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "8eq4LPZ98E3m",
"outputId": "0cb5b8ef-3627-407a-b22c-da560891a67d"
},
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'ashe': 2.95,\n",
" 'arthur': 2.61,\n",
" 'tennis': 2.22,\n",
" 'robert': 1.74,\n",
" 'jr': 1.55,\n",
" 'he': 1.39,\n",
" 'founder': 1.36,\n",
" 'doubles': 1.24,\n",
" 'won': 1.22,\n",
" 'slam': 1.22,\n",
" 'died': 1.19,\n",
" 'singles': 1.1,\n",
" 'was': 1.07,\n",
" 'player': 1.06,\n",
" 'titles': 0.99,\n",
" 'birthday': 0.99,\n",
" 'grand': 0.93,\n",
" 'champion': 0.93,\n",
" 'many': 0.92,\n",
" 'actor': 0.92,\n",
" 'his': 0.89,\n",
" 'death': 0.89,\n",
" '1943': 0.86,\n",
" 'merlin': 0.86,\n",
" 'birth': 0.85,\n",
" 'date': 0.84,\n",
" 'win': 0.83,\n",
" 'professional': 0.81,\n",
" '1993': 0.79,\n",
" 'early': 0.78,\n",
" 'born': 0.77,\n",
" 'medal': 0.75,\n",
" 'double': 0.73,\n",
" 'sr': 0.73,\n",
" 'championship': 0.71,\n",
" 'played': 0.59,\n",
" 'celebrity': 0.59,\n",
" 'nationality': 0.59,\n",
" 'wimbledon': 0.53,\n",
" 'championships': 0.5,\n",
" 'williams': 0.5,\n",
" 'son': 0.49,\n",
" 'carter': 0.49,\n",
" 'tournament': 0.44,\n",
" 'title': 0.43,\n",
" '1991': 0.43,\n",
" 'champ': 0.43,\n",
" 'edgar': 0.4,\n",
" 'alex': 0.38,\n",
" 'medals': 0.37,\n",
" 'him': 0.35,\n",
" 'career': 0.35,\n",
" 'hale': 0.33,\n",
" 'bob': 0.32,\n",
" 'nathan': 0.31,\n",
" 'players': 0.3,\n",
" 'retired': 0.29,\n",
" 'slams': 0.29,\n",
" 'award': 0.26,\n",
" 'gilbert': 0.26,\n",
" 'had': 0.25,\n",
" 'bruce': 0.25,\n",
" 'single': 0.23,\n",
" 'play': 0.23,\n",
" 'andre': 0.22,\n",
" 'wins': 0.21,\n",
" 'finals': 0.2,\n",
" 'doug': 0.2,\n",
" 'jay': 0.18,\n",
" 'three': 0.17,\n",
" 'record': 0.16,\n",
" 'winning': 0.16,\n",
" 'seven': 0.15,\n",
" 'fl': 0.14,\n",
" 'rob': 0.13,\n",
" 'father': 0.12,\n",
" 'chess': 0.12,\n",
" 'athlete': 0.12,\n",
" 'july': 0.1,\n",
" 'open': 0.1,\n",
" 'bailey': 0.1,\n",
" 'serena': 0.09,\n",
" 'ali': 0.08,\n",
" 'profile': 0.07,\n",
" 'tiger': 0.07,\n",
" 'held': 0.06,\n",
" 'ellis': 0.06,\n",
" 'golf': 0.05,\n",
" 'haley': 0.05,\n",
" 'name': 0.04,\n",
" 'jul': 0.04,\n",
" 'king': 0.03,\n",
" 'morris': 0.03,\n",
" 'american': 0.02,\n",
" 'alexander': 0.02,\n",
" 'wright': 0.02,\n",
" '10': 0.01,\n",
" 'charles': 0.01,\n",
" 'artist': 0.01,\n",
" 'israel': 0.01,\n",
" 'davis': 0.01,\n",
" 'hawkins': 0.0}"
]
},
"metadata": {},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"source": [
"len(sorted_tokens)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "QHEGu0mH_PgF",
"outputId": "6f4bb16f-5eb8-4585-d254-19b0095f1855"
},
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"102"
]
},
"metadata": {},
"execution_count": 8
}
]
},
{
"cell_type": "markdown",
"source": [
"## Setting up Efficient SPLADE VI-BT-Large\n",
"\n",
"Efficient SPLADE has a separate document and query encoder.\n",
"\n",
"I'll first illustrate how the document and query encoders differ for the same sentence in the sparse vector.\n",
"\n",
"Next, you'll see how to insert them and use them with [Qdrant Cloud](https://qdrant.to/cloud?utm_source=qdrant&utm_medium=website&utm_campaign=sparse-vectors&utm_content=article&utm_term=sparse-vectors)"
],
"metadata": {
"id": "Tbiscz5T5Yrc"
}
},
{
"cell_type": "code",
"source": [
"from transformers import AutoModelForMaskedLM, AutoTokenizer\n",
"\n",
"doc_model_id = \"naver/efficient-splade-VI-BT-large-doc\"\n",
"doc_tokenizer = AutoTokenizer.from_pretrained(doc_model_id)\n",
"doc_model = AutoModelForMaskedLM.from_pretrained(doc_model_id)\n",
"\n",
"query_model_id = \"naver/efficient-splade-VI-BT-large-query\"\n",
"query_tokenizer = AutoTokenizer.from_pretrained(query_model_id)\n",
"query_model = AutoModelForMaskedLM.from_pretrained(query_model_id)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 401,
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"16b7b05d803d467fa3a557a867dda54b",
"5abb3830a2cc40898468d92453ee8c1e",
"4bad0f73e59545128e760ba28851d485",
"838bf7c2c2674d9fa29371d6ea74da8c",
"5edede8f033c48acbfcd97b3e6fdcb8c",
"450452e841e74cb9b706d26a85ccc871",
"e688886901ea471baff7f46b34d3895e",
"39cba9ab687e46638a79af9ad83b6fb7",
"16a70cb67774457a8464d66968694a94"
]
},
"id": "qTRxi_mj4QSc",
"outputId": "440abbf0-1b98-4c43-89fa-877fa62d4804"
},
"execution_count": 10,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
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],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "88446479fd4d48e993b11b600f493130"
}
},
"metadata": {}
},
{
"output_type": "display_data",
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],
"application/vnd.jupyter.widget-view+json": {
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"version_minor": 0,
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}
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"metadata": {}
},
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}
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"metadata": {}
},
{
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}
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},
{
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"application/vnd.jupyter.widget-view+json": {
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"application/vnd.jupyter.widget-view+json": {
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"metadata": {}
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}
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"## Calculating both Document and Query Vector"
],
"metadata": {
"id": "dbB_axgT9mxj"
}
},
{
"cell_type": "code",
"source": [
"text = \"What a splendid morning!\"\n",
"\n",
"doc_vec, doc_tokens = compute_vector(text, model=doc_model, tokenizer=doc_tokenizer)\n",
"query_vec, query_tokens = compute_vector(text, model=query_model, tokenizer=query_tokenizer)"
],
"metadata": {
"id": "PNHjowDd81rt"
},
"execution_count": 24,
"outputs": []
},
{
"cell_type": "code",
"source": [
"sorted_tokens = extract_and_map_sparse_vector(doc_vec, doc_tokenizer)\n",
"sorted_tokens"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ufI84-Uk9MZi",
"outputId": "cf6d3afe-78c3-4576-fd8e-166c98907088"
},
"execution_count": 25,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'splendid': 2.49,\n",
" 'morning': 1.85,\n",
" 'mornings': 1.21,\n",
" 'beautiful': 1.12,\n",
" 'wonderful': 0.91,\n",
" 'day': 0.67,\n",
" 'paris': 0.62,\n",
" 'magnificent': 0.62,\n",
" 'exquisite': 0.57,\n",
" 'lovely': 0.54,\n",
" 'france': 0.52,\n",
" 'afternoon': 0.5,\n",
" 'weather': 0.44,\n",
" 'french': 0.34,\n",
" 'evening': 0.31,\n",
" 'daytime': 0.31,\n",
" 'perfect': 0.25,\n",
" 'sublime': 0.23,\n",
" '##illy': 0.2,\n",
" 'sunrise': 0.19,\n",
" 'monde': 0.19,\n",
" 'nice': 0.18,\n",
" 'provence': 0.17,\n",
" 'dawn': 0.14,\n",
" 'rising': 0.12,\n",
" 'glorious': 0.1,\n",
" 'greeting': 0.09,\n",
" 'poem': 0.08,\n",
" 'yesterday': 0.08,\n",
" 'clock': 0.07,\n",
" 'eclipse': 0.05,\n",
" 'synonym': 0.04,\n",
" 'surprised': 0.03}"
]
},
"metadata": {},
"execution_count": 25
}
]
},
{
"cell_type": "code",
"source": [
"sorted_tokens = extract_and_map_sparse_vector(query_vec, query_tokenizer)\n",
"sorted_tokens"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "28XQ7wof9S37",
"outputId": "022ec728-4524-43ed-c5cb-631e34d391dd"
},
"execution_count": 26,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'morning': 2.83, '!': 2.28, 'splendid': 2.28, '.': 0.78}"
]
},
"metadata": {},
"execution_count": 26
}
]
},
{
"cell_type": "markdown",
"source": [
"### Observations\n",
"\n",
"1. The document encoder does term expansion to include \"beautiful\", \"wonderful\" and even \"mornings\" -- the plural\n",
"\n",
"2. The query encoder does not do term expansion, but drops \"What\" and adds \".\" punctuation in addition to \"!\""
],
"metadata": {
"id": "VvnT7Rm_9s9-"
}
},
{
"cell_type": "markdown",
"source": [
"## Download the Dataset\n",
"\n",
"Since computing the document sparse vector and a dense vector can take a while, I've made a dataset for you with both:\n",
"\n",
"1. Efficient SPLADE Document Encoder\n",
"2. OpenAI Embedding Ada-002"
],
"metadata": {
"id": "xfW113FX5ljB"
}
},
{
"cell_type": "code",
"source": [
"!pip install -U datasets"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "B_G0Rr0I54XW",
"outputId": "a59891fc-c195-436c-c5d1-dc8c7d4ade64"
},
"execution_count": 13,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting datasets\n",
" Downloading datasets-2.15.0-py3-none-any.whl (521 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m521.2/521.2 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.23.5)\n",
"Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (9.0.0)\n",
"Collecting pyarrow-hotfix (from datasets)\n",
" Downloading pyarrow_hotfix-0.6-py3-none-any.whl (7.9 kB)\n",
"Collecting dill<0.3.8,>=0.3.0 (from datasets)\n",
" Downloading dill-0.3.7-py3-none-any.whl (115 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m10.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (1.5.3)\n",
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"Collecting multiprocess (from datasets)\n",
" Downloading multiprocess-0.70.15-py310-none-any.whl (134 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m13.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: fsspec[http]<=2023.10.0,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.6.0)\n",
"Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.9.1)\n",
"Requirement already satisfied: huggingface-hub>=0.18.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.19.4)\n",
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"Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.4.0)\n",
"Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n",
"Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.18.0->datasets) (3.13.1)\n",
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"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.3.2)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.6)\n",
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"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2023.11.17)\n",
"Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.3.post1)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
"Installing collected packages: pyarrow-hotfix, dill, multiprocess, datasets\n",
"Successfully installed datasets-2.15.0 dill-0.3.7 multiprocess-0.70.15 pyarrow-hotfix-0.6\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from datasets import load_dataset\n",
"\n",
"dataset = load_dataset(\"nirantk/dbpedia-entities-efficient-splade-100K\", split=\"train\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 177,
"referenced_widgets": [
"21c047d142fb4ce8ba003e5bff7f04ce",
"ca0ea0dd53ff43458272d09424179346",
"0f2b3cc0c04044adbdceaacc0ea11cbc",
"451641fd45774ddbb9e5bc5c610b09fa",
"4c98adbd44f44bbabe6228d4fba1e974",
"eaedc824d30049b3b07163a9c02c9deb",
"38832c1ddb56443db5510101825e119f",
"50340af25e5b4bad9af8e5fa6b3ec825",
"eafc7b0d7c8846cdbfd8f1faa962b2a6",
"532a7451d23d4296b283b65fac73e1e7",
"2076a6b9b02340e2b86aa18fd35a86a8",
"e04ff9b96ee84cbaa9a82005d1262e30",
"253000f0a6864949b6f5ed590389b84f",
"aa8dbcc6756d43b69006a2d6ac4f99e6",
"8b3d1a35a6b44005a1cd885edc576c47",
"7c663fc61bc14fbdbade1edd8c57eea6",
"f7a558dc764441aeb447c27cc802a607",
"a5a6c524be13498bbc67bf7ba831bb2c",
"27ef5041b1374fa7a89266e34ea3985f",
"a62581b3276046e09fc59809c1c275bd",
"c390dc6feb764bd78df5817a41ffd64b",
"5684380e666a4e53bd78c558b283ed16",
"97d70232fd6b497db417f992d94d811d",
"e773d443256f4df9a32a9e57f1d3b5e3",
"181e7a976e4e4e45b22259b0c4dbe12e",
"e23a1d1ba344465e8d34284e77afa3b9",
"59c04dd2b331430582cafc6bae778cb7",
"9e2a9569c82e4031b02d16953da0543c",
"deaee6e1cd0a4dbaab31977644550c9d",
"2f20c0e1c26f4a67add7c889727383f4",
"145639e7331e4e709c1d2be4d2aa5aa5",
"67f50fd393b34f938cf310471710c1f6",
"b768fa13b5ff4b909b7ed099311d2469",
"8a8e72c9f6ae4576abc1c32b903ef984",
"68487b8ccc014c1aa3a679abbab5fde8",
"2eb5cfe55be641bbba1e6ec9144aeb6d",
"b431728f6fe24314b3c7bafd326bdcc2",
"fe89bdc5a9d94662855b1d1b9a655e75",
"df6ecccff8a34ac1bb95987eba43b67b",
"03ffa3f3476247aaa944e1c6e0ca6e20",
"73881b2ffbf64d679bf555a2841f3306",
"c69852603bcd4f7ba13d3a02b05361e5",
"4c6cf7888976496da4ca3464bdaf009a",
"ff79d734796744fd9938fb6c8a07a5c6",
"d97d88cefd594b22b3d1c80e1deeea6c",
"4dfed8ea4a7946cba5ab25616d29e999",
"4f95034112b54baab4c69a97f7e9b536",
"8fed100901df43508900201a2ffd2ea0",
"ada1a9dd7e8d473dbb01289556b1751d",
"78de6310c48642cb9879e1be3b34f55b",
"1a912c600ea441769ac263e77560bfaa",
"b29046bda71b449f9b2b21f9da40ed4e",
"c32cd70f19ed44b4a6d33f2415df8aaa",
"0c4b56296f2e4949a1e495ccd9d31a35",
"019d83f7d3774538b67fcc3abe5744f2"
]
},
"id": "5eHxzYQk569F",
"outputId": "fb3848fe-ca31-40cf-a9b8-463d1f86befd"
},
"execution_count": 17,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Resolving data files: 0%| | 0/26 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "21c047d142fb4ce8ba003e5bff7f04ce"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Resolving data files: 0%| | 0/26 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "e04ff9b96ee84cbaa9a82005d1262e30"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading data files: 0%| | 0/1 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "97d70232fd6b497db417f992d94d811d"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Extracting data files: 0%| | 0/1 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "8a8e72c9f6ae4576abc1c32b903ef984"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Generating train split: 0%| | 0/100000 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "d97d88cefd594b22b3d1c80e1deeea6c"
}
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"dataset = dataset.select(range(10000))\n",
"dataset"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "swbLeRqg6vw9",
"outputId": "ed05ce06-313a-4bfb-bedc-6812c7cda742"
},
"execution_count": 19,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Dataset({\n",
" features: ['_id', 'title', 'text', 'openai', 'splade'],\n",
" num_rows: 10000\n",
"})"
]
},
"metadata": {},
"execution_count": 19
}
]
},
{
"cell_type": "markdown",
"source": [
"# Setting up Qdrant\n",
"\n",
"1. You'll need to"
],
"metadata": {
"id": "i2Y4eIGb-8z1"
}
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "Znd2M7W5-_KS"
},
"execution_count": null,
"outputs": []
}
]
}
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