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For this purpose, we first embed papers using a model (e.g. [spectre2](https://huggingface.co/allenai/specter2_base) by default) into dense representations. After clustering them, we apply t-SNE to project them into 2 dimensions for visualization.\n", "\n", "**Before running this colab, make sure the runtime type is set to GPU.** We check the availability of GPUs in the \"Checks\" section.\n", "\n", "The plot will be generated using [plotly](https://plotly.com/python/getting-started/)." ], "metadata": { "id": "AeaHYgzwgyOF" } }, { "cell_type": "code", "source": [ "# @title XML file name to download from acl-anthology github page\n", "FILE_NAME = '2023.acl.xml' # @param {type:\"string\"}" ], "metadata": { "cellView": "form", "id": "mQ31dArhTOmd" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# @title Model name from huggingface\n", "MODEL_NAME = 'allenai/specter2_base' # @param {type:\"string\"}\n", "\n", "ADAPTER_NAME = \"\" # @param {type:\"string\"}" ], "metadata": { "cellView": "form", "id": "jSt0Jpueanvn" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# @title Inference args\n", "BATCH_SIZE = 64 # @param {type:\"integer\"}" ], "metadata": { "cellView": "form", "id": "HryCbmPBcw5V" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# @title Visualization args\n", "NUM_CLUSTERS = 50 # @param {type:\"integer\"}" ], "metadata": { "cellView": "form", "id": "qyedQTz5ezl4" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## Setup" ], "metadata": { "id": "jXbz3X1sUHcr" } }, { "cell_type": "markdown", "source": [ "### Install dependencies" ], "metadata": { "id": "O9n1VhtvUQxS" } }, { "cell_type": "code", "source": [ "!pip install datasets\n", "!pip install transformers\n", "!pip install adapter-transformers==3.0.1" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "d0XchP9jUOhb", "outputId": "133dcd54-f647-44bf-e5d4-f91383be6640" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[33mWARNING: Ignoring invalid 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requests->adapter-transformers==3.0.1) (2023.7.22)\n", "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from sacremoses->adapter-transformers==3.0.1) (1.16.0)\n", "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from sacremoses->adapter-transformers==3.0.1) (8.1.7)\n", "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from sacremoses->adapter-transformers==3.0.1) (1.3.2)\n", "\u001b[33mWARNING: Ignoring invalid distribution -lotly (/usr/local/lib/python3.10/dist-packages)\u001b[0m\u001b[33m\n", "\u001b[0m" ] } ] }, { "cell_type": "markdown", "source": [ "### Imports" ], "metadata": { "id": "c0MMhYc_UKfG" } }, { "cell_type": "code", "source": [ "import json\n", "import os\n", "import re\n", "from functools import partial\n", "from tqdm.auto import tqdm\n", "from typing import Any, Iterable, Mapping\n", "\n", "import datasets\n", "import numpy as np\n", "import pandas as pd\n", "import torch\n", "from torch.utils.data import DataLoader\n", "from transformers import DataCollatorWithPadding, AutoModel, AutoTokenizer, AutoConfig\n", "from sklearn.cluster import KMeans\n", "from sklearn.manifold import TSNE\n", "\n", "import plotly.express as px" ], "metadata": { "id": "AJULv3wPUG0z" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Checks" ], "metadata": { "id": "BY2W1tBTUVWN" } }, { "cell_type": "code", "source": [ "#@markdown **Check GPU type**\n", "!nvidia-smi -L\n", "\n", "#@markdown **Check PyTorch version**\n", "print(\"PyTorch version:\", torch.__version__)\n", "print(\"CUDA version:\", torch.version.cuda)\n", "print(\"#GPUs:\", torch.cuda.device_count())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "cellView": "form", "id": "jtYjxTfuUXUb", "outputId": "4f62a4ba-8b8b-462d-caa6-e002ec2d7b1b" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "GPU 0: Tesla T4 (UUID: GPU-5e2802f0-3a72-ee6b-56ce-fc17d7e725c4)\n", "PyTorch version: 2.0.1+cu118\n", "CUDA version: 11.8\n", "#GPUs: 1\n" ] } ] }, { "cell_type": "markdown", "source": [ "### Load Huggingface Stuff" ], "metadata": { "id": "osH8mbM4aCw0" } }, { "cell_type": "code", "source": [ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n", "\n", "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n", "\n", "config = AutoConfig.from_pretrained(MODEL_NAME, return_dict=True, output_hidden_states=True)\n", "\n", "model = AutoModel.from_pretrained(MODEL_NAME, config=config)\n", "if ADAPTER_NAME:\n", " model.load_adapter(\n", " ADAPTER_NAME,\n", " source=\"hf\",\n", " set_active=True,\n", " )\n", "\n", "model.eval()\n", "model.to(\"cuda\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6j9EGcCSZ8Z_", "outputId": "1edfabc5-35b0-47d6-8c58-8cf1e35ca5fe" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "BertModel(\n", " (shared_parameters): ModuleDict()\n", " (invertible_adapters): ModuleDict()\n", " (embeddings): BertEmbeddings(\n", " (word_embeddings): Embedding(31090, 768, padding_idx=0)\n", " (position_embeddings): Embedding(512, 768)\n", " (token_type_embeddings): Embedding(2, 768)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (encoder): BertEncoder(\n", " (layer): ModuleList(\n", " (0-11): 12 x BertLayer(\n", " (attention): BertAttention(\n", " (self): BertSelfAttention(\n", " (query): Linear(in_features=768, out_features=768, bias=True)\n", " (key): Linear(in_features=768, out_features=768, bias=True)\n", " (value): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " (prefix_tuning): PrefixTuningShim(\n", " (pool): PrefixTuningPool(\n", " (prefix_tunings): ModuleDict()\n", " )\n", " )\n", " )\n", " (output): BertSelfOutput(\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " (adapters): ModuleDict()\n", " (adapter_fusion_layer): ModuleDict()\n", " )\n", " )\n", " (intermediate): BertIntermediate(\n", " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", " (intermediate_act_fn): GELUActivation()\n", " )\n", " (output): BertOutput(\n", " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " (adapters): ModuleDict()\n", " (adapter_fusion_layer): ModuleDict()\n", " )\n", " )\n", " )\n", " )\n", " (pooler): BertPooler(\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", " (activation): Tanh()\n", " )\n", " (prefix_tuning): PrefixTuningPool(\n", " (prefix_tunings): ModuleDict()\n", " )\n", ")" ] }, "metadata": {}, "execution_count": 8 } ] }, { "cell_type": "markdown", "source": [ "## Preparing Data" ], "metadata": { "id": "v9olGFFaP6Un" } }, { "cell_type": "markdown", "source": [ "### Downloading from acl-anthology github" ], "metadata": { "id": "YvFxyYEpP_wj" } }, { "cell_type": "markdown", "source": [ "The paper information can be downloaded from `acl-anthology` github page in the XML format: https://github.com/acl-org/acl-anthology/tree/master/data/xml/" ], "metadata": { "id": "Vm022cIzSorc" } }, { "cell_type": "code", "source": [ "!rm -f $FILE_NAME\n", "!wget \"https://raw.githubusercontent.com/acl-org/acl-anthology/master/data/xml/$FILE_NAME\"\n", "\n", "assert os.path.exists(FILE_NAME), \"Downloaded file exists\"" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "knMDRgK8Sfl_", "outputId": "ea0abab7-fe9f-4ffa-e627-1b3d4f5a8953" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "--2023-09-20 03:28:48-- https://raw.githubusercontent.com/acl-org/acl-anthology/master/data/xml/2023.acl.xml\n", "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n", "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 2597735 (2.5M) [text/plain]\n", "Saving to: ‘2023.acl.xml’\n", "\n", "2023.acl.xml 100%[===================>] 2.48M --.-KB/s in 0.02s \n", "\n", "2023-09-20 03:28:49 (142 MB/s) - ‘2023.acl.xml’ saved [2597735/2597735]\n", "\n" ] } ] }, { "cell_type": "markdown", "source": [ "download the xml file from this [link](https://github.com/acl-org/acl-anthology/tree/006c7247a6bf0ff859bfd3aab6ea6a19452580ad/data/xml). \n", "Convert the xml files to jsonl files by running the following code" ], "metadata": { "id": "2KFobPmUbu7j" } }, { "cell_type": "markdown", "source": [ "### Parsing" ], "metadata": { "id": "CUD4LOJlUmMj" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "WXQgTZQ103g7", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "4edc4fd1-0a7f-4419-ffa3-e1d9f259a139" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "#papers founds in 2023.acl.xml: 1249\n" ] } ], "source": [ "import xml.etree.ElementTree as ET\n", "\n", "URL_MAPPINGS = dict(\n", " D=\"emnlp\",\n", " N=\"naacl\",\n", " P=\"acl\",\n", " Q=\"tacl\",\n", ")\n", "\n", "def xml_to_jsonl(xml_file: os.PathLike) -> Iterable[Mapping[str, Any]]:\n", " tree = ET.parse(xml_file)\n", " root = tree.getroot()\n", " papers = root.findall(\".//paper\")\n", "\n", " for paper in papers:\n", " paper_dict = {}\n", " paper_dict[\"title\"] = \"\".join(paper.find(\"title\").itertext())\n", "\n", " authors = []\n", " for author in paper.findall(\"author\"):\n", " first_name = author.findtext(\"first\")\n", " last_name = author.findtext(\"last\")\n", " authors.append(f\"{first_name} {last_name}\")\n", " paper_dict[\"authors\"] = authors\n", "\n", " paper_dict[\"abstract\"] = \"\" if paper.find(\"abstract\")==None else \"\".join(paper.find(\"abstract\").itertext())\n", " paper_dict[\"pages\"] = paper.findtext(\"pages\")\n", " paper_dict[\"url\"] = paper.findtext(\"url\")\n", " paper_dict[\"bibkey\"] = paper.findtext(\"bibkey\")\n", " paper_dict[\"doi\"] = paper.findtext(\"doi\")\n", "\n", " conference, paper_type = None, None\n", " matched = re.match(r\"(\\d+)\\.(\\w+)-(\\w+)\\.\\d+\", paper_dict[\"url\"])\n", " if matched:\n", " year = int(matched.group(1))\n", " conference = matched.group(2)\n", " paper_type = matched.group(3)\n", " else:\n", " bibs = paper_dict[\"bibkey\"].split(\"-\")\n", " for b in range(len(bibs) - 1, -1, -1):\n", " try:\n", " year = int(bibs[b])\n", " break\n", " except ValueError:\n", " pass\n", "\n", " conference = URL_MAPPINGS.get(paper_dict[\"url\"][0], None)\n", "\n", " paper_dict[\"source\"] = conference\n", " paper_dict[\"year\"] = year\n", " paper_dict[\"publication_type\"] = paper_type\n", "\n", " yield paper_dict\n", "\n", "papers = list(xml_to_jsonl(FILE_NAME))\n", "\n", "print(f\"#papers founds in {FILE_NAME}: {len(papers)}\")" ] }, { "cell_type": "markdown", "source": [ "## Encode" ], "metadata": { "id": "3yXoFyHhdd25" } }, { "cell_type": "markdown", "source": [ "### Creating DataLoader" ], "metadata": { "id": "ml0g17tYX2jP" } }, { "cell_type": "code", "source": [ "dataset = datasets.Dataset.from_list(\n", " [{\"text\": p[\"title\"] + tokenizer.sep_token + (p[\"abstract\"] or \"\"), \"idx\": i + 1} for i, p in enumerate(papers)]\n", ")\n", "\n", "tokenize_fn = lambda batch: tokenizer(batch[\"text\"], padding=True, truncation=True, max_length=512)\n", "dataset = dataset.map(tokenize_fn, batched=True)\n", "\n", "columns = [\"idx\", \"input_ids\", \"attention_mask\"]\n", "if \"token_type_ids\" in dataset.column_names:\n", " columns.append(\"token_type_ids\")\n", "\n", "data_loader = DataLoader(\n", " dataset.with_format(\"torch\", columns=columns),\n", " collate_fn=DataCollatorWithPadding(tokenizer),\n", " batch_size=BATCH_SIZE,\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 153, "referenced_widgets": [ "1619b254fcbb4cb880d1be5685c74dbc", "607c048fb1634a7689e355036c144984", "869501d4d38e46f184a66423d93a2745", "c03dc1381a0c430182fe86d8a100b249", "b6d31f4cebc84ef0a563d41482b14cc2", "9ebec18dbdff4913a4902429a726b9e0", "c9dc6fbcf53a4c9fb53716a18db6ffbe", "c9ef5bf8ff3e44358c4557f74c3e379e", "0cc5f439950e49eaa4d417396e21e2c4", "1479cc60b4ac4864b46b592dc1050157", "a9e75caedfbf46e0bd0effe1e60065cd" ] }, "id": "sCG1iVa4X7ye", "outputId": "a287df82-3448-4b24-9e26-582bd7b4b180" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Map: 0%| | 0/1249 [00:00 5:\n", " return \", \".join(list_of_authors[:5]) + \", et al.\"\n", " elif len(list_of_authors) > 2:\n", " return \", \".join(list_of_authors[:-1]) + \", and \" + list_of_authors[-1]\n", " else:\n", " return \" and \".join(list_of_authors)\n", "\n", "\n", "for i, (point, c, p) in enumerate(zip(reduced_embeds, clusters, papers)):\n", " p[\"x\"] = point[0]\n", " p[\"y\"] = point[1]\n", " p[\"cluster\"] = c\n", " p[\"authors_trimmed\"] = [(x[x.index(\",\") + 1 :].strip() + \" \" + x.split(\",\")[0].strip()) if \",\" in x else x for x in p[\"authors\"]]\n", " if \"publication_type\" in p:\n", " p[\"type\"] = p.pop(\"publication_type\")\n", "\n", "df = pd.DataFrame(papers)\n", "\n", "fig = px.scatter(\n", " df,\n", " x=\"x\",\n", " y=\"y\",\n", " color=\"cluster\",\n", " width=1000,\n", " height=800,\n", " custom_data=(\"title\", \"authors_trimmed\", \"year\", \"source\", \"type\"),\n", " color_continuous_scale=\"fall\",\n", ")\n", "fig.update_traces(\n", " hovertemplate=\"%{customdata[0]}
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