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| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "provenance": [], | |
| "collapsed_sections": [], | |
| "authorship_tag": "ABX9TyM2lAGsvRkRpJ9zceJ2UqzO", | |
| "include_colab_link": true | |
| }, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3" | |
| }, | |
| "language_info": { | |
| "name": "python" | |
| } | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/raven44099/32babed67e122427ec36e1fafd142c08/entitylinking_genre_colab_minimalcode.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## 1. colab GENRE\n", | |
| "at the timepoint 2022.Oct.27, this is a fully functional colab script to run Facebook AI's entity linker called 'GENRE' (https://github.com/facebookresearch/GENRE). This file can be used as an entry point to write a colab script that also includes training (but I haven't written that yet).\n", | |
| "\n", | |
| "A 2nd approach that utilizes huggingface-transfromers package is also provided in one cell, but that code is redundant for the 1st approach." | |
| ], | |
| "metadata": { | |
| "id": "O5ixnsog3C05" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "#@title 1.2. huggingface GENRE\n", | |
| "# # %%capture\n", | |
| "# !pip install transformers\n", | |
| "\n", | |
| "# from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n", | |
| "\n", | |
| "# # OPTIONAL: load the prefix tree (trie), you need to additionally download\n", | |
| "# # https://huggingface.co/facebook/genre-kilt/blob/main/trie.py and \n", | |
| "# # https://huggingface.co/facebook/genre-kilt/blob/main/kilt_titles_trie_dict.pkl\n", | |
| "# # import pickle\n", | |
| "# # from trie import Trie\n", | |
| "# # with open(\"kilt_titles_trie_dict.pkl\", \"rb\") as f:\n", | |
| "# # trie = Trie.load_from_dict(pickle.load(f))\n", | |
| "\n", | |
| "# tokenizer = AutoTokenizer.from_pretrained(\"facebook/genre-kilt\")\n", | |
| "# model = AutoModelForSeq2SeqLM.from_pretrained(\"facebook/genre-kilt\").eval()\n", | |
| "\n", | |
| "# sentences = [\"Einstein was a German physicist.\"]\n", | |
| "\n", | |
| "# outputs = model.generate(\n", | |
| "# **tokenizer(sentences, return_tensors=\"pt\"),\n", | |
| "# num_beams=5,\n", | |
| "# num_return_sequences=5,\n", | |
| "# # OPTIONAL: use constrained beam search\n", | |
| "# # prefix_allowed_tokens_fn=lambda batch_id, sent: trie.get(sent.tolist()),\n", | |
| "# )\n", | |
| "\n", | |
| "# tokenizer.batch_decode(outputs, skip_special_tokens=True)\n", | |
| "\n", | |
| "# sentences = [\" Proteins are well known in the realm of [START] synthetic biology, where E. coli oftenis used to produce the catalyst for experiments associated with the INS gene ..\"]\n", | |
| "\n", | |
| "# outputs = model.generate(\n", | |
| "# **tokenizer(sentences, return_tensors=\"pt\"),\n", | |
| "# num_beams=5,\n", | |
| "# num_return_sequences=5,\n", | |
| "# # OPTIONAL: use constrained beam search\n", | |
| "# # prefix_allowed_tokens_fn=lambda batch_id, sent: trie.get(sent.tolist()),\n", | |
| "# )\n", | |
| "\n", | |
| "# tokenizer.batch_decode(outputs, skip_special_tokens=True)" | |
| ], | |
| "metadata": { | |
| "id": "FogMwcT3I0Hi" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## 1.3. download GENRE & configure" | |
| ], | |
| "metadata": { | |
| "id": "SriJcWRx3Ae0" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "#@title clone GENRE \n", | |
| "# %%capture\n", | |
| "%cd /content/\n", | |
| "!git clone https://github.com/facebookresearch/GENRE" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "sz7aq_MDSxJi", | |
| "outputId": "8d766299-b421-4e90-c3ca-9c7e00e5c400", | |
| "cellView": "form" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "/content\n", | |
| "Cloning into 'GENRE'...\n", | |
| "remote: Enumerating objects: 457, done.\u001b[K\n", | |
| "remote: Counting objects: 100% (173/173), done.\u001b[K\n", | |
| "remote: Compressing objects: 100% (92/92), done.\u001b[K\n", | |
| "remote: Total 457 (delta 114), reused 102 (delta 78), pack-reused 284\u001b[K\n", | |
| "Receiving objects: 100% (457/457), 11.00 MiB | 25.36 MiB/s, done.\n", | |
| "Resolving deltas: 100% (261/261), done.\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "#@title clone & install fairseq\n", | |
| "%%capture\n", | |
| "!git clone --branch fixing_prefix_allowed_tokens_fn https://github.com/nicola-decao/fairseq\n", | |
| "!pwd\n", | |
| "%cd /content/fairseq\n", | |
| "! pip install --editable .\n", | |
| "#! pip install --editable ./" | |
| ], | |
| "metadata": { | |
| "id": "98AaBLuiTkGL", | |
| "cellView": "form" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "'''this path.append must maybe be before runtime restart.'''\n", | |
| "import sys\n", | |
| "sys.path.append(\"/content/fairseq/\")\n", | |
| "sys.path.append(\"/content/GENRE\")" | |
| ], | |
| "metadata": { | |
| "id": "2Htb8TkMCJs8" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "<font color='red'> restart runtime was initiially necessary here, now not anymore...! </font>" | |
| ], | |
| "metadata": { | |
| "id": "wSXi7pqM4nST" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "%%capture\n", | |
| "!pip install jsonlines" | |
| ], | |
| "metadata": { | |
| "id": "esf4epyTXBFX" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "%cd /content/\n", | |
| "!mkdir data\n", | |
| "%cd data\n", | |
| "### KILT prefix tree\n", | |
| "!wget http://dl.fbaipublicfiles.com/GENRE/kilt_titles_trie_dict.pkl" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "0S24jRdQgZQU", | |
| "outputId": "472afe65-39e1-4d6e-ba49-e4d489648549" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "/content\n", | |
| "/content/data\n", | |
| "--2022-11-02 06:17:52-- http://dl.fbaipublicfiles.com/GENRE/kilt_titles_trie_dict.pkl\n", | |
| "Resolving dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)... 104.22.75.142, 172.67.9.4, 104.22.74.142, ...\n", | |
| "Connecting to dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)|104.22.75.142|:80... connected.\n", | |
| "HTTP request sent, awaiting response... 200 OK\n", | |
| "Length: 215214973 (205M) [application/octet-stream]\n", | |
| "Saving to: ‘kilt_titles_trie_dict.pkl’\n", | |
| "\n", | |
| "kilt_titles_trie_di 100%[===================>] 205.24M 26.0MB/s in 8.5s \n", | |
| "\n", | |
| "2022-11-02 06:18:01 (24.3 MB/s) - ‘kilt_titles_trie_dict.pkl’ saved [215214973/215214973]\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| " from GENRE/scripts_genre/download_all_models.sh #" | |
| ], | |
| "metadata": { | |
| "id": "j2cfAYgzeQrK" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "'''\n", | |
| "This code sniped is missing in the README.md,\n", | |
| "but can be found at 'GENRE/scripts_genre/download_all_models.sh'\n", | |
| "'''\n", | |
| "%cd /content/\n", | |
| "!mkdir models\n", | |
| "%cd models\n", | |
| "!wget http://dl.fbaipublicfiles.com/GENRE/fairseq_entity_disambiguation_aidayago.tar.gz\n", | |
| "!tar -zxvf fairseq_entity_disambiguation_aidayago.tar.gz" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "14ChP3M-duHJ", | |
| "outputId": "d8985f54-af2f-4831-d817-56e50dee75c0" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "/content\n", | |
| "/content/models\n", | |
| "--2022-11-02 06:18:01-- http://dl.fbaipublicfiles.com/GENRE/fairseq_entity_disambiguation_aidayago.tar.gz\n", | |
| "Resolving dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)... 104.22.75.142, 172.67.9.4, 104.22.74.142, ...\n", | |
| "Connecting to dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)|104.22.75.142|:80... connected.\n", | |
| "HTTP request sent, awaiting response... 200 OK\n", | |
| "Length: 1201544043 (1.1G) [application/gzip]\n", | |
| "Saving to: ‘fairseq_entity_disambiguation_aidayago.tar.gz’\n", | |
| "\n", | |
| "fairseq_entity_disa 100%[===================>] 1.12G 32.0MB/s in 38s \n", | |
| "\n", | |
| "2022-11-02 06:18:39 (30.3 MB/s) - ‘fairseq_entity_disambiguation_aidayago.tar.gz’ saved [1201544043/1201544043]\n", | |
| "\n", | |
| "fairseq_entity_disambiguation_aidayago/\n", | |
| "fairseq_entity_disambiguation_aidayago/dict.source.txt\n", | |
| "fairseq_entity_disambiguation_aidayago/dict.target.txt\n", | |
| "fairseq_entity_disambiguation_aidayago/model.pt\n", | |
| "fairseq_entity_disambiguation_aidayago/encoder.json\n", | |
| "fairseq_entity_disambiguation_aidayago/vocab.bpe\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import pickle\n", | |
| "from genre.trie import Trie\n", | |
| "%cd /content\n", | |
| "# load the prefix tree (trie)\n", | |
| "with open(\"data/kilt_titles_trie_dict.pkl\", \"rb\") as f:\n", | |
| " trie = Trie.load_from_dict(pickle.load(f))" | |
| ], | |
| "metadata": { | |
| "id": "ZRgMvhYNgP_W", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "64187ccf-3d20-45a3-fec7-ae7ceaba959e" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "/content\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## 1.3 the model" | |
| ], | |
| "metadata": { | |
| "id": "O74GGKj8qwdw" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from genre.fairseq_model import GENRE\n", | |
| "%cd /content/\n", | |
| "model = GENRE.from_pretrained(\"/content/models/fairseq_entity_disambiguation_aidayago\").eval()\n", | |
| "\n", | |
| "# for huggingface/transformers\n", | |
| "# from genre.hf_model import GENRE\n", | |
| "# model = GENRE.from_pretrained(\"../models/hf_entity_disambiguation_aidayago\").eval()" | |
| ], | |
| "metadata": { | |
| "id": "RGsYFjgARz__", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "d2b74c84-a029-401c-f34c-1d480ddea70d" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "/content\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "1042301B [00:00, 3081465.33B/s]\n", | |
| "456318B [00:00, 882396.44B/s]\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# myPhrase = [\"Einstein was a [START_ENT] German [END_ENT] physicist.\"]\n", | |
| "\n", | |
| "myPhrase = [' Proteins are well known in the realm of synthetic biology, where E. coli often is used to produce material for experiments associated with the INS gene.',\n", | |
| " 'Experiments associated with the [START_ENT] INS gene [END_ENT] or in very special cases other sources.',\n", | |
| " 'Insulin is a small molecule. experiments associated with the [START_ENT] INS gene [END_ENT] or in very special cases other sources.']\n", | |
| "model.sample(\n", | |
| " sentences=myPhrase, \n", | |
| " prefix_allowed_tokens_fn=lambda batch_id, sent: trie.get(sent.tolist()),\n", | |
| ")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "5HrhxvrHf-0r", | |
| "outputId": "9ea8a278-c8e0-415e-c22e-41e7f5ef0925" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "/content/fairseq/fairseq/search.py:205: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", | |
| " beams_buf = indices_buf // vocab_size\n", | |
| "/content/fairseq/fairseq/sequence_generator.py:659: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n", | |
| " unfin_idx = idx // beam_size\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "[[{'text': 'Biosynthesis', 'score': tensor(-1.4936)},\n", | |
| " {'text': 'Sodium silicate', 'score': tensor(-1.5992)},\n", | |
| " {'text': 'Biopterin', 'score': tensor(-1.6096)},\n", | |
| " {'text': 'Sodium silicide', 'score': tensor(-1.9425)},\n", | |
| " {'text': 'Biopterin-dependent aromatic amino acid hydroxylase',\n", | |
| " 'score': tensor(-2.4378)}],\n", | |
| " [{'text': 'Inositol trisphosphate', 'score': tensor(-0.1708)},\n", | |
| " {'text': 'Insulin-like growth factor', 'score': tensor(-0.5887)},\n", | |
| " {'text': 'Immunoglobulin E', 'score': tensor(-0.9796)},\n", | |
| " {'text': 'Immunoglobulin A', 'score': tensor(-1.0120)},\n", | |
| " {'text': 'Immunosuppressive drug', 'score': tensor(-1.0889)}],\n", | |
| " [{'text': 'Insulin', 'score': tensor(-0.3428)},\n", | |
| " {'text': 'Insulin-like growth factor', 'score': tensor(-0.3444)},\n", | |
| " {'text': 'Insulin receptor', 'score': tensor(-1.1370)},\n", | |
| " {'text': 'Insulin resistance', 'score': tensor(-1.5255)},\n", | |
| " {'text': 'Insulin receptor substrate', 'score': tensor(-2.6037)}]]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 9 | |
| } | |
| ] | |
| } | |
| ] | |
| } |
Thanks for your comment 😄 . Appreciate it!
Nice! Do you have mGENRE as well?
No... I'm sorry.
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This is really helpful. Thanks!