From 7ca1bc2a57e5036174b5dd31a117ef809a392b51 Mon Sep 17 00:00:00 2001
From: Maja Jablonska <majajjablonska@gmail.com>
Date: Wed, 22 Nov 2023 02:47:39 +1100
Subject: [PATCH] Add a Prediction.ipynb notebook
---
notebooks/Prediction.ipynb | 355 ++++++++++++++++++
.../{model_training.ipynb => Training.ipynb} | 0
2 files changed, 355 insertions(+)
create mode 100644 notebooks/Prediction.ipynb
rename notebooks/{model_training.ipynb => Training.ipynb} (100%)
diff --git a/notebooks/Prediction.ipynb b/notebooks/Prediction.ipynb
new file mode 100644
index 0000000..b0ee520
--- /dev/null
+++ b/notebooks/Prediction.ipynb
@@ -0,0 +1,355 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Prediction\n",
+ "\n",
+ "This notebook will demonstrate how to use a pretrained COMBO model."
+ ],
+ "metadata": {
+ "collapsed": false
+ },
+ "id": "83e245f2c41847f3"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "outputs": [],
+ "source": [
+ "from combo.predict import COMBO"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-11-21T15:15:18.493035Z",
+ "start_time": "2023-11-21T15:15:14.638080Z"
+ }
+ },
+ "id": "d61cad94b3cde64f"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "initial_id",
+ "metadata": {
+ "collapsed": true,
+ "ExecuteTime": {
+ "end_time": "2023-11-21T15:37:53.106009Z",
+ "start_time": "2023-11-21T15:15:18.482336Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 319M/319M [22:15<00:00, 251kB/s] \n",
+ "Some weights of the model checkpoint at allegro/herbert-base-cased were not used when initializing BertModel: ['cls.predictions.decoder.weight', 'cls.predictions.decoder.bias', 'cls.predictions.transform.dense.weight', 'cls.sso.sso_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.sso.sso_relationship.bias', 'cls.predictions.bias']\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": [
+ "Using model LAMBO-UD_Polish-PDB\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": "loading instances: 0it [00:00, ?it/s]",
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "382d00831a14426289956738bd3c4266"
+ }
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "[2023-11-21 15:37:51 UTC Loading archive] Error while loading Training Data Loader: File with path '/net/pr2/projects/plgrid/plgg_nlp/maja/ud-treebanks-v2.13/UD_Polish-PDB/pl_pdb-ud-train.conllu' does not exist!. Setting Data Loader to None\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Using model LAMBO-UD_Polish-PDB\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": "loading instances: 0it [00:00, ?it/s]",
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "b973a8d39ad94eaab4167e5de5418e1c"
+ }
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "[2023-11-21 15:37:51 UTC Loading archive] Error while loading Validation Data Loader: File with path '/net/pr2/projects/plgrid/plgg_nlp/maja/ud-treebanks-v2.13/UD_Polish-PDB/pl_pdb-ud-dev.conllu' does not exist!. Setting Data Loader to None\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Using model LAMBO-UD_Polish-PDB\n"
+ ]
+ }
+ ],
+ "source": [
+ "predictor = COMBO.from_pretrained('polish-herbert-base-ud213')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Predict a sentence:"
+ ],
+ "metadata": {
+ "collapsed": false
+ },
+ "id": "ad45699f49f49450"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "outputs": [],
+ "source": [
+ "predictions = predictor(\"Cześć! To jest przykładowe zdanie.\")"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-11-21T15:41:38.889677Z",
+ "start_time": "2023-11-21T15:41:38.443880Z"
+ }
+ },
+ "id": "a397812ec6ffbd8a"
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Predictions are a list of sentences with corresponding metadata."
+ ],
+ "metadata": {
+ "collapsed": false
+ },
+ "id": "b7bae793bca448cb"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "[Sentence(tokens=[Cześć, !], metadata=matrix([[7.14260619e-03, 9.92857337e-01, 1.21313585e-18],\n [8.81941795e-01, 1.18058220e-01, 8.37145743e-16],\n [1.13332543e-08, 1.00000000e+00, 6.85589674e-09]])),\n Sentence(tokens=[To, jest, przykładowe, zdanie, .], metadata=matrix([[2.30086403e-06, 6.01210148e-09, 1.12105099e-08, 2.96070528e-08,\n 9.99997616e-01, 2.80899673e-14],\n [1.08029099e-05, 5.30108446e-09, 1.08553975e-08, 1.58541518e-08,\n 9.99989152e-01, 1.11981080e-15],\n [5.74975857e-05, 8.36746423e-08, 1.03298721e-08, 2.51524526e-08,\n 9.99942422e-01, 2.73261309e-11],\n [6.84069164e-05, 3.77383458e-09, 4.61269956e-09, 1.75928944e-05,\n 9.99913931e-01, 1.95838829e-13],\n [9.97742176e-01, 1.03157288e-06, 2.51532147e-05, 5.11952919e-07,\n 2.23120954e-03, 1.63324076e-11],\n [2.56056478e-06, 4.58555348e-07, 1.24389462e-05, 2.90007080e-07,\n 9.99984264e-01, 2.47912736e-12]]))]"
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "predictions"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-11-21T15:41:45.899260Z",
+ "start_time": "2023-11-21T15:41:45.855424Z"
+ }
+ },
+ "id": "fba5df5411979d52"
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Tokens contain additional predicted features."
+ ],
+ "metadata": {
+ "collapsed": false
+ },
+ "id": "f582c2ef63f87503"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "[Cześć, !]"
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "predictions[0].tokens"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-11-21T15:42:33.217710Z",
+ "start_time": "2023-11-21T15:42:33.165885Z"
+ }
+ },
+ "id": "4772691b96f8c7fa"
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Let's inspect one token:"
+ ],
+ "metadata": {
+ "collapsed": false
+ },
+ "id": "153a7236e2ff56a8"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "TOKEN LEMMA UPOS HEAD DEPREL \n",
+ "Cześć cześć VERB 0 root \n"
+ ]
+ }
+ ],
+ "source": [
+ "token = predictions[0].tokens[0]\n",
+ "\n",
+ "print(\"{:15} {:15} {:10} {:10} {:10}\".format('TOKEN', 'LEMMA', 'UPOS', 'HEAD', 'DEPREL'))\n",
+ "print(\"{:15} {:15} {:10} {:10} {:10}\".format(token.text, token.lemma, token.upostag, token.head, token.deprel))"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-11-21T15:43:24.164087Z",
+ "start_time": "2023-11-21T15:43:24.091530Z"
+ }
+ },
+ "id": "173da06d7fef299b"
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Let's print all the predicted tokens. For convenience, let's flatten the predictions first."
+ ],
+ "metadata": {
+ "collapsed": false
+ },
+ "id": "344445d6a2b8893e"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "[Cześć, !, To, jest, przykładowe, zdanie, .]"
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tokens = [p.tokens for p in predictions]\n",
+ "flat_tokens = []\n",
+ "for t in tokens:\n",
+ " flat_tokens.extend(t)\n",
+ "flat_tokens"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-11-21T15:46:40.333747Z",
+ "start_time": "2023-11-21T15:46:40.282880Z"
+ }
+ },
+ "id": "718614eb414b6b3a"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "TOKEN LEMMA UPOS HEAD DEPREL \n",
+ "Cześć cześć VERB 0 root \n",
+ "! ! PUNCT 1 punct \n",
+ "To to AUX 4 cop \n",
+ "jest być AUX 4 aux \n",
+ "przykładowe przykładowy ADJ 4 amod \n",
+ "zdanie zdanie NOUN 0 root \n",
+ ". . PUNCT 4 punct \n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"{:15} {:15} {:10} {:10} {:10}\".format('TOKEN', 'LEMMA', 'UPOS', 'HEAD', 'DEPREL'))\n",
+ "for t in flat_tokens:\n",
+ " print(\"{:15} {:15} {:10} {:10} {:10}\".format(t.text, t.lemma, t.upostag, t.head, t.deprel))"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-11-21T15:47:03.697133Z",
+ "start_time": "2023-11-21T15:47:03.670136Z"
+ }
+ },
+ "id": "7722d2623fa2faae"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [],
+ "metadata": {
+ "collapsed": false
+ },
+ "id": "c939410d13aac684"
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/model_training.ipynb b/notebooks/Training.ipynb
similarity index 100%
rename from notebooks/model_training.ipynb
rename to notebooks/Training.ipynb
--
GitLab