StanfordNLP first impression

StanfordNLP.ipynb

{
  "cells": [
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import stanfordnlp",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "stanfordnlp.download('en')",
      "execution_count": 2,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Using the default treebank \"en_ewt\" for language \"en\".\nWould you like to download the models for: en_ewt now? (Y/n)\nY\n\nDefault download directory: /home/daniel.cox/stanfordnlp_resources\nHit enter to continue or type an alternate directory.\n\n\nDownloading models for: en_ewt\nDownload location: /home/daniel.cox/stanfordnlp_resources/en_ewt_models.zip\n"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": "100%|██████████| 235M/235M [00:27<00:00, 8.45MB/s] \n"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "\nDownload complete.  Models saved to: /home/daniel.cox/stanfordnlp_resources/en_ewt_models.zip\nExtracting models file for: en_ewt\nCleaning up...Done.\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "nlp = stanfordnlp.Pipeline()",
      "execution_count": 4,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Use device: gpu\n---\nLoading: tokenize\nWith settings: \n{'model_path': '/home/daniel.cox/stanfordnlp_resources/en_ewt_models/en_ewt_tokenizer.pt', 'lang': 'en', 'shorthand': 'en_ewt', 'mode': 'predict'}\n---\nLoading: pos\nWith settings: \n{'model_path': '/home/daniel.cox/stanfordnlp_resources/en_ewt_models/en_ewt_tagger.pt', 'pretrain_path': '/home/daniel.cox/stanfordnlp_resources/en_ewt_models/en_ewt.pretrain.pt', 'lang': 'en', 'shorthand': 'en_ewt', 'mode': 'predict'}\n---\nLoading: lemma\nWith settings: \n{'model_path': '/home/daniel.cox/stanfordnlp_resources/en_ewt_models/en_ewt_lemmatizer.pt', 'lang': 'en', 'shorthand': 'en_ewt', 'mode': 'predict'}\nBuilding an attentional Seq2Seq model...\nUsing a Bi-LSTM encoder\nUsing soft attention for LSTM.\nFinetune all embeddings.\n[Running seq2seq lemmatizer with edit classifier]\n---\nLoading: depparse\nWith settings: \n{'model_path': '/home/daniel.cox/stanfordnlp_resources/en_ewt_models/en_ewt_parser.pt', 'pretrain_path': '/home/daniel.cox/stanfordnlp_resources/en_ewt_models/en_ewt.pretrain.pt', 'lang': 'en', 'shorthand': 'en_ewt', 'mode': 'predict'}\nDone loading processors!\n---\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "doc = nlp(\"Barack Obama was born in Hawaii.  He was elected president in 2008.\")",
      "execution_count": 5,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": "/pytorch/aten/src/ATen/native/cuda/LegacyDefinitions.cpp:19: UserWarning: masked_fill_ received a mask with dtype torch.uint8, this behavior is now deprecated,please use a mask with dtype torch.bool instead.\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "doc.sentences[0].print_dependencies()",
      "execution_count": 6,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "('Barack', '4', 'nsubj:pass')\n('Obama', '1', 'flat')\n('was', '4', 'aux:pass')\n('born', '0', 'root')\n('in', '6', 'case')\n('Hawaii', '4', 'obl')\n('.', '4', 'punct')\n"
        }
      ]
    }
  ],
  "metadata": {
    "gist": {
      "id": "",
      "data": {
        "description": "StanfordNLP first impression",
        "public": true
      }
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.7.3",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}