notebooks/torchhub.ipynb

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    "<a href=\"https://colab.research.google.com/github/joeynmt/joeynmt/blob/main/notebooks/torchhub.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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   "source": [
    "import logging\n",
    "# supress the logging\n",
    "logging.disable(logging.CRITICAL)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64cd1ecc-1e75-48e7-901f-0ea374e7f3b0",
   "metadata": {
    "id": "64cd1ecc-1e75-48e7-901f-0ea374e7f3b0"
   },
   "source": [
    "# Torch hub interface\n",
    "\n",
    "Author: May Ohta  \n",
    "Last update: 15. January 2024\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
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   "metadata": {
    "id": "dd74c53b-1919-4b23-8f06-c25549bbab12"
   },
   "source": [
    ":warning: Run this notebook on single GPU or CPU.\n",
    "\n",
    "Check if you have a compatible torch version."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dedf5c40-87bb-45d2-ae5a-e07cdee8581a",
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     "height": 35
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    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'2.1.0+cu121'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "torch.__version__"
   ]
  },
  {
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   },
   "source": [
    "Install Joeynmt v2.3:"
   ]
  },
  {
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      "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m106.3/106.3 kB\u001b[0m \u001b[31m961.4 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.3/92.3 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h  Building wheel for joeynmt (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "Name: joeynmt\n",
      "Version: 2.3.0\n",
      "Summary: Minimalist NMT for educational purposes\n",
      "Home-page: https://github.com/joeynmt/joeynmt\n",
      "Author: Jasmijn Bastings and Julia Kreutzer\n",
      "Author-email: \n",
      "License: Apache License\n",
      "Location: /usr/local/lib/python3.10/dist-packages\n",
      "Requires: datasets, flake8, future, matplotlib, numpy, packaging, pandas, pillow, plotly, protobuf, pylint, pytest, pyyaml, sacrebleu, sacremoses, seaborn, sentencepiece, setuptools, six, subword-nmt, tensorboard, torch, wrapt, yapf\n",
      "Required-by: \n"
     ]
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   ],
   "source": [
    "!pip install -q git+https://github.com/joeynmt/joeynmt.git\n",
    "!pip show joeynmt"
   ]
  },
  {
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   "execution_count": null,
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  },
  {
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   "id": "e41c2c12-5eac-43a9-8efc-004e1617144c",
   "metadata": {
    "id": "e41c2c12-5eac-43a9-8efc-004e1617144c"
   },
   "source": [
    "Load En-De / De-En Transformer models trained on WMT'14 data.\n",
    "\n",
    "Note: This takes a few minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7535c2a9-cbb3-4d70-8f05-c600d35aebf8",
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    "colab": {
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    },
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    "outputId": "d338853a-2392-456b-e6ab-10d446e61f4d",
    "tags": []
   },
   "outputs": [],
   "source": [
    "en2de = torch.hub.load('joeynmt/joeynmt', 'wmt14_ende')\n",
    "de2en = torch.hub.load('joeynmt/joeynmt', 'wmt14_deen')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aadaae34-8e64-461a-80ec-334deb301819",
   "metadata": {
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  },
  {
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   "metadata": {
    "id": "73edf0ad-55d7-4b41-81fa-9a62f1b7aaf8"
   },
   "source": [
    "## Translate\n",
    "\n",
    "Provide a list of src sentences in `translate()` func.  \n",
    "The return value is a (flat) list of string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b2d4f94c-87e5-4bf3-8207-390dd5c8644e",
   "metadata": {
    "colab": {
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    },
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Hallo Welt!']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# en->de model, single sentence\n",
    "en2de.translate([\"Hello world!\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9f504315-16cd-4af0-af4d-cd8af1df6199",
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    "colab": {
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    },
    "executionInfo": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Hello world!', 'What is going on?']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# de->en model, multiple sentences\n",
    "de2en.translate([\"Hallo Welt!\", \"Wie geht's?\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "967067a8-4922-4f2a-8f71-a1175738d45b",
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   "source": [
    "## N-best list\n",
    "\n",
    "Set `beam_size` and `n_best` options.  \n",
    "(has to be `beam_size` > `n_best`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "['What is going on?',\n",
       " 'How is it?',\n",
       " 'How is it going?',\n",
       " 'How is this going?',\n",
       " 'How can I do it?']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "de2en.translate([\"Wie geht's?\"], beam_size=5, n_best=5)"
   ]
  },
  {
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   "execution_count": 8,
   "id": "fb3dcf7b-0b70-4663-afcd-10c4dc100382",
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    "executionInfo": {
     "elapsed": 971,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Hallo Welt!',\n",
       " 'Hello Welt!',\n",
       " 'Hello world!',\n",
       " 'Helle Welt!',\n",
       " 'Hello World!',\n",
       " 'Wie sind Sie?',\n",
       " 'Wie sind Sie da?',\n",
       " 'Wie sind Sie denn?',\n",
       " 'Wie stehen Sie?',\n",
       " 'Wie sind Sie denn da?']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "en2de.translate([\"Hello world!\", \"How are you?\"], beam_size=5, n_best=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f3c9888-434f-4835-81a0-5844e8f5d39d",
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   },
   "source": [
    "## Scoring\n",
    "\n",
    "#### 1. Hypothesis scoring\n",
    "- call `score()` func without target.\n",
    "- if Greedy Decoding (`beam_size == 1`), token-level log-likelihood is returned.\n",
    "- if Beam Search (`beam_size > 1`), sequence-level log-likelihood is returned.\n",
    "\n",
    "#### 2. Reference scoring\n",
    "- call `score()` func with target.\n",
    "    \n",
    "The return value is a list of `PredictionOutput` objects, which have\n",
    "- translation: List[str]\n",
    "- tokens: List[List[str]]\n",
    "- token_probs: Optional[List[List[float]]]\n",
    "- sequence_probs: Optional[List[float]]\n",
    "- attention_probs: Optional[List[List[float]]]\n",
    "\n",
    "Each item holds a list of length = n_best."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1ff993c0-20d5-4276-8ae5-53216d3b7121",
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    "executionInfo": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## sent 1 ##\n",
      "-1.5346 ▁Hallo\n",
      "-0.1637 ▁Welt\n",
      "-0.1131 ▁\n",
      "-0.0694 !\n",
      "-0.1075 </s>\n",
      "\n",
      "## sent 2 ##\n",
      "-0.2651 ▁Wie\n",
      "-0.7192 ▁sind\n",
      "-0.1603 ▁Sie\n",
      "-0.6431 ▁\n",
      "-0.1069 ?\n",
      "-0.0967 </s>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "beam_size = 1 # Greedy Decoding -> token-level log-likelihood\n",
    "n_best = 1\n",
    "sentences = [\"Hello world!\", \"How are you?\"]\n",
    "greedy_out = en2de.score(sentences, beam_size=beam_size, n_best=n_best)\n",
    "\n",
    "for i, out in enumerate(greedy_out):\n",
    "    print(f'## sent {i+1} ##')\n",
    "    for n in range(n_best):\n",
    "        for score, token in zip(out.token_probs[n], out.tokens[n]):\n",
    "            print(f'{score:.4f} {token}')\n",
    "        print()"
   ]
  },
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    {
     "name": "stdout",
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     "text": [
      "## sent 1 ##\n",
      "1 best: -1.4635 Hallo Welt!\n",
      "2 best: -2.4815 Hello Welt!\n",
      "3 best: -2.9123 Hello world!\n",
      "\n",
      "## sent 2 ##\n",
      "1 best: -1.3846 Wie sind Sie?\n",
      "2 best: -2.2482 Wie sind Sie da?\n",
      "3 best: -2.6511 Wie sind Sie denn?\n",
      "\n"
     ]
    }
   ],
   "source": [
    "beam_size = 5 # Beam Search -> sequence-level log-likelihood\n",
    "n_best = 3\n",
    "sentences = ['Hello world!', \"How are you?\"]\n",
    "beam_out = en2de.score(sentences, beam_size=beam_size, n_best=n_best)\n",
    "\n",
    "for i, out in enumerate(beam_out):\n",
    "    print(f'## sent {i+1} ##')\n",
    "    for n in range(n_best):\n",
    "        print(f'{n+1} best: {out.sequence_probs[n]:.4f} {out.translation[n]}')\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dd63a991-3010-488e-9ec8-3c5dba66f64b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 236,
     "status": "ok",
     "timestamp": 1705762101217,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": -60
    },
    "id": "dd63a991-3010-488e-9ec8-3c5dba66f64b",
    "outputId": "c50779a0-2ee3-4685-d832-7acff4ab52e4",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## sent 1 ##\n",
      "-0.2305 ▁Ich\n",
      "-0.3924 ▁mag\n",
      "-6.8749 ▁Ke\n",
      "-1.1366 k\n",
      "-0.0226 se\n",
      "-0.1327 ▁\n",
      "-0.0989 .\n",
      "-0.1135 </s>\n",
      "\n",
      "## sent 2 ##\n",
      "-0.2305 ▁Ich\n",
      "-3.5479 ▁liebe\n",
      "-6.6423 ▁Ke\n",
      "-1.3268 k\n",
      "-0.0343 se\n",
      "-0.1388 ▁\n",
      "-0.1114 .\n",
      "-0.1103 </s>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Reference scoring -> always n_best = 1\n",
    "ref_scores = en2de.score(\n",
    "    src=['I like cookies.', 'I like cookies.'],\n",
    "    trg=['Ich mag Kekse.', 'Ich liebe Kekse.'],\n",
    ")\n",
    "\n",
    "n_best = 1\n",
    "for i, ref_score in enumerate(ref_scores):\n",
    "    print(f'## sent {i+1} ##')\n",
    "    for n in range(n_best):\n",
    "        for s, t in zip(ref_score.token_probs[n], ref_score.tokens[n]):\n",
    "            print(f'{s:.4f} {t}')\n",
    "        print()"
   ]
  },
  {
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   "id": "c89184aa-f935-4800-a1e8-e9b4d73c4384",
   "metadata": {
    "id": "c89184aa-f935-4800-a1e8-e9b4d73c4384"
   },
   "source": [
    "## Plot attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d8b80ec7-ee1b-4db7-ab49-1abc49b2f288",
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     "status": "ok",
     "timestamp": 1705762109670,
     "user": {
      "displayName": "",
      "userId": ""
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     "user_tz": -60
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    "outputId": "03984daf-1eab-4f4f-e67e-4517c291f383",
    "tags": []
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   "source": [
    "# get attention probs\n",
    "ref_scores = de2en.score(\n",
    "    src=['Hello world!'],\n",
    "    trg=['Hallo Welt!'],\n",
    ")\n",
    "\n",
    "# plot\n",
    "de2en.plot_attention(\n",
    "    src='Hello world!',\n",
    "    trg='Hallo Welt!',\n",
    "    attention_scores=ref_scores[0].attention_probs[0],\n",
    ")"
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  {
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   "execution_count": null,
   "id": "9e17340c-a72f-4681-9fee-c7898db5c90b",
   "metadata": {
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   "execution_count": null,
   "id": "9194820c-9dac-41fb-8307-ab187ac44fb8",
   "metadata": {
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    "## Prompting\n",
    "\n",
    "JoeyNMT v2.3 supports multilingual translation with language tags.  \n",
    "The following model is trained on iwslt14 en-de and en-fr sentence pairs with three language tags: `<de>`, `<en>` and `<fr>`."
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    },
    "executionInfo": {
     "elapsed": 69026,
     "status": "ok",
     "timestamp": 1705777343390,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": -60
    },
    "id": "6a3c998b-45e9-4330-acd5-b56c14eabf7c",
    "outputId": "03f17f2a-f78f-4e82-fca8-76504f8c2dac"
   },
   "outputs": [],
   "source": [
    "iwslt14 = torch.hub.load('joeynmt/joeynmt', 'iwslt14_prompt')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4bdec69-1851-47af-9a02-ac916c7480c3",
   "metadata": {
    "id": "e4bdec69-1851-47af-9a02-ac916c7480c3"
   },
   "source": [
    "#### 1. language tags (multi-task learning)\n",
    "\n",
    "In the src prompt, we tell the model from which language we are translating, and  \n",
    "in the trg prompt, we tell the model to which language we are translating."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6df23969-5c6f-49e2-9121-6655327c9033",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1095,
     "status": "ok",
     "timestamp": 1705777372788,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": -60
    },
    "id": "6df23969-5c6f-49e2-9121-6655327c9033",
    "outputId": "c60d2e53-ea97-4c9f-b099-8df8145cd0b8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Hello world!', 'Hello world.', 'Hi World!', 'Hi world!', 'Hello, world!']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# de -> en\n",
    "iwslt14.translate(\n",
    "    src=[\"Hallo Welt!\"],\n",
    "    src_prompt=[\"<de>\"],\n",
    "    trg_prompt=[\"<en>\"],\n",
    "    beam_size=5,\n",
    "    n_best=5,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8ff52cf7-2d2b-4376-8179-df9f183154a7",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1836,
     "status": "ok",
     "timestamp": 1705777387237,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": -60
    },
    "id": "8ff52cf7-2d2b-4376-8179-df9f183154a7",
    "outputId": "ff9b8ecc-e4dc-4369-f15a-93dc92ab00c0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Comment es-tu ?',\n",
       " 'Comment êtes-vous ?',\n",
       " 'Comment êtes-vous?',\n",
       " 'Comment allez-vous ?',\n",
       " 'Comment vous êtes-vous ?']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# en -> fr\n",
    "iwslt14.translate(\n",
    "    src=[\"How are you?\"],\n",
    "    src_prompt=[\"<en>\"],\n",
    "    trg_prompt=[\"<fr>\"],\n",
    "    beam_size=5,\n",
    "    n_best=5,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "14a98d19-e222-4fd8-a57d-f14142d40c6d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2698,
     "status": "ok",
     "timestamp": 1705777391118,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": -60
    },
    "id": "14a98d19-e222-4fd8-a57d-f14142d40c6d",
    "outputId": "b016164d-ccac-4fd6-c411-c19253c957a2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Wie geht es Ihnen?',\n",
       " \"Wie geht's dir?\",\n",
       " \"Wie geht's Ihnen?\",\n",
       " 'Wie sind Sie?',\n",
       " 'Wie geht es dir?']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# en -> de\n",
    "iwslt14.translate(\n",
    "    src=[\"How are you?\"],\n",
    "    src_prompt=[\"<en>\"],\n",
    "    trg_prompt=[\"<de>\"],\n",
    "    beam_size=5,\n",
    "    n_best=5,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7881a35a-98ba-47ee-bea4-6a07626c8b4c",
   "metadata": {
    "id": "7881a35a-98ba-47ee-bea4-6a07626c8b4c"
   },
   "source": [
    "#### 2. trg context\n",
    "\n",
    "In addition to the language tags, we can specify the context in the prompt.  \n",
    "The model translated with \"Sie\", in a formal way, for the example above.  \n",
    "We can tell the model that we'd like to translate it with \"Du\", in a more casual form."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6280a037-4a3a-4903-a304-c427bbce72ef",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1127,
     "status": "ok",
     "timestamp": 1705777392244,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": -60
    },
    "id": "6280a037-4a3a-4903-a304-c427bbce72ef",
    "outputId": "3c2dcb1c-31f5-4285-ef62-b3c335107abf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Wie bist du?']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iwslt14.translate(\n",
    "    src=[\"How are you?\"],\n",
    "    src_prompt=[\"<en>\"],\n",
    "    trg_prompt=[\"<de> du\"],\n",
    "    beam_size=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d269e5b-b54d-4f34-aa18-2647a99f2d10",
   "metadata": {
    "id": "8d269e5b-b54d-4f34-aa18-2647a99f2d10"
   },
   "source": [
    "#### 3. src context\n",
    "\n",
    "In the following example, the model translated the English pronoun \"it\" into the neutral form \"es\" in all top-5 candidates.  \n",
    "We can give src-side context, so that the model can get more hint about the gender of the pronoun."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a6c7724e-e910-4945-be04-2bd10014e329",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1954,
     "status": "ok",
     "timestamp": 1705777411372,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": -60
    },
    "id": "a6c7724e-e910-4945-be04-2bd10014e329",
    "outputId": "08373a5b-6997-417f-97bf-e8bf56cbb3ca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 best: -1.4792 | Ich werde es mitbringen.\n",
      "2 best: -1.6045 | Ich bringe es dazu.\n",
      "3 best: -1.6643 | Ich bringe es mit.\n",
      "4 best: -1.6840 | Ich bringe es an.\n",
      "5 best: -1.7278 | Ich bringe es.\n"
     ]
    }
   ],
   "source": [
    "# w/o src context\n",
    "out = iwslt14.score(\n",
    "    src=[\"I'll bring it.\"],\n",
    "    src_prompt=[\"<en>\"],\n",
    "    trg_prompt=[\"<de>\"],\n",
    "    beam_size=5,\n",
    "    n_best=5,\n",
    ")\n",
    "for n in range(5):\n",
    "    print(f'{n+1} best: {out[0].sequence_probs[n]:.4f} | {out[0].translation[n]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e6fa7f3f-0746-4fa0-bcfb-8abc71e38c58",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1685,
     "status": "ok",
     "timestamp": 1705777413056,
     "user": {
      "displayName": "",
      "userId": ""
     },
     "user_tz": -60
    },
    "id": "e6fa7f3f-0746-4fa0-bcfb-8abc71e38c58",
    "outputId": "9f8f44b7-a6eb-4854-8dff-d02d18d2fefa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 best: -1.6253 | Ich bringe es mit.\n",
      "2 best: -1.6836 | Ich bringe es an.\n",
      "3 best: -1.7318 | Ich werde es mitbringen.\n",
      "4 best: -1.7923 | Ich bringe es.\n",
      "5 best: -1.8206 | Ich bringe sie mit.\n"
     ]
    }
   ],
   "source": [
    "# w/ src context\n",
    "out = iwslt14.score(\n",
    "    src=[\"I'll bring it.\"],\n",
    "    src_prompt=[\"<en> There is a camera.\"],\n",
    "    trg_prompt=[\"<de>\"],\n",
    "    beam_size=5,\n",
    "    n_best=5,\n",
    ")\n",
    "for n in range(5):\n",
    "    print(f'{n+1} best: {out[0].sequence_probs[n]:.4f} | {out[0].translation[n]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95f42218-cf18-4cf5-a1ba-38f7b9d993c3",
   "metadata": {
    "id": "95f42218-cf18-4cf5-a1ba-38f7b9d993c3"
   },
   "source": [
    "The feminine form \"sie\" appears at the 5th position of the nbest list,  \n",
    "when we provide \"camera\" in the src prompt. (In German, \"Kamera\" is a feminine noun.)  \n",
    "By designing the prompt, we can gain more control on the trg translation!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09e08cbc-8a3b-47bb-a4d1-9026d5be7303",
   "metadata": {
    "id": "09e08cbc-8a3b-47bb-a4d1-9026d5be7303"
   },
   "source": [
    "### Training with prompt\n",
    "\n",
    "You can provide a tsv file containing all `src`, `src_prompt`, `trg`, and `trg_prompt`.\n",
    "See `test/data/toy/dev.tsv` for example. In this case, we used a preceeding sentence as a prompt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "98c7848d-26b2-4a16-9937-4cf0c2bf5091",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 258
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1705777558832,
     "user": {
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      "userId": ""
     },
     "user_tz": -60
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    "id": "98c7848d-26b2-4a16-9937-4cf0c2bf5091",
    "outputId": "4dd78f6b-6c1e-43bb-e075-3509992c9d12",
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "                                          src_prompt  \\\n",
       "0                                              <de>    \n",
       "1            <de> Ich freue mich , dass ich da bin .   \n",
       "2                              <de> Ja , guten Tag .   \n",
       "3        <de> Ja , also , was soll Biohacking sein ?   \n",
       "4  <de> Ich muss dazu erst mal ein bisschen ausho...   \n",
       "\n",
       "                                                 src  \\\n",
       "0                 Ich freue mich , dass ich da bin .   \n",
       "1                                   Ja , guten Tag .   \n",
       "2             Ja , also , was soll Biohacking sein ?   \n",
       "3  Ich muss dazu erst mal ein bisschen ausholen ,...   \n",
       "4  Ich studiere Molekularbiologie und beschäftige...   \n",
       "\n",
       "                                          trg_prompt  \\\n",
       "0                                               <en>   \n",
       "1                        <en> I’m happy to be here .   \n",
       "2                                 <en> Yes , hello .   \n",
       "3               <en> Yes , so , what is biohacking ?   \n",
       "4  <en> I’ll have to provide some background info...   \n",
       "\n",
       "                                                 trg  \n",
       "0                             I’m happy to be here .  \n",
       "1                                      Yes , hello .  \n",
       "2                    Yes , so , what is biohacking ?  \n",
       "3  I’ll have to provide some background informati...  \n",
       "4  I study molecular biology and have been doing ...  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "\n",
    "hub_dir = Path(torch.hub.get_dir()) / \"joeynmt_joeynmt_main\"\n",
    "df = pd.read_csv(hub_dir / \"test/data/toy/dev.tsv\", sep=\"\\t\")\n",
    "df[[\"src_prompt\", \"src\", \"trg_prompt\", \"trg\"]].head(5)"
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    "You also should pay attention to the `lang` section in the config.  \n",
    "(We have multiple languages in both sides!)\n",
    "\n",
    "```yaml\n",
    "# config.yaml\n",
    "...\n",
    "data:\n",
    "    train: \"test/data/iwslt14/train\"\n",
    "    dev: \"test/data/iwslt14/validation\"\n",
    "    test: \"test/data/iwslt14/test\"\n",
    "    dataset_type: \"tsv\"\n",
    "    src:\n",
    "        lang: \"src\" # <- instead of \"en\", \"de\", etc.\n",
    "        ...\n",
    "        \n",
    "    trg:\n",
    "        lang: \"trg\" # <- instead of \"en\", \"de\", etc.\n",
    "        ...\n",
    "        \n",
    "    special_symbols:\n",
    "        unk_token: \"<unk>\"\n",
    "        pad_token: \"<pad>\"\n",
    "        bos_token: \"<s>\"\n",
    "        eos_token: \"</s>\"\n",
    "        sep_token: \"<sep>\" # <- sepatator token\n",
    "        unk_id: 0\n",
    "        pad_id: 1\n",
    "        bos_id: 2\n",
    "        eos_id: 3\n",
    "        sep_id: 4 # <- separator token index\n",
    "        lang_tags: [\"<de>\", \"<en>\"] # <- language tags\n",
    "    ...\n",
    "```"
   ]
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