{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f5e0b745",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import T5Tokenizer, T5ForConditionalGeneration\n",
    "from transformers import AutoModel, AutoTokenizer\n",
    "from transformers import MarianMTModel\n",
    "from transformers import XLMRobertaForSequenceClassification\n",
    "\n",
    "\n",
    "import os\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "\n",
    "tqdm.pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6e228528",
   "metadata": {},
   "outputs": [],
   "source": [
    "def detect_language(model, tokenizer, text):\n",
    "    \n",
    "    token_dict = tokenizer(text, return_tensors=\"pt\").to(\"cuda\")\n",
    "    outputs = model(token_dict.input_ids)\n",
    "    decoded = outputs.logits.argmax(-1).item()\n",
    "    lang = model.config.id2label[decoded]\n",
    "    return lang\n",
    "\n",
    "def translate(model, tokenizer, text):\n",
    "    input_ids = tokenizer(text, return_tensors=\"pt\").input_ids.to(\"cuda\")\n",
    "    outputs = model.generate(input_ids)\n",
    "    decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
    "    return decoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "454ae3b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDEX</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maybe the UN could talk to those asian and afr...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>‘Juice (Jews) are worse that nukes.’ Caption t...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Foreigners on UK benefits leaps 41% in 5 years...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jews cannot be patriots, since their allegianc...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Israel, Zionism and Jewish Supremacist control...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>Woman are basically childlike.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>women should be submissive to men.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5000</th>\n",
       "      <td>Women are essentially childlike, unable to und...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5001</th>\n",
       "      <td>Women should be submissive to men.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5002</th>\n",
       "      <td>Women should not be allowed to vote, this is a...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5003 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    text  label\n",
       "INDEX                                                          \n",
       "0      Maybe the UN could talk to those asian and afr...      1\n",
       "1      ‘Juice (Jews) are worse that nukes.’ Caption t...      1\n",
       "2      Foreigners on UK benefits leaps 41% in 5 years...      1\n",
       "3      Jews cannot be patriots, since their allegianc...      1\n",
       "4      Israel, Zionism and Jewish Supremacist control...      1\n",
       "...                                                  ...    ...\n",
       "4998                      Woman are basically childlike.      1\n",
       "4999                  women should be submissive to men.      1\n",
       "5000   Women are essentially childlike, unable to und...      1\n",
       "5001                  Women should be submissive to men.      1\n",
       "5002   Women should not be allowed to vote, this is a...      1\n",
       "\n",
       "[5003 rows x 2 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DATASET_PATH = \"../data/processed/\"\n",
    "\n",
    "df = pd.read_csv(DATASET_PATH, index_col=[0])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f2db3a80",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         2.0\n",
       "1         2.0\n",
       "2         4.0\n",
       "3         2.0\n",
       "4         4.0\n",
       "         ... \n",
       "135551    2.0\n",
       "135552    2.0\n",
       "135553    1.0\n",
       "135554    2.0\n",
       "135555    2.0\n",
       "Name: status, Length: 135556, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# PREPROCESSING\n",
    "# Need more preprocessing :\n",
    "#  - stopwords : Name, URL\n",
    "\n",
    "# Worst 10%\n",
    "# df = df.loc[df.hate_speech_score > df.hate_speech_score.quantile(0.90)]\n",
    "\n",
    "# Get negative examples\n",
    "# n = 12785 # = number of gathered worst examples\n",
    "# df = df.loc[df.hate_speech_score < df.hate_speech_score.quantile(0.1)].iloc[:n]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ca78b94",
   "metadata": {},
   "source": [
    "#### Language detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "69fc7f94",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Language detection model\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"papluca/xlm-roberta-base-language-detection\")\n",
    "model = XLMRobertaForSequenceClassification.from_pretrained(\"papluca/xlm-roberta-base-language-detection\").to(\"cuda\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "f05bbff2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████| 135556/135556 [18:54<00:00, 119.45it/s]\n"
     ]
    }
   ],
   "source": [
    "# Add language prediction for later filtering before applying language-dependent translation model\n",
    "df[\"lang\"] = df.text.progress_apply(lambda text: detect_language(model, tokenizer, text))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05d07356",
   "metadata": {},
   "source": [
    "#### Text translation\n",
    "\n",
    "- Process requires long processing time\n",
    "- Process should be run multiple times to complete"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a82a5eb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "zsh:1: no matches found: temp/*\r\n"
     ]
    }
   ],
   "source": [
    "! rm -rf temp/*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3f3f1bfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def write_file(path, text):\n",
    "    with open(path, \"w\") as f:\n",
    "        f.write(text)\n",
    "\n",
    "LANGUAGE = \"en\"\n",
    "OUTPUT_DIR = \"../data/translated\"\n",
    "TEMP_DIR = \"../data/temp\"\n",
    "IN_MEMORY = True\n",
    "\n",
    "output_dir = os.path.join(OUTPUT_DIR, LANGUAGE)\n",
    "if not os.path.exists(OUTPUT_DIR):\n",
    "    os.makedirs(output_dir)\n",
    "#     os.mkdir(OUTPUT_DIR)\n",
    "if not os.path.exists(TEMP_DIR):\n",
    "    os.mkdir(TEMP_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b8122256",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Translation model\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"Abelll/marian-finetuned-kde4-en-to-fr\")\n",
    "model = MarianMTModel.from_pretrained(\"Abelll/marian-finetuned-kde4-en-to-fr\").to(\"cuda\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b9ef106d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                                                              | 0/5003 [00:00<?, ?it/s]/home/louis/miniconda3/envs/sexism_detection/lib/python3.9/site-packages/transformers/generation/utils.py:1387: UserWarning: Neither `max_length` nor `max_new_tokens` has been set, `max_length` will default to 512 (`self.config.max_length`). Controlling `max_length` via the config is deprecated and `max_length` will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n",
      "  warnings.warn(\n",
      "100%|███████████████████████████████████████████████████████████████████████████████████| 5003/5003 [10:27<00:00,  7.97it/s]\n"
     ]
    }
   ],
   "source": [
    "translated_texts = []\n",
    "for i, row in tqdm(df.iterrows(), total=df.shape[0]):\n",
    "    output_path = os.path.join(TEMP_DIR, str(i) + \".txt\")\n",
    "    if not IN_MEMORY:\n",
    "        if os.path.exists(output_path):\n",
    "            print(\"Already translated !\")\n",
    "            continue\n",
    "    translated = translate(model, tokenizer, row.text)\n",
    "    if IN_MEMORY:\n",
    "        translated_texts.append((i, translated))\n",
    "    else:\n",
    "        write_file(output_path, translated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0ecc3000",
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "\n",
    "output_filename = DATASET_PATH\n",
    "\n",
    "if not IN_MEMORY:\n",
    "    filelist = glob.glob(TEMP_DIR + \"/*\")\n",
    "    print(f\"Found {len(filelist)}\")\n",
    "    filelist = sorted(filelist, key=lambda x: int(x.split(\"/\")[-1].split(\".\")[0]))\n",
    "    translated_texts = []\n",
    "    for text_file in tqdm(filelist):\n",
    "        with open(text_file) as f:\n",
    "            translated_text = f.read().splitlines()\n",
    "        translated_texts.append(translated_text)\n",
    "    translated_texts = pd.DataFrame(translated_texts)\n",
    "    df[\"translated\"] = translated_texts\n",
    "\n",
    "df[\"translated\"] = translated_texts\n",
    "df.translated = df.translated.apply(lambda x: x[1])\n",
    "df.dropna().to_csv(output_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "88c69fd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "      <th>translated</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDEX</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maybe the UN could talk to those asian and afr...</td>\n",
       "      <td>1</td>\n",
       "      <td>Peut-être que l'Onu pourrait parler aux nation...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>‘Juice (Jews) are worse that nukes.’ Caption t...</td>\n",
       "      <td>1</td>\n",
       "      <td>« Juice (Juifs) sont pire que les nuées. « &amp; #...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Foreigners on UK benefits leaps 41% in 5 years...</td>\n",
       "      <td>1</td>\n",
       "      <td>Les étrangers au Royaume-Uni bénéficient d'un ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jews cannot be patriots, since their allegianc...</td>\n",
       "      <td>1</td>\n",
       "      <td>Les Juifs ne peuvent pas être patriotes, car l...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Israel, Zionism and Jewish Supremacist control...</td>\n",
       "      <td>1</td>\n",
       "      <td>Israël, le sionisme et le contrôle suprémacist...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>Woman are basically childlike.</td>\n",
       "      <td>1</td>\n",
       "      <td>La femme ressemble à un enfant.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>women should be submissive to men.</td>\n",
       "      <td>1</td>\n",
       "      <td>Les filles doivent être soumises aux hommes.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5000</th>\n",
       "      <td>Women are essentially childlike, unable to und...</td>\n",
       "      <td>1</td>\n",
       "      <td>Les filles sont pour l'essentiel des filles, i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5001</th>\n",
       "      <td>Women should be submissive to men.</td>\n",
       "      <td>1</td>\n",
       "      <td>Les filles doivent être soumises aux hommes.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5002</th>\n",
       "      <td>Women should not be allowed to vote, this is a...</td>\n",
       "      <td>1</td>\n",
       "      <td>Les femmes ne devraient pas être autorisées à ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5003 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    text  label  \\\n",
       "INDEX                                                             \n",
       "0      Maybe the UN could talk to those asian and afr...      1   \n",
       "1      ‘Juice (Jews) are worse that nukes.’ Caption t...      1   \n",
       "2      Foreigners on UK benefits leaps 41% in 5 years...      1   \n",
       "3      Jews cannot be patriots, since their allegianc...      1   \n",
       "4      Israel, Zionism and Jewish Supremacist control...      1   \n",
       "...                                                  ...    ...   \n",
       "4998                      Woman are basically childlike.      1   \n",
       "4999                  women should be submissive to men.      1   \n",
       "5000   Women are essentially childlike, unable to und...      1   \n",
       "5001                  Women should be submissive to men.      1   \n",
       "5002   Women should not be allowed to vote, this is a...      1   \n",
       "\n",
       "                                              translated  \n",
       "INDEX                                                     \n",
       "0      Peut-être que l'Onu pourrait parler aux nation...  \n",
       "1      « Juice (Juifs) sont pire que les nuées. « & #...  \n",
       "2      Les étrangers au Royaume-Uni bénéficient d'un ...  \n",
       "3      Les Juifs ne peuvent pas être patriotes, car l...  \n",
       "4      Israël, le sionisme et le contrôle suprémacist...  \n",
       "...                                                  ...  \n",
       "4998                     La femme ressemble à un enfant.  \n",
       "4999        Les filles doivent être soumises aux hommes.  \n",
       "5000   Les filles sont pour l'essentiel des filles, i...  \n",
       "5001        Les filles doivent être soumises aux hommes.  \n",
       "5002   Les femmes ne devraient pas être autorisées à ...  \n",
       "\n",
       "[5003 rows x 3 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "885ebb18",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"../data/ucberkeley-dlab_measuring-hate-speech_translated_positive.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "576a76f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "translate English to French: for starters bend over the one in pink and kick that ass and pussy to get a taste until she's begging for a dick inside her.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"j'ai lu cette vidéo et j'ai\""
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def translate(text):\n",
    "    input_text = f\"translate {BASE_LANGUAGE} to French: \" + text.lower()\n",
    "    print(input_text)\n",
    "    input_ids = tokenizer(input_text, return_tensors=\"pt\").input_ids\n",
    "    outputs = model.generate(input_ids)\n",
    "    decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
    "    return decoded\n",
    "\n",
    "translate(df.text[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1bbc6dab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MarianMTModel(\n",
       "  (model): MarianModel(\n",
       "    (shared): Embedding(59514, 512, padding_idx=59513)\n",
       "    (encoder): MarianEncoder(\n",
       "      (embed_tokens): Embedding(59514, 512, padding_idx=59513)\n",
       "      (embed_positions): MarianSinusoidalPositionalEmbedding(512, 512)\n",
       "      (layers): ModuleList(\n",
       "        (0): MarianEncoderLayer(\n",
       "          (self_attn): MarianAttention(\n",
       "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "          )\n",
       "          (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "          (activation_fn): SiLUActivation()\n",
       "          (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
       "          (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
       "          (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (1): MarianEncoderLayer(\n",
       "          (self_attn): MarianAttention(\n",
       "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "          )\n",
       "          (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "          (activation_fn): SiLUActivation()\n",
       "          (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
       "          (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
       "          (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (2): MarianEncoderLayer(\n",
       "          (self_attn): MarianAttention(\n",
       "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "          )\n",
       "          (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "          (activation_fn): SiLUActivation()\n",
       "          (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
       "          (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
       "          (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (3): MarianEncoderLayer(\n",
       "          (self_attn): MarianAttention(\n",
       "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "          )\n",
       "          (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "          (activation_fn): SiLUActivation()\n",
       "          (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
       "          (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
       "          (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (4): MarianEncoderLayer(\n",
       "          (self_attn): MarianAttention(\n",
       "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "          )\n",
       "          (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "          (activation_fn): SiLUActivation()\n",
       "          (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
       "          (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
       "          (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (5): MarianEncoderLayer(\n",
       "          (self_attn): MarianAttention(\n",
       "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "          )\n",
       "          (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "          (activation_fn): SiLUActivation()\n",
       "          (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
       "          (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
       "          (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (decoder): MarianDecoder(\n",
       "      (embed_tokens): Embedding(59514, 512, padding_idx=59513)\n",
       "      (embed_positions): MarianSinusoidalPositionalEmbedding(512, 512)\n",
       "      (layers): ModuleList(\n",
       "        (0): MarianDecoderLayer(\n",
       "          (self_attn): MarianAttention(\n",
       "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "          )\n",
       "          (activation_fn): SiLUActivation()\n",
       "          (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "          (encoder_attn): MarianAttention(\n",
       "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "          )\n",
       "          (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "          (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
       "          (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
       "          (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (1): MarianDecoderLayer(\n",
       "          (self_attn): MarianAttention(\n",
       "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "          )\n",
       "          (activation_fn): SiLUActivation()\n",
       "          (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "          (encoder_attn): MarianAttention(\n",
       "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "          )\n",
       "          (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "          (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
       "          (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
       "          (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (2): MarianDecoderLayer(\n",
       "          (self_attn): MarianAttention(\n",
       "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "          )\n",
       "          (activation_fn): SiLUActivation()\n",
       "          (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "          (encoder_attn): MarianAttention(\n",
       "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "          )\n",
       "          (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "          (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
       "          (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
       "          (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (3): MarianDecoderLayer(\n",
       "          (self_attn): MarianAttention(\n",
       "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "          )\n",
       "          (activation_fn): SiLUActivation()\n",
       "          (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "          (encoder_attn): MarianAttention(\n",
       "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "          )\n",
       "          (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "          (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
       "          (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
       "          (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (4): MarianDecoderLayer(\n",
       "          (self_attn): MarianAttention(\n",
       "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "          )\n",
       "          (activation_fn): SiLUActivation()\n",
       "          (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "          (encoder_attn): MarianAttention(\n",
       "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "          )\n",
       "          (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "          (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
       "          (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
       "          (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (5): MarianDecoderLayer(\n",
       "          (self_attn): MarianAttention(\n",
       "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "          )\n",
       "          (activation_fn): SiLUActivation()\n",
       "          (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "          (encoder_attn): MarianAttention(\n",
       "            (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "            (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "          )\n",
       "          (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "          (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
       "          (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
       "          (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (lm_head): Linear(in_features=512, out_features=59514, bias=False)\n",
       ")"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  }
 ],
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