Supervised NMT with Helsinki-NLP.ipynb

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Helsinki-NLP/opus-mt-en-fr on a translation task with a parallel corpus from Europarl, tested on a parallel corpus from ECHR."],"metadata":{"id":"yW6bKw76uyIk"}},{"cell_type":"markdown","source":["We start with the preprocessing and preparation of the datasets."],"metadata":{"id":"Y7luvTTAu2vn"}},{"cell_type":"markdown","source":["## Prepare the data"],"metadata":{"id":"UnX3OUt3u6Z6"}},{"cell_type":"code","execution_count":1,"metadata":{"id":"jYWk1hk9ur4d","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1683726885160,"user_tz":-120,"elapsed":30445,"user":{"displayName":"María Sierro","userId":"02188206244184953197"}},"outputId":"f30a97bc-cfc6-4109-8085-043de58d301d"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"markdown","source":["## Pre-process the dataset"],"metadata":{"id":"REr40cIXwNdr"}},{"cell_type":"markdown","source":["Tokenizing the dataset."],"metadata":{"id":"Hx6azZV7wRRa"}},{"cell_type":"code","source":["! pip install datasets transformers sacrebleu torch sentencepiece transformers[sentencepiece]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"Cny-f4JhKwsh","outputId":"d97b4bee-f85e-46ff-bd5b-4fa5b905fd35","executionInfo":{"status":"ok","timestamp":1683726908096,"user_tz":-120,"elapsed":20360,"user":{"displayName":"María Sierro","userId":"02188206244184953197"}}},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting datasets\n","  Downloading datasets-2.12.0-py3-none-any.whl (474 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m474.6/474.6 kB\u001b[0m \u001b[31m9.8 MB/s\u001b[0m eta 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all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","tensorflow 2.12.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 3.20.2 which is incompatible.\n","tensorflow-metadata 1.13.1 requires protobuf<5,>=3.20.3, but you have protobuf 3.20.2 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 colorama-0.4.6 datasets-2.12.0 dill-0.3.6 frozenlist-1.3.3 huggingface-hub-0.14.1 multidict-6.0.4 multiprocess-0.70.14 portalocker-2.7.0 protobuf-3.20.2 responses-0.18.0 sacrebleu-2.3.1 sentencepiece-0.1.99 tokenizers-0.13.3 transformers-4.28.1 xxhash-3.2.0 yarl-1.9.2\n"]},{"output_type":"display_data","data":{"application/vnd.colab-display-data+json":{"pip_warning":{"packages":["google"]}}},"metadata":{}}]},{"cell_type":"code","source":["import os\n","os.environ[\"WANDB_DISABLED\"]=\"true\""],"metadata":{"id":"e2xo3DhiLMjH","executionInfo":{"status":"ok","timestamp":1683726925609,"user_tz":-120,"elapsed":265,"user":{"displayName":"María Sierro","userId":"02188206244184953197"}}},"execution_count":3,"outputs":[]},{"cell_type":"code","source":["import transformers\n","print(transformers.__version__) #Ensure that the version is greater than 4.11.1"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uOMe3Ap4MFH6","outputId":"3b4728e5-1372-469c-819d-5d87d78ba12b","executionInfo":{"status":"ok","timestamp":1683726928503,"user_tz":-120,"elapsed":747,"user":{"displayName":"María Sierro","userId":"02188206244184953197"}}},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["4.28.1\n"]}]},{"cell_type":"code","source":["#Call the model\n","model_checkpoint = \"Helsinki-NLP/opus-mt-en-fr\""],"metadata":{"id":"-6Ai2-HJMtNP","executionInfo":{"status":"ok","timestamp":1683726930071,"user_tz":-120,"elapsed":222,"user":{"displayName":"María Sierro","userId":"02188206244184953197"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["#Prepare the tokenizer\n","from transformers import AutoTokenizer\n","    \n","tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"],"metadata":{"id":"4fKYo9L_M0f_","executionInfo":{"status":"ok","timestamp":1683726935044,"user_tz":-120,"elapsed":1639,"user":{"displayName":"María Sierro","userId":"02188206244184953197"}},"colab":{"base_uri":"https://localhost:8080/","height":212,"referenced_widgets":["f07b1b4670ae4b7ca65264d603c74002","d934739220bf48e5a13af1ff2758a0cf","c47853866ed44e48b112de70bd2be5d0","1696816bf55040b898b299e5777f1d4a","eaaf747d00b5419baf2d3e25278e02ee","32d30c10372245238d3f9f9d800f1fd9","565dfd366dd14bc6a629bd69569ae8a8","e388dd562bc2454497fe18db4ea4ce2b","60d1a47471f74516b6c4ecfdfb3bbc03","af1dc8c540a04540bd76badbbe0df652","ffd7b2c8a4554ac38a8979b970036fa4","48c84ecb1c2744148be07fbaeea92779","517a0f1b6d224f778246d929690bc8c0","9dddda578a49429985f61230df13d72e","f890abdf09f54bb0bf362c901831d25a","750be9621d64486dbbb3131be0cb09bb","b508abbc7d3e43b98c7e3eec935dbd1f","651a0ba978374e8493e8f5a522e82755","db7b82fa57444bfab901bf99570a12bc","fc03e514a95048eaa92cc7527a9acc53","7445180776484c94b2c25e3687be028d","1ba0adf19314400ca8ca62728ab001d9","32b787da1bc24307b4ef15d93ad838bf","92620744e64144c995fc97aaac5d83b4","93aa61fdbfb64eb68f5c8cdc61e3cb96","27ca0effd91449cd935f31ff84c47d13","be3006cbd6c24bf699fd0806f57be4ca","c1451dcfa3d44a05b5f9ae2b7d3833c8","0be7f45c7540419fbc784ff2ae8a48ab","f0f19da2586b4680bdd5ab95ae11289c","aad4faad78724eb0b35a49a898bbf6c3","2df0710a5ea94e5b96c93d3a843b9aff","65987eeee5a246b7a210d0072bf65dc6","2c36a46165994194880793f9e2470867","6be371ff4c9d4e3980b54fb782082946","453b0b05611a4d5680e79f27c262ea73","12e3ddc251f6495c92ba5ca5ee76a7c2","a9bb803c257f4052a6f14659feda38cc","d5e8e2d0c5dd4871956c4a5db5b2241e","d9b0bd00aebd4414ae43ecb8b6f5d00c","4c82bc519db34151967abc1c32b81e4d","5cc45d2191294a5ebd73d22011401cec","22f09b5a65004f04b020bcd09cdf89bb","9fbc2057482542bb9dd9e95a28e48ccd","d266c330b5f8461883291d363c5f64eb","503f84bc762740389f43cde956c17328","2e08aaf4950e49db9ef8e8cf46014f2d","604c43f2e28d4146bc01ab06a5e5b6cf","4c20ecc085c843c2859f7cd7bc968527","2ac19fd8295a4c32b833590760d9e118","44d2211ca3ee407c8f02c0fb167cb632","5fe9dce170db42f49cb9570bee268a5f","302f600e9e6b4333aa9cafa7bcb0dcd3","f58176aadb854941b4278eb12909eb5b","f2129d3e18e1465a8eac73a876e7e6f6"]},"outputId":"ca97d574-2237-41b3-9dbd-617a9396e4ee"},"execution_count":6,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading 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?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2c36a46165994194880793f9e2470867"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)olve/main/vocab.json:   0%|          | 0.00/1.34M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d266c330b5f8461883291d363c5f64eb"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/models/marian/tokenization_marian.py:194: UserWarning: Recommended: pip install sacremoses.\n","  warnings.warn(\"Recommended: pip install sacremoses.\")\n"]}]},{"cell_type":"code","source":["#We define our variables so that we don't have to keep calling the path\n","# USING THE SPLITTED FILES THAT WE OBTAINED FROM THE NOTEBOOK 'SUPERVISED-NMT' (PARALLEL CORPUS FROM EUROPARL)\n","train_dataset_en = \"/content/drive/MyDrive/HAPLAPMaster/MachineTranslation/MT-project/en-fr-baseline/train.en\"\n","train_dataset_fr = \"/content/drive/MyDrive/HAPLAPMaster/MachineTranslation/MT-project/en-fr-baseline/train.fr\"\n","dev_dataset_en = '/content/drive/MyDrive/HAPLAPMaster/MachineTranslation/MT-project/en-fr-baseline/dev.en'\n","dev_dataset_fr = '/content/drive/MyDrive/HAPLAPMaster/MachineTranslation/MT-project/en-fr-baseline/dev.fr'\n","\n","with open(train_dataset_en, \"r\", encoding=\"utf-8\") as f:\n","    train_en = f.readlines()\n","\n","with open(train_dataset_fr, \"r\", encoding=\"utf-8\") as f:\n","    train_fr = f.readlines()\n","\n","with open(dev_dataset_en, \"r\", encoding=\"utf-8\") as f:\n","    dev_en = f.readlines()\n","\n","with open(dev_dataset_fr, \"r\", encoding=\"utf-8\") as f:\n","    dev_fr = f.readlines()"],"metadata":{"id":"aRvbk6NYNjNd","executionInfo":{"status":"ok","timestamp":1683726946363,"user_tz":-120,"elapsed":8329,"user":{"displayName":"María Sierro","userId":"02188206244184953197"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["#We define a function for preprocessing\n","prefix = \"\"\n","max_input_length = 128\n","max_target_length = 128\n","source_lang = \"en\"\n","target_lang = \"fr\"\n","\n","def preprocess_function(examples):\n","    source_examples = [prefix + ex for ex in examples[\"source\"]]\n","    target_examples = [ex for ex in examples[\"target\"]]\n","\n","    source_inputs = tokenizer(source_examples, max_length=max_input_length, truncation=True, padding=\"max_length\")\n","    target_inputs = tokenizer(target_examples, max_length=max_target_length, truncation=True, padding=\"max_length\")\n","\n","    model_inputs = {\n","        \"input_ids\": source_inputs[\"input_ids\"],\n","        \"attention_mask\": source_inputs[\"attention_mask\"],\n","        \"labels\": target_inputs[\"input_ids\"],\n","    }\n","\n","    return model_inputs"],"metadata":{"id":"gly1gpeVM19b","executionInfo":{"status":"ok","timestamp":1683726948543,"user_tz":-120,"elapsed":218,"user":{"displayName":"María Sierro","userId":"02188206244184953197"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["from datasets import Dataset\n","# Crear un diccionario con las claves \"source\" y \"target\" para pasar a la función preprocess_function\n","data_dict = {\n","    \"source\": train_en,\n","    \"target\": train_fr,\n","}\n","\n","# Convertir el diccionario en un objeto Dataset\n","train = Dataset.from_dict(data_dict)\n","\n","tokenized_train = train.map(preprocess_function, batched=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":17,"referenced_widgets":["426e27b8a93f48bb88fb99df4a0dcba2","ca02e83e31724193ab6a82dcf78a98b0","438cfd6876974661a70b0529aee76209","8f3c09f4fe7e4f288282a82746eec597","6adb39f920a84c319d8efe1fd81c2963","b97758e719da4595bbd78e72c468f909","ac938008a48e48338864bb43b5c4435a","5e46f0ff2470495d8cb96b65b38f404f","9f47de88486b4841ae17638251df1ab1","6e3a21082a414e0188d79e99e7376c75","b5186f8282c24baead31ddec27e55f17"]},"id":"uq6_bzLxOzE3","outputId":"3dbdbbbd-aca0-4d81-f195-c707805a6cc2","executionInfo":{"status":"ok","timestamp":1683727616692,"user_tz":-120,"elapsed":664817,"user":{"displayName":"María Sierro","userId":"02188206244184953197"}}},"execution_count":9,"outputs":[{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/1884273 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"426e27b8a93f48bb88fb99df4a0dcba2"}},"metadata":{}}]},{"cell_type":"code","source":["tokenized_train"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H09EotVHPeR-","outputId":"aeff0395-e66b-47aa-fc56-2c2caf04bbb6","executionInfo":{"status":"ok","timestamp":1683727692272,"user_tz":-120,"elapsed":221,"user":{"displayName":"María Sierro","userId":"02188206244184953197"}}},"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Dataset({\n","    features: ['source', 'target', 'input_ids', 'attention_mask', 'labels'],\n","    num_rows: 1884273\n","})"]},"metadata":{},"execution_count":11}]},{"cell_type":"code","source":["tokenized_train['source'][27]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"0nZVF4-HPgAZ","outputId":"0e33e63d-4fbc-4ab0-d352-2e1debec272d","executionInfo":{"status":"ok","timestamp":1683727697674,"user_tz":-120,"elapsed":365,"user":{"displayName":"María Sierro","userId":"02188206244184953197"}}},"execution_count":12,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'\"My report hinges on the notions of transparency and accountability , regulation and solidarity .\"\\n'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":12}]},{"cell_type":"code","source":["tokenized_train['target'][27]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"mIJeC0_SlPyW","executionInfo":{"status":"ok","timestamp":1683727702515,"user_tz":-120,"elapsed":2889,"user":{"displayName":"María Sierro","userId":"02188206244184953197"}},"outputId":"2cd43274-8ce8-4083-f6c4-ce91926d38bd"},"execution_count":13,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'\"L&apos; axe de mon rapport tourne autour des notions de transparence et de responsabilisation , de régulation et de solidarité .\"\\n'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":13}]},{"cell_type":"code","source":["from datasets import Dataset\n","# Crear un diccionario con las claves \"source\" y \"target\" para pasar a la función preprocess_function\n","data_dict = {\n","    \"source\": dev_en,\n","    \"target\": dev_fr,\n","}\n","\n","# Convertir el diccionario en un objeto Dataset\n","dev = Dataset.from_dict(data_dict)\n","\n","tokenized_dev = dev.map(preprocess_function, batched=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":17,"referenced_widgets":["e28927df73d24e1b87d9ea356e5c94de","8a9438723785425abdcca696790c5a8c","4bf91b7c8cce48da8864fdcfc088a232","bf395b67702b420ea2cb781a05dee791","2cf41033ba4541afa80176144c6c59f7","535a4e09219d49eb8e1b33fc029275ea","7ea3e3fa0e18460091e7e476a004e681","4b6ccd30ee994fbfa8a9b859d81a139e","d94fd293b9094768b5c7931c6fe04a8e","5ec92e36d4f94f969a1959dd740ae324","e08e056cbdd9425e8bb7239042ac88cd"]},"outputId":"e39c15e2-4272-407a-badf-be2d9e587fd8","id":"5bra04T0RB3y","executionInfo":{"status":"ok","timestamp":1683727705578,"user_tz":-120,"elapsed":1094,"user":{"displayName":"María Sierro","userId":"02188206244184953197"}}},"execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/1000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e28927df73d24e1b87d9ea356e5c94de"}},"metadata":{}}]},{"cell_type":"markdown","source":["## Start the fine-tuning and training"],"metadata":{"id":"K5ZYr08rxOEP"}},{"cell_type":"code","source":["from transformers import AutoModelForSeq2SeqLM, DataCollatorForSeq2Seq, Seq2SeqTrainingArguments, Seq2SeqTrainer\n","model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)"],"metadata":{"id":"brEGDBmrP2TL","executionInfo":{"status":"ok","timestamp":1683727721310,"user_tz":-120,"elapsed":13460,"user":{"displayName":"María Sierro","userId":"02188206244184953197"}},"colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["4c7f5b5882694acf9300b98b84aa928a","451d3e7f0e78493e9b03473b4671b3cb","8f12a523e67447828802eab56e096ac8","26b7048226d14e3bb7cf5a42fb583182","793c2631e1404f27a250430f64a6c6cf","4809c30960e84cc5ad74cbc5fd9fac11","1cb9d689f5d647bd819c9eeef6a85617","0dd833a9b5cc4ecfac59aee44a567550","ac8672531ebb448c8fac40c05c97e76a","650260ee0c2641b7b925ae589858991a","0e449c8f5e2c44f29780521dcd5ad84a","b5b4356a27144c89a64d264a60fa73bc","edf5c2b9afbf480c906dd8782f94dac8","56a8c757a2554a3ca3ece391d0cd70ab","3c383340e9944785a6855663667e6417","eb5134af549a466f9fedac25e5eef424","c3a6bfc2c93f42b3a2934befbb907944","a8f67fd075c2483da48f33eee852c121","98e8ef7e41f74a128e081b2dcde8a35d","42e41bc5285c4240ac72d92fcf982e8f","5a65243d847141f2917501ad0ed6e5b5","3edce64c07c742fc9bb98a85c2b22282"]},"outputId":"04bf27f5-57f3-4f7c-a673-e1782cefbcd6"},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading pytorch_model.bin:   0%|          | 0.00/301M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4c7f5b5882694acf9300b98b84aa928a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)neration_config.json:   0%|          | 0.00/293 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b5b4356a27144c89a64d264a60fa73bc"}},"metadata":{}}]},{"cell_type":"code","source":["batch_size = 16\n","model_name = model_checkpoint.split(\"/\")[-1]\n","args = Seq2SeqTrainingArguments(\n","    f\"{model_name}-finetuned-{source_lang}-to-{target_lang}\",\n","    evaluation_strategy = \"epoch\",\n","    learning_rate=2e-5,\n","    per_device_train_batch_size=batch_size,\n","    per_device_eval_batch_size=batch_size,\n","    weight_decay=0.01,\n","    save_total_limit=3,\n","    num_train_epochs=1,\n","    predict_with_generate=True    \n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5eJB-4R0P5r7","outputId":"754a22cd-c0c7-4871-a2f4-f5cb4a33c5e7","executionInfo":{"status":"ok","timestamp":1683727723388,"user_tz":-120,"elapsed":216,"user":{"displayName":"María Sierro","userId":"02188206244184953197"}}},"execution_count":16,"outputs":[{"output_type":"stream","name":"stderr","text":["Using the `WANDB_DISABLED` environment variable is deprecated and will be removed in v5. Use the --report_to flag to control the integrations used for logging result (for instance --report_to none).\n"]}]},{"cell_type":"code","source":["#We define our datacollator\n","data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)"],"metadata":{"id":"qYoCZOkxQF6V","executionInfo":{"status":"ok","timestamp":1683727725694,"user_tz":-120,"elapsed":5,"user":{"displayName":"María Sierro","userId":"02188206244184953197"}}},"execution_count":17,"outputs":[]},{"cell_type":"code","source":["#We define some processing functions to check the metrics\n","!pip install evaluate\n","import evaluate\n","metric = evaluate.load(\"bleu\")\n","\n","import numpy as np\n","def postprocess_text(preds, labels):\n","    preds = [pred.strip() for pred in preds]\n","    labels = [[label.strip()] for label in labels]\n","    return preds, labels\n","def compute_metrics(eval_preds):\n","    preds, labels = eval_preds\n","    if isinstance(preds, tuple):\n","        preds = preds[0]\n","    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n","    # Replace -100 in the labels as we can't decode them.\n","    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n","    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n","    # Some simple post-processing\n","    decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)\n","    result = metric.compute(predictions=decoded_preds, references=decoded_labels)\n","    result = {\"bleu\": result[\"bleu\"]}\n","    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]\n","    result[\"gen_len\"] = np.mean(prediction_lens)\n","    result = {k: round(v, 4) for k, v in result.items()}\n","    return result"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":738,"referenced_widgets":["fd3b22205c544027a592e508cd4cd82f","d7a96373c37a4193844728db0c810e8b","56d49ef6b7a24235b7ee706824649cd0","8ce089ad58184f418370a5752f958ba9","dc36f4e2599c441eaf5834aa483031df","b3d0b1ff2fd24a94bbf535850b8075a1","56ccba3cb20844b69a3c074ae9b81c9f","23e3072c27964e5cb81ffe4439ee2c50","6eb3e1ebde654de88110c689bfab39c3","715ac0c3aebd4b5abf1b8422a803dc02","febc07a5f39a4438b137285434db7858","fe18bb8346db4cc19b6828b8d855c514","1f21775571dc4d0d8dcea94f165d915a","bde043a216f644c2b2b6ad7e5c67a90e","6aaaeb92989948d2abf6ebcdc57e1fbd","070d19753cb44bd4a5b1c69d3b7aa044","200f47c66f3547a1bdff904ee34cbbff","36bf023e220d4f77b5947ea34225a6c9","abbd7b365d4c496a923a33e4c42ab1ed","55f8d0ced6604a87adc069a4f1d4c076","2e5498116745423faec65834c215f23b","7dfc4ec487b34eefa84a6f5ea4415549","20f761d5ac274e0496c0704c5270de30","eb4063959ffe46dbb62f0dd1e5d877a7","912808d7f85a49dabac92d6149020eb4","05d8f69d6d164bce8888e753392d0c24","3b509b443f2b43c28e0364d5a66e7c27","e7ab32bcc8c14f088611c7eff7e6f40f","48f253d279614a8381ef830df8cd2ea7","50d3c18b3bb34005a9788f213f8caf5a","a8d337602ba14dcd83621e4adb89cbe7","a0dfe0c8f0694ee8ac8d600e519ee787","938cf985fb6f4c6e89db86394df91a06"]},"id":"e0e-v-cZQHBl","outputId":"114a0eac-3468-4594-960b-df173eb1ac16","executionInfo":{"status":"ok","timestamp":1683727733465,"user_tz":-120,"elapsed":6294,"user":{"displayName":"María Sierro","userId":"02188206244184953197"}}},"execution_count":18,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting evaluate\n","  Downloading evaluate-0.4.0-py3-none-any.whl (81 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m81.4/81.4 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: datasets>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from evaluate) (2.12.0)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from evaluate) (1.22.4)\n","Requirement already satisfied: dill in /usr/local/lib/python3.10/dist-packages (from evaluate) (0.3.6)\n","Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from evaluate) (1.5.3)\n","Requirement already satisfied: requests>=2.19.0 in 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?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fe18bb8346db4cc19b6828b8d855c514"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading extra modules:   0%|          | 0.00/3.34k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"20f761d5ac274e0496c0704c5270de30"}},"metadata":{}}]},{"cell_type":"code","source":["#We define the training parameters\n","trainer = Seq2SeqTrainer(\n","    model,\n","    args,\n","    train_dataset=tokenized_train,\n","    eval_dataset=tokenized_dev,\n","    data_collator=data_collator,\n","    tokenizer=tokenizer,\n","    compute_metrics=compute_metrics\n",")"],"metadata":{"id":"TT6wnNvRQWjF","executionInfo":{"status":"ok","timestamp":1683727740193,"user_tz":-120,"elapsed":5042,"user":{"displayName":"María Sierro","userId":"02188206244184953197"}}},"execution_count":19,"outputs":[]},{"cell_type":"code","source":["trainer.train()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":196},"id":"6Luq_wfZRwLw","outputId":"1335421d-2e70-4972-ffd4-9ff19d3e0321","executionInfo":{"status":"ok","timestamp":1683480110763,"user_tz":-120,"elapsed":12957673,"user":{"displayName":"María Sierro","userId":"02188206244184953197"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='117768' max='117768' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [117768/117768 3:35:56, Epoch 1/1]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Epoch</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","      <th>Bleu</th>\n","      <th>Gen Len</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>1</td>\n","      <td>0.275100</td>\n","      <td>0.259839</td>\n","      <td>0.438300</td>\n","      <td>62.202000</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=117768, training_loss=0.2723879138782502, metrics={'train_runtime': 12956.9562, 'train_samples_per_second': 145.426, 'train_steps_per_second': 9.089, 'total_flos': 6.387377377797734e+16, 'train_loss': 0.2723879138782502, 'epoch': 1.0})"]},"metadata":{},"execution_count":29}]},{"cell_type":"code","source":["#Saving it on Drive\n","output_dir = \"/content/drive/MyDrive/HAPLAPMaster/MachineTranslation/MT-project/fine-tuning-results/\"\n","\n","trainer.save_model(output_dir)\n"],"metadata":{"id":"lkFpOuznj65v"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Example of use. We try with a sentence that is in the original corpus of English used in the training."],"metadata":{"id":"cl5oX1cXkyEN"}},{"cell_type":"code","source":["from transformers import MarianMTModel, MarianTokenizer\n","!pip install sacremoses \n","\n","src_text = ['The European Parliament should work hard to make an ambitious , Europeanist response worthy of our citizens .']\n","# reference translation = Le Parlement européen doit travailler dur pour apporter une réponse européiste ambitieuse digne de nos concitoyens .\n","\n","tokenizer = MarianTokenizer.from_pretrained('/content/drive/MyDrive/HAPLAPMaster/MachineTranslation/MT-project/fine-tuning-results/')\n","model = MarianMTModel.from_pretrained('/content/drive/MyDrive/HAPLAPMaster/MachineTranslation/MT-project/fine-tuning-results/')\n","translated = model.generate(**tokenizer(src_text, return_tensors=\"pt\", padding=True))\n","[tokenizer.decode(t, skip_special_tokens=True) for t in translated]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"b3DWEYmxkdrq","outputId":"cceba068-ab9a-469e-d0cd-f48447ecd2e8","executionInfo":{"status":"ok","timestamp":1683721228200,"user_tz":-120,"elapsed":10048,"user":{"displayName":"María Sierro","userId":"02188206244184953197"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: sacremoses in /usr/local/lib/python3.10/dist-packages (0.0.53)\n","Requirement already satisfied: regex in /usr/local/lib/python3.10/dist-packages (from sacremoses) (2022.10.31)\n","Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from sacremoses) (1.16.0)\n","Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from sacremoses) (8.1.3)\n","Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from sacremoses) (1.2.0)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from sacremoses) (4.65.0)\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1313: UserWarning: Using `max_length`'s default (512) to control the generation length. This behaviour is deprecated and 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"]},{"output_type":"execute_result","data":{"text/plain":["['Le Parlement européen devrait travailler dur pour apporter une réponse ambitieuse et européiste digne de nos▁citoyens.']"]},"metadata":{},"execution_count":20}]},{"cell_type":"code","source":["# PREPARING THE TEST SET FOR EVALUATION\n","\n","test_dataset_en = \"/content/drive/MyDrive/HAPLAPMaster/MachineTranslation/MT-project/en-fr-baseline/test.en\"\n","test_dataset_fr = \"/content/drive/MyDrive/HAPLAPMaster/MachineTranslation/MT-project/en-fr-baseline/test.fr\"\n","\n","with open(test_dataset_en, \"r\", encoding=\"utf-8\") as f:\n","    test_en = f.readlines()\n","\n","with open(test_dataset_fr, \"r\", encoding=\"utf-8\") as f:\n","    test_fr = f.readlines()"],"metadata":{"id":"KqfBHU_lduwU","executionInfo":{"status":"ok","timestamp":1683727795261,"user_tz":-120,"elapsed":1031,"user":{"displayName":"María Sierro","userId":"02188206244184953197"}}},"execution_count":20,"outputs":[]},{"cell_type":"code","source":["from datasets import Dataset\n","# Crear un diccionario con las claves \"source\" y \"target\" para pasar a la función preprocess_function\n","data_dict = {\n","    \"source\": test_en,\n","    \"target\": test_fr,\n","}\n","\n","# Convertir el diccionario en un objeto Dataset\n","test = Dataset.from_dict(data_dict)\n","\n","tokenized_test = test.map(preprocess_function, batched=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":17,"referenced_widgets":["7bcfccfbbb564bcba13a3ae8a12fa620","50624637110d4f4faef977005c775b3b","28f1cb54c5384c6ea3f0f77e21383589","8cde4ba8bd9b42d2b05bbecda59a89d8","c150238f8db44034882b06ef99ad252e","2e406955454f48f6be00556f0dfd3441","b5205d4e1f2f4d8d8798411216276d51","76e46ef5da354df4a8825b27e3c03898","d8e93378a9ae476ea0d3328145431e9e","2acd8810747b4cf4b8f364aa326bbcdf","0cae48bc8c6f49d4894f70c8f178159b"]},"id":"1lHwGE3WeKHF","executionInfo":{"status":"ok","timestamp":1683727798070,"user_tz":-120,"elapsed":809,"user":{"displayName":"María Sierro","userId":"02188206244184953197"}},"outputId":"fe9943f8-8ff5-4b15-adf3-c6440cee6e54"},"execution_count":21,"outputs":[{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/330 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7bcfccfbbb564bcba13a3ae8a12fa620"}},"metadata":{}}]},{"cell_type":"code","source":["tokenized_test"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"r8qScJiyeh0c","executionInfo":{"status":"ok","timestamp":1683727800297,"user_tz":-120,"elapsed":7,"user":{"displayName":"María Sierro","userId":"02188206244184953197"}},"outputId":"5caf2a27-0c2f-4f36-f1d2-771cb785cf90"},"execution_count":22,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Dataset({\n","    features: ['source', 'target', 'input_ids', 'attention_mask', 'labels'],\n","    num_rows: 330\n","})"]},"metadata":{},"execution_count":22}]},{"cell_type":"code","source":["tokenized_test['source'][27]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":53},"id":"MeGYoR9DeqsU","executionInfo":{"status":"ok","timestamp":1683727802197,"user_tz":-120,"elapsed":212,"user":{"displayName":"María Sierro","userId":"02188206244184953197"}},"outputId":"29ae8aa6-c87b-4715-fbcc-6d8f23508873"},"execution_count":23,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'The Grand Chamber to be constituted included ex officio Mr Ryssdal , President of the Court , Mr Bernhardt , Vice-President of the Court , and the other members and substitute judges ( namely , Mr B. Repik , Mr F. Gölcüklü , Mr R. Pekkanen and Mr K. Jungwiert ) of the Chamber which had relinquished jurisdiction ( Rule 51 para . 2 ( a ) and ( b ) ) .\\n'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":23}]},{"cell_type":"code","source":["tokenized_test['target'][27]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":53},"id":"YtqpiMV4eutp","executionInfo":{"status":"ok","timestamp":1683727805422,"user_tz":-120,"elapsed":213,"user":{"displayName":"María Sierro","userId":"02188206244184953197"}},"outputId":"f6cb8cd7-725e-4c35-e062-5315d6ef30eb"},"execution_count":24,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'Conformément à l ’ article 51 par . 2 a ) et b ) du règlement A , le président et le vice-président de la Cour , M. Ryssdal et M. Bernhardt , ainsi que les autres membres et juges suppléants ( à savoir M. B. Repik , M. F. Gölcüklü , M. R. Pekkanen et M. K. Jungwiert ) de la chambre originaire sont devenus membres de la grande chambre .\\n'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":24}]},{"cell_type":"code","source":["# EVALUATING THE MODEL WITH THE TEST SET\n","\n","trainer.evaluate(eval_dataset=tokenized_test)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":141},"id":"Z8kyL4zke7kr","executionInfo":{"status":"ok","timestamp":1683535017412,"user_tz":-120,"elapsed":45472,"user":{"displayName":"María Sierro","userId":"02188206244184953197"}},"outputId":"5f202faa-61ff-47c8-c49c-7fb2fe53dcc1"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='21' max='21' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [21/21 00:38]\n","    </div>\n","    "]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["{'eval_loss': 6.845170497894287,\n"," 'eval_bleu': 0.1629,\n"," 'eval_gen_len': 35.4212,\n"," 'eval_runtime': 46.0068,\n"," 'eval_samples_per_second': 7.173,\n"," 'eval_steps_per_second': 0.456}"]},"metadata":{},"execution_count":24}]},{"cell_type":"code","source":["# PREPARING ONE TEXT OF THE TEST SET FOR TRANSLATION\n","\n","partial_test_en = \"/content/drive/MyDrive/HAPLAPMaster/MachineTranslation/MT-project/en-fr/partial-test.en\"\n","partial_test_fr = \"/content/drive/MyDrive/HAPLAPMaster/MachineTranslation/MT-project/en-fr/partial-test.fr\"\n","\n","with open(partial_test_en, \"r\", encoding=\"utf-8\") as f:\n","    p_test_en = f.readlines()\n","\n","with open(partial_test_fr, \"r\", encoding=\"utf-8\") as f:\n","    p_test_fr = f.readlines()"],"metadata":{"id":"smq4mIPGAknJ","executionInfo":{"status":"ok","timestamp":1683728110373,"user_tz":-120,"elapsed":1150,"user":{"displayName":"María Sierro","userId":"02188206244184953197"}}},"execution_count":25,"outputs":[]},{"cell_type":"code","source":["from transformers import MarianMTModel, MarianTokenizer\n","!pip install sacremoses \n","\n","# TRANSLATING ONE TEXT OF THE TEST SET \n","\n","tokenizer = MarianTokenizer.from_pretrained('/content/drive/MyDrive/HAPLAPMaster/MachineTranslation/MT-project/fine-tuning-results/')\n","model = MarianMTModel.from_pretrained('/content/drive/MyDrive/HAPLAPMaster/MachineTranslation/MT-project/fine-tuning-results/')\n","translated = model.generate(**tokenizer(p_test_en, return_tensors=\"pt\", padding=True))\n","[tokenizer.decode(t, skip_special_tokens=True) for t in translated]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"m7_OcNV7djbt","outputId":"1686c547-50ac-405e-e88e-8882402bfe73","executionInfo":{"status":"ok","timestamp":1683728261330,"user_tz":-120,"elapsed":50361,"user":{"displayName":"María Sierro","userId":"02188206244184953197"}}},"execution_count":27,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: sacremoses in /usr/local/lib/python3.10/dist-packages (0.0.53)\n","Requirement already satisfied: regex in /usr/local/lib/python3.10/dist-packages (from sacremoses) (2022.10.31)\n","Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from sacremoses) (1.16.0)\n","Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from sacremoses) (8.1.3)\n","Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from sacremoses) (1.2.0)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from sacremoses) (4.65.0)\n"]},{"output_type":"execute_result","data":{"text/plain":["['Procédure d&apos; intégration',\n"," '\"L ▁affaire a▁trouvé son▁origine dans une▁demande ( n ° 8866 / 04 ) déposée par un ressortissant▁britannique, M. Yassar Hussain, en vertu de l  article 34 de la Convention de sauvegarde des droits de l  homme et des▁libertés fondamentales ( &quot; la Convention &quot; ), à l  encontre du Royaume-Uni, le 1er mars 2004.\"',\n"," 'Le▁demandeur était représenté par M. Bromley de Lichfield Reynolds à Stoke-on-Trent.',\n"," '\"Le gouvernement du Royaume-Uni ( « le gouvernement » ) était représenté par son agent, M. J. Grager, du ministère des affaires étrangères et du Commonwealth.\"',\n"," '\"Le 16 février 2005, la Cour a décidé de transmettre la▁demande au gouvernement.\"',\n"," '\"Conformément aux dispositions de l&apos; article 29,▁paragraphe 3, de la Convention, elle a décidé d&apos; examiner le bien-fondé de la▁demande en même temps que sa recevabilité.\"',\n"," 'L&apos; état d&apos; avancement de l&apos; Union européenne dans le▁domaine de l&apos; énergie et de l&apos; énergie',\n"," 'I. LE CIRCUMgroCES DE L&apos; incitation à l&apos; aide d&apos; un système d&apos; aide au développement et à l&apos; aide d&apos; un système d&apos; aide au développement.',\n"," 'Le▁demandeur est né en 1981 et vit à Stoke-on-Trent.',\n"," 'Le▁demandeur a été arrêté le 14 mai 2002 pour▁soupçon d&apos; avoir intimidé des témoins.',\n"," '\"Lors d&apos; une▁parade d&apos; identité▁organisée le même après-midi, il a été identifié positivement par l&apos; un des deux témoins.\"',\n"," 'Le▁demandeur▁affirme que ce témoin a accepté dans sa déclaration initiale de preuve à la police qu&apos; elle était une utilisatrice d&apos; héroïne et qu&apos; elle n&apos; était pas fiable.',\n"," 'Le▁demandeur a ensuite été▁accusé de cinq chefs d&apos; accusation d&apos; avoir commis des▁actes tendant à pervertir le cours de la justice publique.',\n"," 'Le▁demandeur a plaidé non coupable le 2 août 2002 et le procès a été reporté devant la cour de la Cour de justice Stoke-on-Trent le 7 octobre 2002.',\n"," '\"À cette date, aucun▁juge n&apos; était disponible et le procès a été reporté au 3 février 2003.\"',\n"," '\"Le deuxième jour du procès, le témoin qui avait identifié le▁demandeur n&apos; était pas présent et le procès a dû être avorté.\"',\n"," '\"Lors du procès réinscrit le 15 septembre 2003, l&apos; avocat de la couronne s&apos; est adressé à la Cour dans les conditions suivantes :\"',\n"," '\"Lorsque cette▁affaire a été énumérée comme procès en février, &#91; L. &#93; était présente le premier jour.\"',\n"," 'Elle n&apos; a pas été appelée à donner des preuves et n&apos; est pas▁revenue le deuxième jour.',\n"," 'Cela a certainement suscité quelques inquiétudes.',\n"," '\"Je pense qu&apos; il est▁juste de dire qu&apos; à▁différents stades de l&apos;▁affaire, elle a fait montre d&apos; un peu de chaud et de froid.\"',\n"," '&#91; L. &#93; a indiqué à la police▁lorsqu&apos; elle a été contactée récemment et qu&apos; elle était en fait réticente à donner des témoignages hier.',\n"," '\"Elle a▁assisté au tribunal aujourd&apos; hui et, avec la permission de mon éminent ami, j&apos; ai eu l&apos; occasion de m&apos;▁asseoir et de▁discuter avec elle.\"',\n"," '\"Votre▁honorable, la réalité, c&apos; est qu&apos; elle ne veut pas témoigner.\"',\n"," 'Elle a des raisons personnelles que je n&apos; ai pas l&apos; intention de dénoncer en public.',\n"," 'Elle a constitué une nouvelle relation.',\n"," 'Elle a évolué.',\n"," 'Elle s&apos; efforce d&apos; arrêter les événements de toute cette▁affaire.',\n"," 'Elle a▁satisfait la police qui s&apos; est entretenue avec elle hier et a▁satisfait ceux qui m&apos; ont dit qu&apos; aucune▁menace n&apos; avait été faite à son encontre dans le cadre de la procédure d&apos; aujourd&apos; hui.',\n"," 'Elle▁insiste sur le fait qu&apos; il n&apos; y a eu aucun contact de la part du &#91;▁demandeur &#93; ou d&apos; autres personnes agissant en son nom.',\n"," '\"Nous ne pensons pas qu&apos; en raison de la nature de cette allégation, elle devrait être obligée de donner la preuve dans cette▁affaire.\"',\n"," '\"Votre▁honorable, il y a eu une discussion très complète et l&apos; avis que nous avons adopté est que, sans sa preuve, nous ne pouvons clairement pas procéder.\"',\n"," 'Je n&apos; apporte aucune preuve.',\n"," 'Les vérdicts doivent suivre. »',\n"," 'Le▁demandeur a été dûment acquitté.',\n"," '\"En ce qui▁concerne la▁demande d&apos; injonction d&apos; un défendeur, le▁juge a refusé d&apos; introduire une décision et a déclaré :\"',\n"," 'Cet ordre sera refusé.',\n"," 'Il y a des preuves claires sur les documents des tribunaux.',\n"," '\"La Cour a estimé qu&apos; elle n&apos; allait pas obliger ce témoin, bien qu&apos; il▁existe des preuves incontestables à cet égard.\"',\n"," 'C  est une▁discrétion que je ne vais malheureusement pas exercer en votre faveur. »',\n"," '\"La tentative d  appel du▁demandeur a été rejetée le 14 novembre 2003, car « pour pouvoir▁faire l  objet d  un appel comme condamnation, l  ordonnance doit être conditionnée à la condamnation.\"',\n"," '\"Étant donné que l&apos; injonction de l&apos; inculpé n&apos; intervient qu&apos; en cas d&apos; échec d&apos; une poursuite, elle ne peut être condamnée et ne peut▁faire l&apos; objet d&apos; un recours devant la Cour d&apos; appel ».\"']"]},"metadata":{},"execution_count":27}]}]}