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+{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":20058,"status":"ok","timestamp":1696057052179,"user":{"displayName":"Михаил Дворецкий","userId":"05054516382534405435"},"user_tz":-180},"id":"YQE9iI7muSEE","outputId":"d0febd6e-5ef6-4ae0-bb15-c16f2e8ccedd"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting transformers\n","  Downloading transformers-4.33.3-py3-none-any.whl (7.6 MB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m48.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hCollecting datasets\n","  Downloading datasets-2.14.5-py3-none-any.whl (519 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m519.6/519.6 kB\u001b[0m \u001b[31m38.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: filelock in 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six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n","Installing collected packages: tokenizers, safetensors, xxhash, dill, multiprocess, huggingface-hub, transformers, datasets\n","Successfully installed datasets-2.14.5 dill-0.3.7 huggingface-hub-0.17.3 multiprocess-0.70.15 safetensors-0.3.3 tokenizers-0.13.3 transformers-4.33.3 xxhash-3.3.0\n"]}],"source":["!pip install transformers datasets"]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":17,"status":"ok","timestamp":1696057052180,"user":{"displayName":"Михаил Дворецкий","userId":"05054516382534405435"},"user_tz":-180},"id":"xAhSzSv4tGKi","outputId":"d5cdc85b-6682-42c7-eae8-041507478bf9"},"outputs":[{"output_type":"stream","name":"stdout","text":["Token will not been saved to git credential helper. Pass `add_to_git_credential=True` if you want to set the git credential as well.\n","Token is valid (permission: write).\n","Your token has been saved to /root/.cache/huggingface/token\n","Login successful\n"]}],"source":["!huggingface-cli login --token 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"]},{"cell_type":"code","source":["!pip install transformers[torch]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3fuCLWFPxkFl","executionInfo":{"status":"ok","timestamp":1696057060107,"user_tz":-180,"elapsed":7937,"user":{"displayName":"Михаил Дворецкий","userId":"05054516382534405435"}},"outputId":"9a74689c-dad7-44f8-870b-66357d6afab0"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: transformers[torch] in /usr/local/lib/python3.10/dist-packages (4.33.3)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (3.12.2)\n","Requirement already satisfied: 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accelerate-0.23.0\n"]}]},{"cell_type":"code","execution_count":4,"metadata":{"executionInfo":{"elapsed":10255,"status":"ok","timestamp":1696057070335,"user":{"displayName":"Михаил Дворецкий","userId":"05054516382534405435"},"user_tz":-180},"id":"YoYMH8g6tl77"},"outputs":[],"source":["from transformers import AutoTokenizer, TrainingArguments, Trainer, AutoModelForQuestionAnswering\n","import pandas as pd\n","from transformers import DefaultDataCollator\n","from datasets import Dataset"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":317,"referenced_widgets":["6a9405378a2c4b768198bec703b6c5ed","ff31f8dba656432c9eba301e4decd6fd","5009a50415b34664bbbdb97dd13f4a08","776a34370620464cac886019dd9b20b9","d3b72b3110484652938c1866a2425e66","ad634cd3a4e54436b2c9deb3e67c587e","639d04ab4a154aea9d89a2b82d594a75","9829e2d444664b24a123dd12fb60b45c","7e1fff101381474eaf784849b63a492a","e3f4c5837c2a40e4b1b69aae4551818b","ccc1ba717346441784201a8374b37eef","787e83175dc544259751c526731adfc3","f4d48177fd4b4ef8810b270b51f65058","d458f015e0264adab4a6a4143bf3b0fa","8277a542522c4c18b8b5287b0c7e4c50","ca708c4952154b789228f5e30ca8b6bc","a975eafbb45c4d898a121e4f7471c523","ba9f9de1cd73492482eb3eb51907f8cc","5a098c18bb72449d92367f7c2bf35f30","cd3ae104e9584b238881d2d8d125cf82","ff388bc683974a6e88bd5df073bd7773","6b7b3937368949599a572b9509cc4392","de5b1372628741eca6e49bf226b54fe7","06fa804ff03e4f77b96ba0669234f0e0","8358f38bb15149fc984cb2c70733997c","d44d054f85f14e7d947963543a7b9260","eba18c15d71b466c84c366c2efd0be1d","33257c3f1cf840c59660b2e3c80a0b3e","c7b581a0e0804a999eb807d4408128ff","40f5000ca9024076b950a7e26538ac05","d721928ea4b24cc28b250fd3eb80a6ee","8732ac9c9098488e9c6065162fb29991","f1b9aefa512d4763ab6e372eb4ce9a5d","783b1e764d994dcebd1cfaeabfdda844","1fb77166964443a6aed1863d36933c4b","e32a281517344ae09449be971eb67da7","c291262f0ad74a2f95d24ef00a5da470","e739dee905d845978505c7807d1c522a","7020eba8292e4e78a099185032d9c538","8b1b641792bc419e88d0db49ec4d4e90","1877325aa28b48ff8d9c97d672b52b2c","a22d7aadf5bb4f62b4b825827fd3f9a7","92f2f3c007fd4705b6945a6c4570db0e","c57cff14464c4f209fcc033194e7b1c3","55213581b1fd4339b78d5dc023d952c3","c1aa6eb05acc44a4a01ee9d0217d3b91","1c64e45f9ded444abab117e2d6e1fdc6","b5010912d9a6491498f1d7980fffe1ba","acd25676ea744dd49fbfabb4d8e793f9","e077ea61963f407fb13e2fe9cd542230","214dda72ea174daaa6bbdea2f3a22a95","a39d8a50eb6f479883add717bdceee94","e8ddabe424394525882925b9b912a0d4","60ac7cd117f2421d98af0874ccf90996","11e017ac2fee4fa2a291e8e71ba74894"]},"executionInfo":{"elapsed":5231,"status":"ok","timestamp":1696057075558,"user":{"displayName":"Михаил Дворецкий","userId":"05054516382534405435"},"user_tz":-180},"id":"RwEBZ6YGtrGP","outputId":"c980ce7e-c4ab-455f-824a-e23b667e6271"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading (…)okenizer_config.json:   0%|          | 0.00/516 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6a9405378a2c4b768198bec703b6c5ed"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)lve/main/config.json:   0%|          | 0.00/781 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"787e83175dc544259751c526731adfc3"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)tencepiece.bpe.model:   0%|          | 0.00/5.07M [00:00<?, 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Дворецкий","userId":"05054516382534405435"},"user_tz":-180},"id":"z5mIoQgUtt66"},"outputs":[],"source":["data = pd.read_csv(\"/content/drive/MyDrive/Colab_Notebooks/sample_data/questions_answers.csv\").drop(\"Unnamed: 0\", axis=1)\n","data.columns = ['question', 'answers', 'context']\n","\n","for i in range(data.shape[0]):\n","    data[\"answers_start\"] = data[\"context\"][i].find(data[\"answers\"][i])"]},{"cell_type":"code","execution_count":9,"metadata":{"executionInfo":{"elapsed":638,"status":"ok","timestamp":1696057096585,"user":{"displayName":"Михаил Дворецкий","userId":"05054516382534405435"},"user_tz":-180},"id":"PxgDnIjMtzOS"},"outputs":[],"source":["def preprocessing(data):\n","    questions = [q.strip() for q in data[\"question\"]]\n","    inputs = tokenizer(\n","        questions,\n","        data[\"context\"].tolist(),\n","        max_length=500,\n","        truncation=\"only_second\",\n","        return_offsets_mapping=True,\n","        padding=\"max_length\",\n","    )\n","    offset_mapping = inputs.pop(\"offset_mapping\")\n","    answers = data[\"answers\"].tolist()\n","    start_positions = []\n","    end_positions = []\n","\n","    for i, offset in enumerate(offset_mapping):\n","        answer = answers[i]\n","        start_char = data[\"answers_start\"]\n","        end_char = data[\"answers_start\"] + len(answer)\n","        sequence_ids = inputs.sequence_ids(i)\n","\n","        # Find the start and end of the context\n","        idx = 0\n","        while sequence_ids[idx] != 1:\n","            idx += 1\n","        context_start = idx\n","        while sequence_ids[idx] == 1:\n","            idx += 1\n","        context_end = idx - 1\n","        # If the answer is not fully inside the context, label it (0, 0)\n","        if offset[context_start][0] > end_char[i] or offset[context_end][1] < start_char[i]:\n","            start_positions.append(0)\n","            end_positions.append(0)\n","        else:\n","            # Otherwise it's the start and end token positions\n","            idx = context_start\n","            while idx <= context_end and offset[idx][0] <= start_char[i]:\n","                idx += 1\n","            start_positions.append(idx - 1)\n","\n","            idx = context_end\n","            while idx >= context_start and offset[idx][1] >= end_char[i]:\n","                idx -= 1\n","            end_positions.append(idx + 1)\n","\n","    inputs[\"start_positions\"] = start_positions\n","    inputs[\"end_positions\"] = end_positions\n","    return inputs"]},{"cell_type":"code","execution_count":10,"metadata":{"executionInfo":{"elapsed":1112,"status":"ok","timestamp":1696057101748,"user":{"displayName":"Михаил Дворецкий","userId":"05054516382534405435"},"user_tz":-180},"id":"lFLCNe6Vt0Yk"},"outputs":[],"source":["data = Dataset.from_dict(preprocessing(data))"]},{"cell_type":"code","execution_count":11,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":77,"referenced_widgets":["6c0f9224645f43c093c2d355fdee335d","cad7db9c8546411bbce1f3abf250058d","b30767e8371c45209ce78372425556f2","0e0d5cdcd8d14cd38edee1a9d7e259f4","334499cf1e29446b9d0241d6c59552bd","8a10971754004930836fe820e49b212d","ed43aadb6c5449d78d6a75deab809bf4","66dc2621a32c4364b9879a4f7e2457f0","ab1733d7a1fb4abca477eac077a51047","6dca6550bef84edb90542ded37fb0abf","40e0081f38ed4a93af5eeb8b6f728619"]},"id":"h8jfXolgt3XJ","executionInfo":{"status":"ok","timestamp":1696057127991,"user_tz":-180,"elapsed":25705,"user":{"displayName":"Михаил Дворецкий","userId":"05054516382534405435"}},"outputId":"eb584389-bef6-4007-ded2-bfa92f2bac4b"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading pytorch_model.bin:   0%|          | 0.00/2.24G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6c0f9224645f43c093c2d355fdee335d"}},"metadata":{}}],"source":["model = AutoModelForQuestionAnswering.from_pretrained(\"AlexKay/xlm-roberta-large-qa-multilingual-finedtuned-ru\").cuda(\"cuda\")"]},{"cell_type":"code","source":["data_collator = DefaultDataCollator()"],"metadata":{"id":"BglPzKPWzB3Y","executionInfo":{"status":"ok","timestamp":1696057127992,"user_tz":-180,"elapsed":31,"user":{"displayName":"Михаил Дворецкий","userId":"05054516382534405435"}}},"execution_count":12,"outputs":[]},{"cell_type":"code","execution_count":13,"metadata":{"id":"U6uS2zXCt5ys","executionInfo":{"status":"ok","timestamp":1696057127992,"user_tz":-180,"elapsed":29,"user":{"displayName":"Михаил Дворецкий","userId":"05054516382534405435"}}},"outputs":[],"source":["training_args = TrainingArguments(\n","    output_dir=\"/content/drive/MyDrive/Colab_Notebooks/\",\n","    evaluation_strategy=\"epoch\",\n","    learning_rate=2e-5,\n","    per_device_train_batch_size=5,\n","    per_device_eval_batch_size=5,\n","    num_train_epochs=3,\n","    weight_decay=0.01,\n","    push_to_hub=True,\n",")"]},{"cell_type":"code","execution_count":14,"metadata":{"id":"XMy37I48t8Md","executionInfo":{"status":"ok","timestamp":1696057127992,"user_tz":-180,"elapsed":29,"user":{"displayName":"Михаил Дворецкий","userId":"05054516382534405435"}}},"outputs":[],"source":["trainer = Trainer(\n","    model=model,\n","    args=training_args,\n","    train_dataset=data,\n","    eval_dataset=data,\n","    tokenizer=tokenizer,\n","    data_collator=data_collator,\n",")"]},{"cell_type":"code","execution_count":15,"metadata":{"id":"LoaQj2ZHt8Tk","colab":{"base_uri":"https://localhost:8080/","height":221},"executionInfo":{"status":"ok","timestamp":1696057460669,"user_tz":-180,"elapsed":332123,"user":{"displayName":"Михаил Дворецкий","userId":"05054516382534405435"}},"outputId":"dfc3f1c6-b74e-4ce4-a914-a6d3497ee067"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='477' max='477' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [477/477 05:30, Epoch 3/3]\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","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>1</td>\n","      <td>No log</td>\n","      <td>0.283688</td>\n","    </tr>\n","    <tr>\n","      <td>2</td>\n","      <td>No log</td>\n","      <td>0.213056</td>\n","    </tr>\n","    <tr>\n","      <td>3</td>\n","      <td>No log</td>\n","      <td>0.113657</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=477, training_loss=0.45255054018032626, metrics={'train_runtime': 332.0341, 'train_samples_per_second': 1.437, 'train_steps_per_second': 1.437, 'total_flos': 432610581294000.0, 'train_loss': 0.45255054018032626, 'epoch': 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