fth2023-summarisation-model-training-sample.ipynb

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   "source": [
    "## Libraries"
   ]
  },
  {
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   "source": [
    "from transformers import PegasusForConditionalGeneration, PegasusTokenizer, Trainer, TrainingArguments\n",
    "import torch\n",
    "from datasets import load_dataset"
   ]
  },
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   },
   "source": [
    "## Dataset"
   ]
  },
  {
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   "source": [
    "class PegasusDataset(torch.utils.data.Dataset):\n",
    "    def __init__(self, encodings, labels):\n",
    "        self.encodings = encodings\n",
    "        self.labels = labels\n",
    "    def __getitem__(self, idx):\n",
    "        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
    "        item['labels'] = torch.tensor(self.labels['input_ids'][idx])\n",
    "        return item\n",
    "    def __len__(self):\n",
    "        return len(self.labels['input_ids'])"
   ]
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   "source": [
    "def prepare_data(model_name, \n",
    "                 train_texts, train_labels, \n",
    "                 val_texts=None, val_labels=None, \n",
    "                 test_texts=None, test_labels=None):\n",
    "    \"\"\"\n",
    "    Prepare input data for model fine-tuning\n",
    "    \"\"\"\n",
    "    tokenizer = PegasusTokenizer.from_pretrained(model_name)\n",
    "\n",
    "    prepare_val = False if val_texts is None or val_labels is None else True\n",
    "    prepare_test = False if test_texts is None or test_labels is None else True\n",
    "\n",
    "    def tokenize_data(texts, labels):\n",
    "        encodings = tokenizer(texts, truncation=True, padding=True)\n",
    "        decodings = tokenizer(labels, truncation=True, padding=True)\n",
    "        dataset_tokenized = PegasusDataset(encodings, decodings)\n",
    "        return dataset_tokenized\n",
    "\n",
    "    train_dataset = tokenize_data(train_texts, train_labels)\n",
    "    val_dataset = tokenize_data(val_texts, val_labels) if prepare_val else None\n",
    "    test_dataset = tokenize_data(test_texts, test_labels) if prepare_test else None\n",
    "\n",
    "    return train_dataset, val_dataset, test_dataset, tokenizer"
   ]
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   "source": [
    "## Finetune"
   ]
  },
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    "def fine_tune(model_name, tokenizer, train_dataset, val_dataset=None, freeze_encoder=False, output_dir='./results'):\n",
    "    \"\"\"\n",
    "    Prepare config and base model for fine-tuning\n",
    "    \"\"\"\n",
    "    torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "    model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device)\n",
    "\n",
    "    if freeze_encoder:\n",
    "        for param in model.model.encoder.parameters():\n",
    "            param.requires_grad = False\n",
    "\n",
    "    training_args = TrainingArguments(\n",
    "        output_dir=output_dir,\n",
    "        num_train_epochs=1,\n",
    "        per_device_train_batch_size=1,\n",
    "        save_steps=500,\n",
    "        save_total_limit=2,\n",
    "        warmup_steps=500,\n",
    "        weight_decay=0.01,\n",
    "        logging_dir='./logs',\n",
    "        logging_steps=200,\n",
    "    )\n",
    "\n",
    "    trainer = Trainer(\n",
    "        model=model,\n",
    "        args=training_args,\n",
    "        train_dataset=train_dataset,\n",
    "        tokenizer=tokenizer\n",
    "    )\n",
    "\n",
    "    return trainer"
   ]
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      "Downloading and preparing dataset json/default (download: 785.12 MiB, generated: 1.40 GiB, post-processed: Unknown size, total: 2.17 GiB) to /root/.cache/huggingface/datasets/parquet/nickmuchi--trade-the-event-finance-6bcad652637af166/0.0.0/0b6d5799bb726b24ad7fc7be720c170d8e497f575d02d47537de9a5bac074901...\n"
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      "Dataset parquet downloaded and prepared to /root/.cache/huggingface/datasets/parquet/nickmuchi--trade-the-event-finance-6bcad652637af166/0.0.0/0b6d5799bb726b24ad7fc7be720c170d8e497f575d02d47537de9a5bac074901. Subsequent calls will reuse this data.\n"
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     "text": [
      "/opt/conda/lib/python3.7/site-packages/transformers/optimization.py:310: 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",
      "  FutureWarning,\n",
      "***** Running training *****\n",
      "  Num examples = 1000\n",
      "  Num Epochs = 1\n",
      "  Instantaneous batch size per device = 1\n",
      "  Total train batch size (w. parallel, distributed & accumulation) = 1\n",
      "  Gradient Accumulation steps = 1\n",
      "  Total optimization steps = 1000\n",
      "Automatic Weights & Biases logging enabled, to disable set os.environ[\"WANDB_DISABLED\"] = \"true\"\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mmaze508\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
    },
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       "wandb version 0.13.8 is available!  To upgrade, please run:\n",
       " $ pip install wandb --upgrade"
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       "Run data is saved locally in <code>/kaggle/working/wandb/run-20230112_014156-34zhhumr</code>"
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       "Syncing run <strong><a href=\"https://wandb.ai/maze508/huggingface/runs/34zhhumr\" target=\"_blank\">./results</a></strong> to <a href=\"https://wandb.ai/maze508/huggingface\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>"
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       "    <div>\n",
       "      \n",
       "      <progress value='1000' max='1000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [1000/1000 08:32, Epoch 1/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>200</td>\n",
       "      <td>1.847700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>400</td>\n",
       "      <td>0.309700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>600</td>\n",
       "      <td>0.284500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>800</td>\n",
       "      <td>0.256300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1000</td>\n",
       "      <td>0.246200</td>\n",
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     "text": [
      "Saving model checkpoint to ./results/checkpoint-500\n",
      "Configuration saved in ./results/checkpoint-500/config.json\n",
      "Model weights saved in ./results/checkpoint-500/pytorch_model.bin\n",
      "tokenizer config file saved in ./results/checkpoint-500/tokenizer_config.json\n",
      "Special tokens file saved in ./results/checkpoint-500/special_tokens_map.json\n",
      "Saving model checkpoint to ./results/checkpoint-1000\n",
      "Configuration saved in ./results/checkpoint-1000/config.json\n",
      "Model weights saved in ./results/checkpoint-1000/pytorch_model.bin\n",
      "tokenizer config file saved in ./results/checkpoint-1000/tokenizer_config.json\n",
      "Special tokens file saved in ./results/checkpoint-1000/special_tokens_map.json\n",
      "\n",
      "\n",
      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=1000, training_loss=0.5888946800231933, metrics={'train_runtime': 521.1961, 'train_samples_per_second': 1.919, 'train_steps_per_second': 1.919, 'total_flos': 1444732207104000.0, 'train_loss': 0.5888946800231933, 'epoch': 1.0})"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load TTE Dataset\n",
    "ds_tte = load_dataset(\"nickmuchi/trade-the-event-finance\")\n",
    "train_texts, train_labels = ds_tte['train']['text'][:1000], ds_tte['train']['title'][:1000]\n",
    "\n",
    "# Fine Tune F-Sum Pegasus Large\n",
    "model_name = 'human-centered-summarization/financial-summarization-pegasus'\n",
    "train, __, __, tokenizer = prepare_data(model_name, train_texts, train_labels)\n",
    "trainer = fine_tune(model_name, tokenizer, train)\n",
    "\n",
    "trainer.train()"
   ]
  }
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