Classification_From_Bert_AIL.ipynb

{
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    "id": "tE-EhgPxP3xW",
    "outputId": "8b797041-be7c-4871-fdad-8b0b672ef9e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install -U transformers datasets huggingface_hub accelerate torch huggingface_hub wandb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "b9OdRSD5_F0d"
   },
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "dataset = load_dataset(\"Nielzac/CoM_Audio_Image_LLM_Generation\")\n",
    "train_s = dataset[\"train\"]\n",
    "\n",
    "max_source_length = 128\n",
    "max_target_length = 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SXPu_O0HRu0m",
    "outputId": "fd2b7676-501d-4c04-a8bf-0db49f6e6331"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "from transformers import BertForSequenceClassification, TrainingArguments, Trainer, AutoTokenizer, DataCollatorWithPadding\n",
    "model_id = \"bert-base-uncased\"\n",
    "model = BertForSequenceClassification.from_pretrained(model_id, num_labels=3)\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "1_mliF60_y5A"
   },
   "outputs": [],
   "source": [
    "val_size = int(0.2 * len(train_s)) # 20% du dataset pour la validation\n",
    "\n",
    "train_dataset = train_s.train_test_split(test_size=val_size, seed=42)[\"train\"]\n",
    "val_dataset = train_s.train_test_split(test_size=val_size, seed=42)[\"test\"]\n",
    "\n",
    "train_dataset = train_dataset.rename_column(\"Text\", \"text\")\n",
    "train_dataset = train_dataset.rename_column(\"Label\", \"labels\")\n",
    "\n",
    "val_dataset = val_dataset.rename_column(\"Text\", \"text\")\n",
    "val_dataset = val_dataset.rename_column(\"Label\", \"labels\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "DomJyAEyUS7j"
   },
   "outputs": [],
   "source": [
    "def tokenize(batch):\n",
    "    return tokenizer(batch[\"text\"], truncation=True, padding=\"max_length\", max_length=max_source_length, add_special_tokens=True, return_tensors='pt')\n",
    "def filter_null(batch):\n",
    "    non_null_indices = batch[\"text\"] != \"\"\n",
    "    return {\"input_ids\": batch[\"input_ids\"][non_null_indices],\n",
    "            \"attention_mask\": batch[\"attention_mask\"][non_null_indices]}\n",
    "\n",
    "train_dataset = train_dataset.map(tokenize, batched=True).filter(filter_null)\n",
    "val_dataset = val_dataset.map(tokenize, batched=True).filter(filter_null)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 201
    },
    "id": "QBRJPRVoqtCX",
    "outputId": "d2316f31-b2f1-498c-b150-ada7d760aae4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mzengdar\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Changes to your `wandb` environment variables will be ignored because your `wandb` session has already started. For more information on how to modify your settings with `wandb.init()` arguments, please refer to <a href='https://wandb.me/wandb-init' target=\"_blank\">the W&B docs</a>."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Tracking run with wandb version 0.16.4"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Run data is saved locally in <code>/workspace/wandb/run-20240316_145231-q4jo3z5r</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "Syncing run <strong><a href='https://wandb.ai/zengdar/CoM_Small_AIL/runs/q4jo3z5r' target=\"_blank\">copper-vortex-1</a></strong> to <a href='https://wandb.ai/zengdar/CoM_Small_AIL' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
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      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/html": [
       " View project at <a href='https://wandb.ai/zengdar/CoM_Small_AIL' target=\"_blank\">https://wandb.ai/zengdar/CoM_Small_AIL</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       " View run at <a href='https://wandb.ai/zengdar/CoM_Small_AIL/runs/q4jo3z5r' target=\"_blank\">https://wandb.ai/zengdar/CoM_Small_AIL/runs/q4jo3z5r</a>"
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     "metadata": {},
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    }
   ],
   "source": [
    "#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\n",
    "from huggingface_hub import notebook_login\n",
    "import os \n",
    "import wandb\n",
    "wandb.login()\n",

    "notebook_login()\n",
    "\n",
    "\n",
    "run = wandb.init(\n",
    "    # Set the project where this run will be logged\n",
    "    project=\"CoM_Small_AIL\",\n",
    "    # Track hyperparameters and run metadata\n",
    "    config={\n",
    "        \"learning_rate\": 0.01,\n",
    "        \"epochs\": 20,\n",
    "    },\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JD5TZHBbP_eN",
    "outputId": "73c1351e-9d0e-4585-a1f8-1cf218bef866"
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "training_args = TrainingArguments(\n",
    "    output_dir=\"CoM_Small_AIL\",\n",
    "    push_to_hub=True,\n",
    "    num_train_epochs=20,\n",
    "    per_device_train_batch_size=34,\n",
    "    per_device_eval_batch_size=64,\n",
    "    warmup_steps=500,\n",
    "    weight_decay=0.01,\n",
    "    evaluation_strategy=\"epoch\",\n",
    ")\n",
    "checkpoint_path = \"./checkpoint-7500\"\n",
    "\n",
    "# Créer l'entraîneur\n",
    "trainer = Trainer(\n",
    "    model=model.from_pretrained(checkpoint_path),\n",
    "    args=training_args,\n",
    "    train_dataset=train_dataset,\n",
    "    eval_dataset=val_dataset,\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='787' max='787' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [787/787 02:27, 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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.002200</td>\n",
       "      <td>0.044438</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=787, training_loss=0.007865190505981445, metrics={'train_runtime': 147.5686, 'train_samples_per_second': 181.238, 'train_steps_per_second': 5.333, 'total_flos': 1759242089283840.0, 'train_loss': 0.007865190505981445, 'epoch': 1.0})"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "id": "Mab2QCWbaCWM"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'eval_loss': 0.044438157230615616,\n",
       " 'eval_runtime': 11.4777,\n",
       " 'eval_samples_per_second': 582.523,\n",
       " 'eval_steps_per_second': 9.148,\n",
       " 'epoch': 1.0}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.evaluate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "da09a84c495d4a5c9d9d707dfb06dd19",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "model.safetensors:   0%|          | 0.00/438M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "CommitInfo(commit_url='https://huggingface.co/Nielzac/CoM_Small_AIL/commit/e91f8efe93a3cbbc651e52edf1ecdf2c12ea7b1a', commit_message='./CoM_Small_AIL', commit_description='', oid='e91f8efe93a3cbbc651e52edf1ecdf2c12ea7b1a', pr_url=None, pr_revision=None, pr_num=None)"
      ]
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