3-models-4-tokenizers.ipynb

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    "tags": []
   },
   "source": [
    "# Models"
   ]
  },
  {
   "cell_type": "markdown",
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    "tags": []
   },
   "source": [
    "## Creating a Transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4fe8e7af",
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    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-04-05 15:24:05.983197: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-04-05 15:24:05.983367: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-04-05 15:24:06.171027: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
     ]
    }
   ],
   "source": [
    "from transformers import BertConfig, TFBertModel\n",
    "\n",
    "# Building the config\n",
    "config = BertConfig()\n",
    "\n",
    "# Building the model from the config\n",
    "model = TFBertModel(config)"
   ]
  },
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     "text": [
      "BertConfig {\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 768,\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 3072,\n",
      "  \"layer_norm_eps\": 1e-12,\n",
      "  \"max_position_embeddings\": 512,\n",
      "  \"model_type\": \"bert\",\n",
      "  \"num_attention_heads\": 12,\n",
      "  \"num_hidden_layers\": 12,\n",
      "  \"pad_token_id\": 0,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"transformers_version\": \"4.38.2\",\n",
      "  \"type_vocab_size\": 2,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 30522\n",
      "}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(config)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfb01531",
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    },
    "tags": []
   },
   "source": [
    "### Different loading methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "beed8b4b",
   "metadata": {
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   "source": [
    "from transformers import BertConfig, TFBertModel\n",
    "\n",
    "config = BertConfig()\n",
    "model = TFBertModel(config)\n",
    "\n",
    "# Model is randomly initialized!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7292a5c1",
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    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e34d1df8dbf74012932a3c88d9221b58",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
      ]
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    {
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       "model_id": "9e0164a16f3143b6b0243b094efce92f",
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       "version_minor": 0
      },
      "text/plain": [
       "model.safetensors:   0%|          | 0.00/436M [00:00<?, ?B/s]"
      ]
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     "text": [
      "Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFBertModel: ['cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias']\n",
      "- This IS expected if you are initializing TFBertModel from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing TFBertModel from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "All the weights of TFBertModel were initialized from the PyTorch model.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertModel for predictions without further training.\n"
     ]
    }
   ],
   "source": [
    "from transformers import TFBertModel\n",
    "\n",
    "model = TFBertModel.from_pretrained(\"bert-base-cased\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68d65deb",
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     "start_time": "2024-04-05T15:24:27.488611",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Saving methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "49d50b9e",
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    "tags": []
   },
   "outputs": [],
   "source": [
    "model.save_pretrained(\"directory_on_my_computer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "14e4447c",
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/pty.py:89: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
      "  pid, fd = os.forkpty()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "config.json  tf_model.h5\r\n"
     ]
    }
   ],
   "source": [
    "ls directory_on_my_computer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59273ff3",
   "metadata": {
    "execution": {
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     "start_time": "2024-04-05T15:24:29.800285",
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    "tags": []
   },
   "source": [
    "# Tokenizers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9429b96",
   "metadata": {
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     "duration": 0.008717,
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     "start_time": "2024-04-05T15:24:29.820854",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Loading and saving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "696816e6",
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   "outputs": [
    {
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      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c84e5daa7496451099f11f974216c7cf",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "tokenizer_config.json:   0%|          | 0.00/49.0 [00:00<?, ?B/s]"
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       "model_id": "6dcdeb4c2c49447ca5ce7d2d08e32600",
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      },
      "text/plain": [
       "vocab.txt:   0%|          | 0.00/213k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "efeaf40169b84aa488c88f1f8e038561",
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       "version_minor": 0
      },
      "text/plain": [
       "tokenizer.json:   0%|          | 0.00/436k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from transformers import BertTokenizer\n",
    "\n",
    "tokenizer = BertTokenizer.from_pretrained(\"bert-base-cased\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8b34475f",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "64bf1b11",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input_ids': [101, 7993, 170, 13809, 23763, 2443, 1110, 3014, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer(\"Using a Transformer network is simple\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "882f7418",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('directory_on_my_computer/tokenizer_config.json',\n",
       " 'directory_on_my_computer/special_tokens_map.json',\n",
       " 'directory_on_my_computer/vocab.txt',\n",
       " 'directory_on_my_computer/added_tokens.json',\n",
       " 'directory_on_my_computer/tokenizer.json')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.save_pretrained(\"directory_on_my_computer\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b9f7968",
   "metadata": {
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     "duration": 0.009611,
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     "start_time": "2024-04-05T15:24:31.328042",
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    "tags": []
   },
   "source": [
    "### Tokenization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "949ece5d",
   "metadata": {
    "execution": {
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Using', 'a', 'Trans', '##former', 'network', 'is', 'simple']\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoTokenizer\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
    "\n",
    "sequence = \"Using a Transformer network is simple\"\n",
    "tokens = tokenizer.tokenize(sequence)\n",
    "\n",
    "print(tokens)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4f9f030",
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    },
    "tags": []
   },
   "source": [
    "### From tokens to input IDs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ceee11dc",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7993, 170, 13809, 23763, 2443, 1110, 3014]\n"
     ]
    }
   ],
   "source": [
    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
    "\n",
    "print(ids)"
   ]
  },
  {
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   "source": [
    "### Decoding"
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      "Using a transformer network is simple\n"
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