1-processing-the-data.ipynb

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   "source": [
    "# Processing the data"
   ]
  },
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     "text": [
      "2024-04-06 02:07:03.934450: 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",
      "2024-04-06 02:07:03.934601: 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",
      "2024-04-06 02:07:04.097578: 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"
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      "text/plain": [
       "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
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       "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
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      "text/plain": [
       "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
      ]
     },
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       "model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
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    {
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     "output_type": "stream",
     "text": [
      "All PyTorch model weights were used when initializing TFBertForSequenceClassification.\n",
      "\n",
      "Some weights or buffers of the TF 2.0 model TFBertForSequenceClassification were not initialized from the PyTorch model and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: AutoGraph could not transform <function infer_framework at 0x7a168eed6cb0> and will run it as-is.\n",
      "Cause: for/else statement not yet supported\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n"
     ]
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       "0.13526639342308044"
      ]
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     "execution_count": 1,
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   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
    "\n",
    "# Same as before\n",
    "checkpoint = \"bert-base-uncased\"\n",
    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
    "sequences = [\n",
    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
    "    \"This course is amazing!\",\n",
    "]\n",
    "batch = dict(tokenizer(sequences, padding=True, truncation=True, return_tensors=\"tf\"))\n",
    "\n",
    "# This is new\n",
    "model.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\")\n",
    "labels = tf.convert_to_tensor([1, 1])\n",
    "model.train_on_batch(batch, labels)"
   ]
  },
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    {
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      "text/plain": [
       "Downloading builder script:   0%|          | 0.00/7.78k [00:00<?, ?B/s]"
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       "Downloading metadata:   0%|          | 0.00/4.47k [00:00<?, ?B/s]"
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    {
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     "text": [
      "Downloading and preparing dataset glue/mrpc (download: 1.43 MiB, generated: 1.43 MiB, post-processed: Unknown size, total: 2.85 MiB) to /root/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad...\n"
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       "version_minor": 0
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      "text/plain": [
       "Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
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      "text/plain": [
       "Downloading data: 0.00B [00:00, ?B/s]"
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       "Downloading data: 0.00B [00:00, ?B/s]"
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       "model_id": "02c830551534416db8e0e63e82968067",
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       "Downloading data: 0.00B [00:00, ?B/s]"
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      },
      "text/plain": [
       "Generating train split:   0%|          | 0/3668 [00:00<?, ? examples/s]"
      ]
     },
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    {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating validation split:   0%|          | 0/408 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "5d5e3e9b8eee462b8cd66abfd533ac61",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "Generating test split:   0%|          | 0/1725 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset glue downloaded and prepared to /root/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad. Subsequent calls will reuse this data.\n"
     ]
    },
    {
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       "model_id": "899369e7955e474ebe0720fa99d778b8",
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      },
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     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
       "        num_rows: 3668\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
       "        num_rows: 408\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
       "        num_rows: 1725\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
    "raw_datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2f90fe68",
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    "tags": []
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
       "    num_rows: 3668\n",
       "})"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_datasets[\"train\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "816e1548",
   "metadata": {
    "execution": {
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    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sentence1': 'Revenue in the first quarter of the year dropped 15 percent from the same period a year earlier .',\n",
       " 'sentence2': \"With the scandal hanging over Stewart 's company , revenue the first quarter of the year dropped 15 percent from the same period a year earlier .\",\n",
       " 'label': 1,\n",
       " 'idx': 5}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_train_dataset = raw_datasets[\"train\"]\n",
    "raw_train_dataset[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "675f1cd6",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sentence1': Value(dtype='string', id=None),\n",
       " 'sentence2': Value(dtype='string', id=None),\n",
       " 'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], id=None),\n",
       " 'idx': Value(dtype='int32', id=None)}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_train_dataset.features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "954ce0ba",
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    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3668"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(raw_datasets[\"train\"][\"sentence1\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b5cf0892",
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    "tags": []
   },
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer\n",
    "\n",
    "checkpoint = \"bert-base-uncased\"\n",
    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
    "tokenized_sentences_1 = tokenizer(raw_datasets[\"train\"][\"sentence1\"])\n",
    "tokenized_sentences_2 = tokenizer(raw_datasets[\"train\"][\"sentence2\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "729bbc99",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input_ids': [101, 2023, 2003, 1996, 2034, 6251, 1012, 102, 2023, 2003, 1996, 2117, 2028, 1012, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs = tokenizer(\"This is the first sentence.\", \"This is the second one.\")\n",
    "inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9eed7484",
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    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['[CLS]',\n",
       " 'this',\n",
       " 'is',\n",
       " 'the',\n",
       " 'first',\n",
       " 'sentence',\n",
       " '.',\n",
       " '[SEP]',\n",
       " 'this',\n",
       " 'is',\n",
       " 'the',\n",
       " 'second',\n",
       " 'one',\n",
       " '.',\n",
       " '[SEP]']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"])"
   ]
  },
  {
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   "execution_count": null,
   "id": "369a19d7",
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