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"source": [ | |
"from transformers import DistilBertTokenizerFast\n", | |
"tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-cased')\n", | |
"train_encodings = tokenizer(train_texts, is_split_into_words=True, return_offsets_mapping=True, padding=True, truncation=True)\n", | |
"val_encodings = tokenizer(val_texts, is_split_into_words=True, return_offsets_mapping=True, padding=True, truncation=True)" | |
] | |
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"import numpy as np\n", | |
"\n", | |
"def encode_tags(tags, encodings):\n", | |
" labels = [[tag2id[tag] for tag in doc] for doc in tags]\n", | |
" encoded_labels = []\n", | |
" for doc_labels, doc_offset in zip(labels, encodings.offset_mapping):\n", | |
" # create an empty array of -100\n", | |
" doc_enc_labels = np.ones(len(doc_offset),dtype=int) * -100\n", | |
" arr_offset = np.array(doc_offset)\n", | |
"\n", | |
" # set labels whose first offset position is 0 and the second is not 0\n", | |
" doc_enc_labels[(arr_offset[:,0] == 0) & (arr_offset[:,1] != 0)] = doc_labels\n", | |
" encoded_labels.append(doc_enc_labels.tolist())\n", | |
"\n", | |
" return encoded_labels\n", | |
"\n", | |
"train_labels = encode_tags(train_tags, train_encodings)\n", | |
"val_labels = encode_tags(val_tags, val_encodings)\n" | |
] | |
}, | |
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"execution_count": 16, | |
"metadata": { | |
"id": "ZBMk6jHSWbLp" | |
}, | |
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"source": [ | |
"import torch\n", | |
"\n", | |
"class TAGTOGataset(torch.utils.data.Dataset):\n", | |
" def __init__(self, encodings, labels):\n", | |
" self.encodings = encodings\n", | |
" self.labels = labels\n", | |
"\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[idx])\n", | |
" return item\n", | |
"\n", | |
" def __len__(self):\n", | |
" return len(self.labels)\n", | |
"\n", | |
"train_encodings.pop(\"offset_mapping\") # we don't want to pass this to the model\n", | |
"val_encodings.pop(\"offset_mapping\")\n", | |
"train_dataset = TAGTOGataset(train_encodings, train_labels)\n", | |
"val_dataset = TAGTOGataset(val_encodings, val_labels)" | |
] | |
} | |
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