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"description_width": ""
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}
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}
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}
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},
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"cells": [
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{
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"cell_type": "code",
|
||
"source": [
|
||
"!unzip -q /content/en-ner-conll-2003.zip -d /content/"
|
||
],
|
||
"metadata": {
|
||
"id": "iP3nNcGigcvf"
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||
},
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||
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||
"outputs": []
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||
},
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{
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||
"cell_type": "code",
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||
"source": [
|
||
"import os\n",
|
||
"import pandas as pd\n",
|
||
"from transformers import pipeline, AutoModelForTokenClassification, BertTokenizer"
|
||
],
|
||
"metadata": {
|
||
"id": "tjRmxd_mawQx"
|
||
},
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"execution_count": 2,
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"outputs": []
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||
},
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{
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"cell_type": "markdown",
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"source": [
|
||
"### Declare path"
|
||
],
|
||
"metadata": {
|
||
"id": "S0PTWhFxbgJ-"
|
||
}
|
||
},
|
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{
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||
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|
||
"source": [
|
||
"data_dir_path = 'en-ner-conll-2003'\n",
|
||
"train_path = os.path.join(data_dir_path, 'train', 'train.tsv')\n",
|
||
"dev_texts_path = os.path.join(data_dir_path, 'dev-0', 'in.tsv')\n",
|
||
"dev_labels_path = os.path.join(data_dir_path, 'dev-0', 'expected.tsv')\n",
|
||
"dev_predicted_path = os.path.join(data_dir_path, 'dev-0', 'out.tsv')\n",
|
||
"test_texts_path = os.path.join(data_dir_path, 'test-A', 'in.tsv')\n",
|
||
"test_predicted_path = os.path.join(data_dir_path, 'test-A', 'out.tsv')"
|
||
],
|
||
"metadata": {
|
||
"id": "hB0tKt3ubewl"
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||
},
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||
"execution_count": 3,
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||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"### Load files"
|
||
],
|
||
"metadata": {
|
||
"id": "T9WieEzabiGc"
|
||
}
|
||
},
|
||
{
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||
"cell_type": "code",
|
||
"source": [
|
||
"train_data = pd.read_csv(train_path, sep='\\t', usecols=[0, 1], header=None, names=['label', 'text'])\n",
|
||
"dev_texts_data = pd.read_csv(dev_texts_path, sep='\\t', usecols=[0], header=None, names=['text'])\n",
|
||
"dev_labels_data = pd.read_csv(dev_labels_path, sep='\\t', usecols=[0], header=None, names=['label'])\n",
|
||
"test_texts_data = pd.read_csv(test_texts_path, sep='\\t', usecols=[0], header=None, names=['text'])"
|
||
],
|
||
"metadata": {
|
||
"id": "vyiLjdQ7bkMf"
|
||
},
|
||
"execution_count": 4,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"### Create transformer and tokenizer pipeline"
|
||
],
|
||
"metadata": {
|
||
"id": "JGNePvf7bmEI"
|
||
}
|
||
},
|
||
{
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||
"cell_type": "code",
|
||
"source": [
|
||
"model = AutoModelForTokenClassification.from_pretrained(\"dbmdz/bert-large-cased-finetuned-conll03-english\")\n",
|
||
"\n",
|
||
"class SpaceTokenizer(BertTokenizer):\n",
|
||
" def tokenize(self, text):\n",
|
||
" return text.split()\n",
|
||
"\n",
|
||
"tokenizer = SpaceTokenizer.from_pretrained(\"bert-base-cased\")\n",
|
||
"\n",
|
||
"recognizer = pipeline(\"ner\", model=model, tokenizer=tokenizer)"
|
||
],
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||
"metadata": {
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"outputs": [
|
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{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
|
||
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
|
||
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
|
||
"You will be able to reuse this secret in all of your notebooks.\n",
|
||
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
|
||
" warnings.warn(\n"
|
||
]
|
||
},
|
||
{
|
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"output_type": "display_data",
|
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"data": {
|
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|
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"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"Some weights of the model checkpoint at dbmdz/bert-large-cased-finetuned-conll03-english were not used when initializing BertForTokenClassification: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight']\n",
|
||
"- This IS expected if you are initializing BertForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
|
||
"- This IS NOT expected if you are initializing BertForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
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|
||
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|
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],
|
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|
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|
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},
|
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{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
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|
||
"vocab.txt: 0%| | 0.00/213k [00:00<?, ?B/s]"
|
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],
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|
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|
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|
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|
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{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"tokenizer.json: 0%| | 0.00/436k [00:00<?, ?B/s]"
|
||
],
|
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"metadata": {}
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||
},
|
||
{
|
||
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|
||
"name": "stderr",
|
||
"text": [
|
||
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
|
||
" warnings.warn(\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"config.json: 0%| | 0.00/570 [00:00<?, ?B/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "57b40d2d262842af820c49c4258b73e7"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization. \n",
|
||
"The tokenizer class you load from this checkpoint is 'BertTokenizer'. \n",
|
||
"The class this function is called from is 'SpaceTokenizer'.\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"### Predict and save results"
|
||
],
|
||
"metadata": {
|
||
"id": "AzrJlbUNjNVX"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"def predict(X):\n",
|
||
" predictions = list()\n",
|
||
" for text in X:\n",
|
||
" predictions.append(recognizer(text))\n",
|
||
" return predictions"
|
||
],
|
||
"metadata": {
|
||
"id": "ytccBfKEa5Y9"
|
||
},
|
||
"execution_count": 6,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"def map_predictions(X, predictions):\n",
|
||
" results = list()\n",
|
||
" for text, prediction in zip(X, predictions):\n",
|
||
" result = ['O'] * len(text.split())\n",
|
||
" for prediction_element in prediction:\n",
|
||
" result[prediction_element['index']-1] = prediction_element['entity']\n",
|
||
" result = \" \".join(result)\n",
|
||
" results.append(result)\n",
|
||
" return results"
|
||
],
|
||
"metadata": {
|
||
"id": "Iemt7zqydPns"
|
||
},
|
||
"execution_count": 7,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"def predict_and_save(X, filename):\n",
|
||
" X = X['text']\n",
|
||
" predictions = predict(X)\n",
|
||
" Y_predicted = map_predictions(X, predictions)\n",
|
||
" Y_predicted_df = pd.DataFrame(Y_predicted, columns=['predicted_label'])\n",
|
||
" Y_predicted_df.to_csv(filename, sep='\\t', index=False, header=None)"
|
||
],
|
||
"metadata": {
|
||
"id": "52sw1t-thZXb"
|
||
},
|
||
"execution_count": 8,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"dev_predicted = predict_and_save(dev_texts_data, dev_predicted_path)"
|
||
],
|
||
"metadata": {
|
||
"id": "Pgox00dGeV5t"
|
||
},
|
||
"execution_count": 9,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"test_predicted = predict_and_save(test_texts_data, test_predicted_path)"
|
||
],
|
||
"metadata": {
|
||
"id": "CUA-gcUKpNwg"
|
||
},
|
||
"execution_count": 10,
|
||
"outputs": []
|
||
}
|
||
]
|
||
} |