minor fixes
This commit is contained in:
Said Bleik
Родитель
274615b396
Коммит
51c22b9607
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@ -13,7 +13,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 43,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -21,7 +21,7 @@
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"sys.path.append(\"../../\")\n",
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"import os\n",
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"import pandas as pd\n",
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"from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
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"from sklearn.metrics import classification_report\n",
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"from sklearn.model_selection import train_test_split\n",
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"from utils_nlp.dataset.url_utils import maybe_download, extract_zip\n",
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"from utils_nlp.eval.classification import eval_classification\n",
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@ -45,7 +45,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 48,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -76,8 +76,8 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"zip_file = \"dac.zip\"\n",
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"csv_file = \"arabic_dataset_classifiction.csv\"\n",
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"zip_file = URL.split[\"/\"][-1]\n",
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"csv_file = zip_file.replace(\".zip\", \"\")\n",
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"maybe_download(URL, filename=zip_file, work_directory=DATA_FOLDER)\n",
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"extract_zip(file_path=os.path.join(DATA_FOLDER, zip_file), dest_path=DATA_FOLDER)\n",
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"df = pd.read_csv(os.path.join(DATA_FOLDER, csv_file))"
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@ -344,7 +344,7 @@
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"source": [
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"In addition, we perform the following preprocessing steps in the cell below:\n",
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"- Convert the tokens into token indices corresponding to the BERT tokenizer's vocabulary\n",
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"- Add sentence markers\n",
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"- Add the special tokens [CLS] and [SEP] to mark the beginning and end of a sentence\n",
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"- Pad or truncate the token lists to the specified max length\n",
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"- Return mask lists that indicate paddings' positions\n",
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"\n",
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@ -419,7 +419,7 @@
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"epoch:1/1; batch:1573->1834/2618; loss:0.217612\n",
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"epoch:1/1; batch:1835->2096/2618; loss:0.314651\n",
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"epoch:1/1; batch:2097->2358/2618; loss:0.065314\n",
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"epoch:1/1; batch:2359->2620/2618; loss:0.088071\n",
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"epoch:1/1; batch:2359->2618/2618; loss:0.088071\n",
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"[Training time: 1.414 hrs]\n"
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]
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}
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@ -475,106 +475,30 @@
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},
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{
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"cell_type": "code",
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"execution_count": 41,
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"execution_count": 46,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" precision recall f1-score support\n",
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"\n",
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" accuracy: 0.946656\n"
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" culture 0.91 0.96 0.94 3479\n",
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" diverse 0.92 0.98 0.95 4091\n",
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" economy 0.92 0.85 0.88 3517\n",
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" politics 0.91 0.89 0.90 5222\n",
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" sports 0.99 0.99 0.99 11623\n",
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"\n",
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" accuracy 0.95 27932\n",
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" macro avg 0.93 0.93 0.93 27932\n",
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"weighted avg 0.95 0.95 0.95 27932\n",
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"\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>label</th>\n",
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" <th>precision</th>\n",
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" <th>recall</th>\n",
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" <th>f1</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>culture</td>\n",
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" <td>0.911389</td>\n",
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" <td>0.963783</td>\n",
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" <td>0.936854</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>diverse</td>\n",
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" <td>0.923255</td>\n",
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" <td>0.976289</td>\n",
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" <td>0.949032</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>economy</td>\n",
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" <td>0.918719</td>\n",
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" <td>0.848450</td>\n",
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" <td>0.882188</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>politics</td>\n",
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" <td>0.911776</td>\n",
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" <td>0.886633</td>\n",
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" <td>0.899029</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>sports</td>\n",
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" <td>0.989656</td>\n",
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" <td>0.987783</td>\n",
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" <td>0.988719</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" label precision recall f1\n",
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"0 culture 0.911389 0.963783 0.936854\n",
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"1 diverse 0.923255 0.976289 0.949032\n",
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"2 economy 0.918719 0.848450 0.882188\n",
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"3 politics 0.911776 0.886633 0.899029\n",
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"4 sports 0.989656 0.987783 0.988719"
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]
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},
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"execution_count": 41,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"accuracy = accuracy_score(df_test[label_col], preds)\n",
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"precision = precision_score(df_test[label_col], preds, average=None)\n",
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"recall = recall_score(df_test[label_col], preds, average=None)\n",
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"f1 = f1_score(df_test[label_col], preds, average=None)\n",
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"\n",
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"print(\"\\n accuracy: {:.6f}\".format(accuracy))\n",
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"pd.DataFrame({\"label\": labels, \"precision\": precision, \"recall\": recall, \"f1\": f1})"
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"print(classification_report(df_test[label_col], preds, target_names=labels))"
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]
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}
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],
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@ -46,13 +46,11 @@ class Tokenizer:
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self.language = language
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def tokenize(self, text):
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"""Uses a BERT tokenizer
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"""Tokenizes a list of documents using a BERT tokenizer
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Args:
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text (list): [description]
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text (list(str)): list of text documents.
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Returns:
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[list]: [description]
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[list(str)]: list of token lists.
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"""
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tokens = [self.tokenizer.tokenize(x) for x in text]
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return tokens
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@ -140,7 +140,7 @@ class BERTSequenceClassifier:
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epoch + 1,
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num_epochs,
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i + 1,
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(i + 1 + (num_batches // 10)) % num_batches,
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min(i + 1 + num_batches // 10, num_batches),
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num_batches,
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loss.data,
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)
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@ -7,7 +7,6 @@ https://www.nyu.edu/projects/bowman/multinli/
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import os
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import pandas as pd
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import requests
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from utils_nlp.dataset.url_utils import extract_zip, maybe_download
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URL = "http://www.nyu.edu/projects/bowman/multinli/multinli_1.0.zip"
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@ -19,21 +18,20 @@ DATA_FILES = {
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def load_pandas_df(local_cache_path=None, file_split="train"):
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"""Downloads and extracts the dataset files
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"""Downloads and extracts the dataset files
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Args:
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local_cache_path ([type], optional): [description]. Defaults to None.
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file_split (str, optional): The subset to load.
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One of: {"train", "dev_matched", "dev_mismatched"}
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Defaults to "train".
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One of: {"train", "dev_matched", "dev_mismatched"}
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Defaults to "train".
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Returns:
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pd.DataFrame: pandas DataFrame containing the specified MultiNLI subset.
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pd.DataFrame: pandas DataFrame containing the specified
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MultiNLI subset.
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"""
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file_name = URL.split("/")[-1]
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if not os.path.exists(os.path.join(local_cache_path, file_name)):
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response = requests.get(URL)
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with open(os.path.join(local_cache_path, file_name), "wb") as f:
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f.write(response.content)
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maybe_download(URL, file_name, local_cache_path)
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if not os.path.exists(
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os.path.join(local_cache_path, DATA_FILES[file_split])
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):
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