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nlp-recipes
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minor updates to seq classification

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Said Bleik 2019-06-03 10:03:49 -04:00
Родитель 61b66a57aa
Коммит ee9134d96f
4 изменённых файлов: 221 добавлений и 31 удалений
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242
scenarios/text_classification/tc_yahoo_answers_bert.ipynb
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@ -13,7 +13,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@ -34,20 +34,20 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"DATA_FOLDER = \"../../../.../temp\"\n",
"DATA_FOLDER = \"./temp\"\n",
"TRAIN_FILE = \"yahoo_answers_csv/train.csv\"\n",
"TEST_FILE = \"yahoo_answers_csv/test.csv\"\n",
"BERT_CACHE_DIR = \"../../../temp\"\n",
"MAX_LEN = 300\n",
"BERT_CACHE_DIR = \"./temp\"\n",
"MAX_LEN = 250\n",
"BATCH_SIZE = 16\n",
"USE_GPU = True\n",
"NUM_GPUS = 2\n",
"NUM_EPOCHS = 1\n",
"NUM_ROWS_TRAIN = 10000 # number of training examples to read\n",
"NUM_ROWS_TEST = 10000 # number of test examples to read"
"NUM_ROWS_TRAIN = 50000 # number of training examples to read\n",
"NUM_ROWS_TEST = 20000 # number of test examples to read"
]
},
{
@ -59,10 +59,12 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"if not os.path.exists(DATA_FOLDER):\n",
" os.mkdir(DATA_FOLDER)\n",
"ya_dataset.download(DATA_FOLDER)"
]
},
@ -75,7 +77,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@ -105,19 +107,44 @@
"## Tokenize and Preprocess"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Before training, we tokenize the text documents and convert them to lists of tokens. The following steps instantiate a BERT tokenizer given the language, and tokenize the text of the training and test sets."
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"tokenizer = Tokenizer(Language.ENGLISH, to_lower=False, cache_dir=BERT_CACHE_DIR)\n",
"tokenizer = Tokenizer(Language.ENGLISH, to_lower=True, cache_dir=BERT_CACHE_DIR)\n",
"\n",
"# tokenize\n",
"tokens_train = tokenizer.tokenize(text_train)\n",
"tokens_test = tokenizer.tokenize(text_test)\n",
"tokens_test = tokenizer.tokenize(text_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In addition, we perform the following preprocessing steps in the cell below:\n",
"- Convert the tokens into token indices corresponding to the BERT tokenizer's vocabulary\n",
"- Add sentence markers\n",
"- Pad or truncate the token lists to the specified max length\n",
"\n",
"# get BERT-format tokens (padded and truncated)\n",
"*See the original [implementation](https://github.com/google-research/bert/blob/master/run_classifier.py) for more information on BERT's input format.*"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"tokens_train, mask_train = tokenizer.preprocess_classification_tokens(\n",
" tokens_train, MAX_LEN\n",
")\n",
@ -135,7 +162,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
@ -153,9 +180,36 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"execution_count": 8,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"t_total value of -1 results in schedule not being applied\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch:1/1; batch:1->313/3125; loss:2.469508\n",
"epoch:1/1; batch:314->626/3125; loss:1.179081\n",
"epoch:1/1; batch:627->939/3125; loss:0.677443\n",
"epoch:1/1; batch:940->1252/3125; loss:1.689727\n",
"epoch:1/1; batch:1253->1565/3125; loss:0.781167\n",
"epoch:1/1; batch:1566->1878/3125; loss:1.036024\n",
"epoch:1/1; batch:1879->2191/3125; loss:0.909294\n",
"epoch:1/1; batch:2192->2504/3125; loss:0.441344\n",
"epoch:1/1; batch:2505->2817/3125; loss:0.823389\n",
"epoch:1/1; batch:2818->3130/3125; loss:1.036229\n",
"[Training time: 1.132 hrs]\n"
]
}
],
"source": [
"# train\n",
"with Timer() as t:\n",
@ -163,12 +217,11 @@
" token_ids=tokens_train,\n",
" input_mask=mask_train,\n",
" labels=labels_train, \n",
" use_gpu=USE_GPU, \n",
" num_gpus=NUM_GPUS, \n",
" num_epochs=NUM_EPOCHS,\n",
" batch_size=BATCH_SIZE, \n",
" verbose=True,\n",
" )\n",
" \n",
" ) \n",
"print(\"[Training time: {:.3f} hrs]\".format(t.interval / 3600))"
]
},
@ -181,12 +234,20 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 9,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 20000/20000 [08:00<00:00, 41.85it/s]\n"
]
}
],
"source": [
"preds = classifier.predict(\n",
" token_ids=tokens_test, input_mask=mask_test, use_gpu=False, batch_size=BATCH_SIZE\n",
" token_ids=tokens_test, input_mask=mask_test, num_gpus=NUM_GPUS, batch_size=BATCH_SIZE\n",
")"
]
},
@ -194,16 +255,134 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluate Results"
"## Evaluate Results\n",
"Finally, we compute the accuracy, precision, recall, and F1 metrics of the evaluation on the test set."
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 10,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" accuracy: 0.6564\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>precision</th>\n",
" <th>recall</th>\n",
" <th>f1</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.592506</td>\n",
" <td>0.497053</td>\n",
" <td>0.540598</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.749070</td>\n",
" <td>0.673518</td>\n",
" <td>0.709288</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.789308</td>\n",
" <td>0.680955</td>\n",
" <td>0.731139</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.561592</td>\n",
" <td>0.440535</td>\n",
" <td>0.493752</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.854772</td>\n",
" <td>0.789272</td>\n",
" <td>0.820717</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.885998</td>\n",
" <td>0.847659</td>\n",
" <td>0.866404</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0.425440</td>\n",
" <td>0.687416</td>\n",
" <td>0.525592</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0.756364</td>\n",
" <td>0.700337</td>\n",
" <td>0.727273</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0.826006</td>\n",
" <td>0.485432</td>\n",
" <td>0.611496</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0.756186</td>\n",
" <td>0.731039</td>\n",
" <td>0.743400</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" precision recall f1\n",
"0 0.592506 0.497053 0.540598\n",
"1 0.749070 0.673518 0.709288\n",
"2 0.789308 0.680955 0.731139\n",
"3 0.561592 0.440535 0.493752\n",
"4 0.854772 0.789272 0.820717\n",
"5 0.885998 0.847659 0.866404\n",
"6 0.425440 0.687416 0.525592\n",
"7 0.756364 0.700337 0.727273\n",
"8 0.826006 0.485432 0.611496\n",
"9 0.756186 0.731039 0.743400"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# eval metrics\n",
"accuracy = accuracy_score(labels_test, preds)\n",
"precision = precision_score(labels_test, preds, average=None)\n",
"recall = recall_score(labels_test, preds, average=None)\n",
@ -212,6 +391,13 @@
"print(\"\\n accuracy: {}\".format(accuracy))\n",
"pd.DataFrame({\"precision\": precision, \"recall\": recall, \"f1\": f1})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {

6
utils_nlp/bert/sequence_classification.py
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@ -115,6 +115,7 @@ class SequenceClassifier:
)
opt.zero_grad()
y_h = self.model(
input_ids=x_batch,
token_type_ids=None,
@ -128,7 +129,7 @@ class SequenceClassifier:
if verbose:
if i % ((num_batches // 10) + 1) == 0:
print(
"epoch:{}/{}; batch:{}->{}/{}; loss:{}".format(
"epoch:{}/{}; batch:{}->{}/{}; loss:{:.6f}".format(
epoch + 1,
num_epochs,
i + 1,
@ -137,6 +138,9 @@ class SequenceClassifier:
loss.data,
)
)
# empty cache
del [x_batch, y_batch, mask_batch]
torch.cuda.empty_cache()
def predict(self, token_ids, input_mask, num_gpus=1, batch_size=32):
"""Scores the given dataset and returns the predicted classes.

2
utils_nlp/dataset/yahoo_answers.py
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@ -15,7 +15,7 @@ def download(dir_path):
"""Downloads and extracts the dataset files"""
file_name = URL.split("/")[-1]
maybe_download(URL, file_name, dir_path)
extract_tar(os.path.join(dir_path, file_name))
extract_tar(os.path.join(dir_path, file_name), dir_path)
def read_data(data_file, nrows=None):

2
utils_nlp/pytorch/device_utils.py
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@ -11,7 +11,7 @@ def get_device(device="gpu"):
Args:
device (str, optional): Device string: "cpu" or "gpu". Defaults to "gpu".
Returns:
torch.device: A PyTorch device: cpu or gpu.
torch.device: A PyTorch device (cpu or gpu).
"""
if device == "gpu":
if torch.cuda.is_available():