Feat: add functionality to download MNLI preprocessed tsv data.

Leverage NYU Jiant Toolkit preprocessed tsv data source

utils_nlp/dataset/multinli.py

@@ -22,6 +22,9 @@ from utils_nlp.models.transformers.common import MAX_SEQ_LEN
 from utils_nlp.models.transformers.sequence_classification import Processor
 
 URL = "http://www.nyu.edu/projects/bowman/multinli/multinli_1.0.zip"
+
+# Source - https://github.com/nyu-mll/jiant/blob/master/scripts/download_glue_data.py
+URL_JIANT_MNLI_TSV = "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FMNLI.zip?alt=media&token=50329ea1-e339-40e2-809c-10c40afff3ce"
 DATA_FILES = {
     "train": "multinli_1.0/multinli_1.0_train.jsonl",
     "dev_matched": "multinli_1.0/multinli_1.0_dev_matched.jsonl",
@@ -29,7 +32,9 @@ DATA_FILES = {
 }
 
 
-def download_file_and_extract(local_cache_path: str = ".", file_split: str = "train") -> None:
+def download_file_and_extract(
+    local_cache_path: str = ".", file_split: str = "train"
+) -> None:
     """Download and extract the dataset files
 
     Args:
@@ -46,6 +51,31 @@ def download_file_and_extract(local_cache_path: str = ".", file_split: str = "tr
         extract_zip(os.path.join(local_cache_path, file_name), local_cache_path)
 
 
+def download_tsv_files_and_extract(local_cache_path: str = ".") -> None:
+    """Download and extract the dataset files in tsv format from NYU Jiant 
+        downloads both original and tsv formatted data. 
+
+    Args:
+        local_cache_path (str [optional]) -- Directory to cache files to. Defaults to current working directory (default: {"."})
+    
+    Returns:
+        None -- Nothing is returned
+    """
+    try:
+        folder_name = "MNLI"
+        file_name = f"{folder_name}.zip"
+        maybe_download(URL_JIANT_MNLI_TSV, file_name, local_cache_path)
+        if not os.path.exists(os.path.join(local_cache_path, folder_name)):
+            extract_zip(os.path.join(local_cache_path, file_name), local_cache_path)
+
+        # Clean up zip download
+        if os.path.exists(os.path.join(local_cache_path, file_name)):
+            os.remove(os.path.join(local_cache_path, file_name))
+    except IOError as e:
+        raise (e)
+    print("Downloaded file to: ", os.path.join(local_cache_path, folder_name))
+
+
 def load_pandas_df(local_cache_path=".", file_split="train"):
     """Loads extracted dataset into pandas
     Args:
@@ -61,10 +91,18 @@ def load_pandas_df(local_cache_path=".", file_split="train"):
         download_file_and_extract(local_cache_path, file_split)
     except Exception as e:
         raise e
-    return pd.read_json(os.path.join(local_cache_path, DATA_FILES[file_split]), lines=True)
+    return pd.read_json(
+        os.path.join(local_cache_path, DATA_FILES[file_split]), lines=True
+    )
 
 
-def get_generator(local_cache_path=".", file_split="train", block_size=10e6, batch_size=10e6, num_batches=None):
+def get_generator(
+    local_cache_path=".",
+    file_split="train",
+    block_size=10e6,
+    batch_size=10e6,
+    num_batches=None,
+):
     """ Returns an extracted dataset as a random batch generator that
     yields pandas dataframes.
     Args:
@@ -84,9 +122,13 @@ def get_generator(local_cache_path=".", file_split="train", block_size=10e6, bat
     except Exception as e:
         raise e
 
-    loader = DaskJSONLoader(os.path.join(local_cache_path, DATA_FILES[file_split]), block_size=block_size)
+    loader = DaskJSONLoader(
+        os.path.join(local_cache_path, DATA_FILES[file_split]), block_size=block_size
+    )
 
-    return loader.get_sequential_batches(batch_size=int(batch_size), num_batches=num_batches)
+    return loader.get_sequential_batches(
+        batch_size=int(batch_size), num_batches=num_batches
+    )
 
 
 def load_tc_dataset(
@@ -161,17 +203,23 @@ def load_tc_dataset(
     label_encoder.fit(all_df[label_col])
 
     if test_fraction < 0 or test_fraction >= 1.0:
-        logging.warning("Invalid test fraction value: {}, changed to 0.25".format(test_fraction))
+        logging.warning(
+            "Invalid test fraction value: {}, changed to 0.25".format(test_fraction)
+        )
         test_fraction = 0.25
 
-    train_df, test_df = train_test_split(all_df, train_size=(1.0 - test_fraction), random_state=random_seed)
+    train_df, test_df = train_test_split(
+        all_df, train_size=(1.0 - test_fraction), random_state=random_seed
+    )
 
     if train_sample_ratio > 1.0:
         train_sample_ratio = 1.0
         logging.warning("Setting the training sample ratio to 1.0")
     elif train_sample_ratio < 0:
         logging.error("Invalid training sample ration: {}".format(train_sample_ratio))
-        raise ValueError("Invalid training sample ration: {}".format(train_sample_ratio))
+        raise ValueError(
+            "Invalid training sample ration: {}".format(train_sample_ratio)
+        )
 
     if test_sample_ratio > 1.0:
         test_sample_ratio = 1.0
@@ -195,12 +243,16 @@ def load_tc_dataset(
     train_dataset = processor.dataset_from_dataframe(
         df=train_df, text_col=text_col, label_col=label_col, max_len=max_len,
     )
-    train_dataloader = dataloader_from_dataset(train_dataset, batch_size=batch_size, num_gpus=num_gpus, shuffle=True)
+    train_dataloader = dataloader_from_dataset(
+        train_dataset, batch_size=batch_size, num_gpus=num_gpus, shuffle=True
+    )
 
     test_dataset = processor.dataset_from_dataframe(
         df=test_df, text_col=text_col, label_col=label_col, max_len=max_len,
     )
-    test_dataloader = dataloader_from_dataset(test_dataset, batch_size=batch_size, num_gpus=num_gpus, shuffle=False)
+    test_dataloader = dataloader_from_dataset(
+        test_dataset, batch_size=batch_size, num_gpus=num_gpus, shuffle=False
+    )
 
     return (train_dataloader, test_dataloader, label_encoder, test_labels)