add more transformers to toolkit

datasets/Melbourne_Airbnb_Open_Data/column_info_all_text.json

@@ -0,0 +1,99 @@
+{
+  "text_cols": [
+    "name",
+    "summary",
+    "host_about",
+    "host_location",
+    "host_is_superhost",
+    "host_neighborhood",
+    "host_identity_verified",
+    "neighborhood",
+    "city",
+    "suburb",
+    "state",
+    "property_type",
+    "room_type",
+    "bed_type",
+    "cancellation_policy",
+    "instant_bookable",
+    "internet",
+    "wifi",
+    "pets_live_on_this_property",
+    "dog(s)",
+    "tv",
+    "air_conditioning",
+    "wifi",
+    "kitchen",
+    "heating",
+    "family/kid_friendly",
+    "washer",
+    "smoke_detector",
+    "first_aid_kit",
+    "fire_extinguisher",
+    "essentials",
+    "shampoo",
+    "lock_on_bedroom_door",
+    "hair_dryer",
+    "iron",
+    "laptop_friendly_workspace",
+    "private_entrance",
+    "hot_water",
+    "microwave",
+    "coffee_maker",
+    "refrigerator",
+    "dishes_and_silverware",
+    "cooking_basics",
+    "oven",
+    "stove",
+    "garden_or_backyard",
+    "luggage_dropoff_allowed",
+    "long_term_stays_allowed",
+    "free_parking_on_premises",
+    "elevator",
+    "buzzer/wireless_intercom",
+    "dryer",
+    "hangers",
+    "self_check_in",
+    "lockbox",
+    "bed_linens",
+    "extra_pillows_and_blankets",
+    "dishwasher",
+    "patio_or_balcony",
+    "breakfast",
+    "pool",
+    "bathtub",
+    "host_greets_you",
+    "smoking_allowed",
+    "gym",
+    "room_darkening_shades",
+    "single_level_home",
+    "bbq_grill",
+    "step_free_access",
+    "flat_path_to_front_door",
+    "well_lit_path_to_entrance",
+    "paid_parking_on_premises",
+    "free_street_parking",
+    "carbon_monoxide_detector",
+    "private_living_room",
+    "cable_tv",
+    "step_free_access",
+    "accommodates",
+    "bathrooms",
+    "beds",
+    "security_deposit",
+    "cleaning_fee",
+    "guests_included",
+    "extra_people",
+    "minimum_nights",
+    "availability_30",
+    "availability_60",
+    "availability_90",
+    "availability_365",
+    "number_of_reviews",
+    "review_scores_rating",
+    "reviews_per_month"
+  ],
+  "label_col": "price",
+  "text_col_sep_token": " ",
+  "label_list": []
+}

datasets/Womens_Clothing_E-Commerce_Reviews/column_info.json

@@ -4,7 +4,6 @@
     "Review Text"
   ],
   "cat_cols": [
-    "Clothing ID",
     "Division Name",
     "Department Name",
     "Class Name"

datasets/Womens_Clothing_E-Commerce_Reviews/column_info_all_text.json

@@ -0,0 +1,15 @@
+{
+  "text_cols": [
+    "Title",
+    "Review Text",
+    "Division Name",
+    "Department Name",
+    "Class Name",
+    "Rating",
+    "Age",
+    "Positive Feedback Count"
+  ],
+  "text_col_sep_token": " ",
+  "label_col": "Recommended IND",
+  "label_list": ["Not Recommended", "Recommended"]
+}

main.py

@@ -76,7 +76,7 @@ def main():
         numerical_cols=data_args.column_info['num_cols'],
         categorical_encode_type=data_args.categorical_encode_type,
         numerical_transformer_method=data_args.numerical_transformer_method,
-        sep_text_token_str=tokenizer.sep_token,
+        sep_text_token_str=tokenizer.sep_token if not data_args.column_info['text_col_sep_token'] else data_args.column_info['text_col_sep_token'],
         max_token_length=training_args.max_token_length,
         debug=training_args.debug_dataset,
     )
@@ -93,8 +93,8 @@ def main():
         cache_dir=model_args.cache_dir,
     )
     tabular_config = TabularConfig(num_labels=num_labels,
-                                   cat_feat_dim=train_dataset.cat_feats.shape[1],
-                                   numerical_feat_dim=train_dataset.numerical_feats.shape[1],
+                                   cat_feat_dim=train_dataset.cat_feats.shape[1] if train_dataset.cat_feats is not None else 0,
+                                   numerical_feat_dim=train_dataset.numerical_feats.shape[1] if train_dataset.numerical_feats is not None else 0,
                                    **vars(data_args))
     config.tabular_config = tabular_config
     logger.info(tabular_config)

multimodal_exp_args.py

@@ -110,7 +110,15 @@ def __post_init__(self):
         if self.column_info is None and self.column_info_path:
             with open(self.column_info_path, 'r') as f:
                 self.column_info = json.load(f)
-
+            assert 'text_cols' in self.column_info and 'label_col' in self.column_info
+            if 'cat_cols' not in self.column_info:
+                self.column_info['cat_cols'] = None
+                self.categorical_encode_type = 'none'
+            if 'num_cols' not in self.column_info:
+                self.column_info['num_cols'] = None
+                self.numerical_transformer_method = 'none'
+            if 'text_col_sep_token' not in self.column_info:
+                self.column_info['text_col_sep_token'] = None
 
 @dataclass
 class OurTrainingArguments(TrainingArguments):

multimodal_transformers/model/tabular_modeling_auto.py

@@ -3,20 +3,35 @@
 from transformers.configuration_utils import PretrainedConfig
 from transformers.configuration_auto import (
     AutoConfig,
+    AlbertConfig,
     BertConfig,
     DistilBertConfig,
     RobertaConfig,
-
+    XLNetConfig,
+    XLMConfig,
+    XLMRobertaConfig
 )
 
-from .tabular_transformers import RobertaWithTabular, BertWithTabular, DistilBertWithTabular
+from .tabular_transformers import (
+    RobertaWithTabular,
+    BertWithTabular,
+    DistilBertWithTabular,
+    AlbertWithTabular,
+    XLNetWithTabular,
+    XLMWithTabular,
+    XLMRobertaWithTabular
+)
 
 
 MODEL_FOR_SEQUENCE_W_TABULAR_CLASSIFICATION_MAPPING = OrderedDict(
     [
         (RobertaConfig, RobertaWithTabular),
         (BertConfig, BertWithTabular),
-        (DistilBertConfig, DistilBertWithTabular)
+        (DistilBertConfig, DistilBertWithTabular),
+        (AlbertConfig, AlbertWithTabular),
+        (XLNetConfig, XLNetWithTabular),
+        (XLMConfig, XLMWithTabular),
+        (XLMRobertaConfig, XLMRobertaWithTabular)
     ]
 )