solved all conflicts with master

.gitignore

@@ -17,9 +17,10 @@ Untitled*.ipynb
 
 # data related dirs
 tmp_data/
-model_weights/
 tmp_dir/
 weights/
+pretrained_weights/
+model_weights/
 
 # Unit Tests/Coverage
 .coverage

README.md

@@ -24,6 +24,8 @@ text and images using Wide and Deep models in Pytorch
 
 **Experiments and comparison with `LightGBM`**: [TabularDL vs LightGBM](https://github.com/jrzaurin/tabulardl-benchmark)
 
+**Slack**: if you want to contribute or just want to chat with us, join [slack](https://join.slack.com/t/pytorch-widedeep/shared_invite/zt-soss7stf-iXpVuLeKZz8lGTnxxtHtTw)
+
 The content of this document is organized as follows:
 
 1. [introduction](#introduction)
@@ -142,9 +144,12 @@ Note that while there are scientific publications for the TabTransformer,
 SAINT and FT-Transformer, the TabFasfFormer and TabPerceiver are our own
 adaptation of those algorithms for tabular data.
 
-For details on these models (and all the other models in the library for the
-different data modes) and their corresponding options please see the examples
-in the Examples folder and the documentation.
+In addition, Self-Supervised pre-training can be used for all `deeptabular`
+models, with the exception of the `TabPerceiver`. Self-Supervised
+pre-training can be used via two methods or routines which we refer as:
+encoder-decoder method and constrastive-denoising method. Please, see the
+documentation and the examples for details on this functionality, and all
+other options in the library.
 
 ###  Installation
 

VERSION

@@ -1 +1 @@
-1.1.2
+1.2.0

examples/notebooks/01_Preprocessors_and_utils.ipynb

@@ -702,7 +702,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████████████████████████████████████████████████████████████████████████████████████████| 1001/1001 [00:01<00:00, 601.98it/s]\n"
+      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 1001/1001 [00:01<00:00, 601.56it/s]\n"
      ]
     },
     {

examples/notebooks/02_model_components.ipynb

@@ -180,7 +180,7 @@
     {
      "data": {
       "text/plain": [
-       "tensor([-0.1975], grad_fn=<AddBackward0>)"
+       "tensor([-0.3839], grad_fn=<AddBackward0>)"
       ]
      },
      "execution_count": 7,
@@ -200,7 +200,7 @@
     {
      "data": {
       "text/plain": [
-       "tensor([-0.1975], grad_fn=<AddBackward0>)"
+       "tensor([-0.3839], grad_fn=<AddBackward0>)"
       ]
      },
      "execution_count": 8,
@@ -323,7 +323,7 @@
        "    )\n",
        "    (cont_norm): BatchNorm1d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
        "  )\n",
-       "  (tab_mlp): MLP(\n",
+       "  (encoder): MLP(\n",
        "    (mlp): Sequential(\n",
        "      (dense_layer_0): Sequential(\n",
        "        (0): Dropout(p=0.1, inplace=False)\n",
@@ -474,7 +474,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "resnet = Vision(pretrained_model_name=\"resnet18\", n_trainable=0)"
+    "resnet = Vision(pretrained_model_setup=\"resnet18\", n_trainable=0)"
    ]
   },
   {

examples/notebooks/04_regression_with_images_and_text.ipynb

@@ -421,7 +421,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████████████████████████████████████████████████████████████████████████████████████████| 1001/1001 [00:01<00:00, 586.65it/s]\n"
+      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 1001/1001 [00:01<00:00, 514.73it/s]\n"
      ]
     },
     {
@@ -473,7 +473,7 @@
     ")\n",
     "\n",
     "# Pretrained Resnet 18\n",
-    "resnet = Vision(pretrained_model_name=\"resnet18\", n_trainable=4)"
+    "resnet = Vision(pretrained_model_setup=\"resnet18\", n_trainable=4)"
    ]
   },
   {
@@ -523,8 +523,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "epoch 1: 100%|████████████████████████████████████████████████████████████████████████████████| 25/25 [00:53<00:00,  2.13s/it, loss=132]\n",
-      "valid: 100%|████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:11<00:00,  1.63s/it, loss=122]\n"
+      "epoch 1: 100%|███████████████████████████████████████████████████████████████████████████████████████| 25/25 [00:23<00:00,  1.09it/s, loss=132]\n",
+      "valid: 100%|███████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:05<00:00,  1.28it/s, loss=122]\n"
      ]
     }
    ],
@@ -672,15 +672,22 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "epoch 1: 100%|████████████████████████████████████████████████████████████████████████████████| 25/25 [00:52<00:00,  2.12s/it, loss=112]\n",
-      "valid: 100%|███████████████████████████████████████████████████████████████████████████████████| 7/7 [00:10<00:00,  1.56s/it, loss=94.8]"
+      "epoch 1: 100%|███████████████████████████████████████████████████████████████████████████████████████| 25/25 [00:28<00:00,  1.13s/it, loss=107]\n",
+      "valid: 100%|██████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:06<00:00,  1.13it/s, loss=93.5]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Model weights after training corresponds to the those of the final epoch which might not be the best performing weights. Usethe 'ModelCheckpoint' Callback to restore the best epoch weights.\n"
+      "Model weights after training corresponds to the those of the final epoch which might not be the best performing weights. Use the 'ModelCheckpoint' Callback to restore the best epoch weights.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
      ]
     },
     {