first commit

.gitignore

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+.ipynb_checkpoints
+
+
+__pycache__/

README.md

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-# multi-label-sentiment-classifier
-How to build a multi-label sentiment classifiers with Tez and PyTorch
+### Training a Multi-Label Emotion Classifier with Tez and PyTorch
+
+If you're tired of rewriting the same boilerplate code of your training pipelines in PyTorch, I've found a pretty neat solution that could make your life easier. Don't worry, it's not a heavy library that'll change your way of doing things.
+
+It's rather a lightweight wrapper that encapsulates your training logic in a single class. It's built on top of PyTorch, it's quite recent but I've tested it and I think it does what it promises so far.
+
+It's called Tez and we'll see it today in action on a fun multi-label text classification problem. Let's jump right in.
+
+![](./images/goemotions.png)

labels.py

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+_CLASS_NAMES = [
+    "admiration",
+    "amusement",
+    "anger",
+    "annoyance",
+    "approval",
+    "caring",
+    "confusion",
+    "curiosity",
+    "desire",
+    "disappointment",
+    "disapproval",
+    "disgust",
+    "embarrassment",
+    "excitement",
+    "fear",
+    "gratitude",
+    "grief",
+    "joy",
+    "love",
+    "nervousness",
+    "optimism",
+    "pride",
+    "realization",
+    "relief",
+    "remorse",
+    "sadness",
+    "surprise",
+    "neutral",
+]
+
+mapping = dict(zip(range(len(_CLASS_NAMES)),_CLASS_NAMES))