added model

README.md

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+---
+language: en
+tags:
+- text-classification
+- pytorch
+- roberta
+- emotions
+- multi-class-classification
+- multi-label-classification
+datasets:
+- go_emotions
+license: mit
+widget:
+- text: "I am not having a great day."
+---
+
+Model trained from [roberta-base](https://huggingface.co/roberta-base) on the [go_emotions](https://huggingface.co/datasets/go_emotions) dataset for multi-label classification.
+
+[go_emotions](https://huggingface.co/datasets/go_emotions) is based on Reddit data and has 28 labels. It is a multi-label dataset where one or multiple labels may apply for any given input text, hence this model is a multi-label classification model with 28 'probability' float outputs for any given input text. Typically a threshold of 0.5 is applied to the probabilities for the prediction for each label.
+
+The model was trained using `AutoModelForSequenceClassification.from_pretrained` with `problem_type="multi_label_classification"` for 3 epochs with a learning rate of 2e-5 and weight decay of 0.01.
+
+Evaluation (of the 28 dim output via a threshold of 0.5 to binarize each) using the dataset test split gives:
+- Micro F1 0.585
+- ROC AUC 0.751
+- Accuracy 0.474
+
+But the metrics would be more meaningful when measured per label given the multi-label nature.
+
+Additionally some labels (E.g. `gratitude`) when considered independently perform very strongly with F1 around 0.9, whilst others (E.g. `relief`) perform very poorly. This is a challenging dataset. Labels such as `relief` do have much fewer examples in the training data (less than 100 out of the 40k+), but there is also some ambiguity and/or labelling errors visible in the training data of `go_emotions` that is suspected to constrain the performance.

config.json

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+{
+  "_name_or_path": "roberta-base",
+  "architectures": [
+    "RobertaForSequenceClassification"
+  ],
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+  "id2label": {
+    "0": "admiration",
+    "1": "amusement",
+    "2": "anger",
+    "3": "annoyance",
+    "4": "approval",
+    "5": "caring",
+    "6": "confusion",
+    "7": "curiosity",
+    "8": "desire",
+    "9": "disappointment",
+    "10": "disapproval",
+    "11": "disgust",
+    "12": "embarrassment",
+    "13": "excitement",
+    "14": "fear",
+    "15": "gratitude",
+    "16": "grief",
+    "17": "joy",
+    "18": "love",
+    "19": "nervousness",
+    "20": "optimism",
+    "21": "pride",
+    "22": "realization",
+    "23": "relief",
+    "24": "remorse",
+    "25": "sadness",
+    "26": "surprise",
+    "27": "neutral"
+  },
+  "initializer_range": 0.02,
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+    "relief": 23,
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+  "use_cache": true,
+  "vocab_size": 50265
+}

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