Update README.md

README.md

@@ -8,4 +8,50 @@ base_model:
 - google/mobilebert-uncased
 pipeline_tag: text-classification
 library_name: transformers
----
+---
+
+
+#### Overview
+
+Model trained from [mobileBert](https://huggingface.co/google/mobilebert-uncased) on the [go_emotions](https://huggingface.co/datasets/go_emotions) dataset for multi-label classification.
+
+#### Dataset used for the model
+
+[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.
+
+#### How the model was created
+
+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.
+
+#### Inference
+
+There are multiple ways to use this model in Huggingface Transformers. Possibly the simplest is using a pipeline:
+
+### 1. Install dependencies
+
+```bash
+pip install torch transformers
+```
+
+
+```python
+from transformers import pipeline
+classifier = pipeline(task="text-classification", model="AR04/Senti", top_k=None)
+sentences = ["hi! u r looki beautiful today dear"]
+model_outputs = classifier(sentences)
+print(model_outputs[0])
+# produces a list of dicts for each of the labels
+
+[{'label': 'admiration', 'score': 0.9517803192138672}, {'label': 'love', 'score': 0.18317067623138428}, {'label': 'joy', 'score': 0.03131399303674698}, {'label': 'neutral', 'score': 0.01567094214260578}, {'label': 'surprise', 'score': 0.009232419542968273}, {'label': 'approval', 'score': 0.007308646105229855}, {'label': 'excitement', 'score': 0.006345656234771013}, {'label': 'pride', 'score': 0.004945244640111923}, {'label': 'caring', 'score': 0.0038624939043074846}, {'label': 'realization', 'score': 0.0023580112028867006}, {'label': 'desire', 'score': 0.0017759536858648062}, {'label': 'optimism', 'score': 0.0013220690889284015}, {'label': 'sadness', 'score': 0.001188945840112865}, {'label': 'disappointment', 'score': 0.0009136834414675832}, {'label': 'gratitude', 'score': 0.0008250900427810848}, {'label': 'relief', 'score': 0.0005154621903784573}, {'label': 'amusement', 'score': 0.0004376845608931035}, {'label': 'fear', 'score': 0.00038696840056218207}, {'label': 'embarrassment', 'score': 0.0003084330528508872}, {'label': 'grief', 'score': 0.00019462488126009703}, {'label': 'confusion', 'score': 0.00018893269589170814}, {'label': 'annoyance', 'score': 0.0001587819424457848}, {'label': 'curiosity', 'score': 0.0001355114800389856}, {'label': 'remorse', 'score': 0.00011744408402591944}, {'label': 'anger', 'score': 0.00010586195276118815}, {'label': 'disgust', 'score': 9.386352030560374e-05}, {'label': 'nervousness', 'score': 7.547048153355718e-05}, {'label': 'disapproval', 'score': 3.7117086321813986e-05}]
+```
+
+#### Evaluation / metrics
+
+Here are the evaluation results of **Senti** on the GoEmotions validation set:
+
+| Metric       | Value |
+|--------------|-------|
+| Loss         | 0.085 |
+| F1-score     | 0.586 |
+| ROC AUC      | 0.752 |
+| Accuracy     | 0.460 |