Update README.md

README.md

@@ -25,25 +25,18 @@ model-index:
 # Text Classification GoEmotions
 
 This a ONNX quantized model and is fined-tuned version of [nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large) on the on the [go_emotions](https://huggingface.co/datasets/go_emotions) dataset using [tasinho/text-classification-goemotions](https://huggingface.co/tasinhoque/text-classification-goemotions) as teacher model.
+The original model can be found [here](https://huggingface.co/minuva/MiniLMv2-goemotions-v2)
 
 # Usage
 
-## Transformers
-
-
-
-
-
-## No-transformers
-
-### Installation
+## Installation
 ```bash
 pip install tokenizers
 pip install onnxruntime
 git clone https://huggingface.co/minuva/MiniLMv2-goemotions-v2-onnx
 ```
 
-### Load the Model
+## Run the Model
 
 ```py
 import os
@@ -64,7 +57,7 @@ tokenizer.enable_padding(
 tokenizer.enable_truncation(max_length=256)
 batch_size = 16
 
-texts = ["I am angry",]
+texts = ["I am angry", "I feel in love"]
 outputs = []
 model = InferenceSession("MiniLMv2-goemotions-v2-onnx/model_optimized_quantized.onnx", providers=['CUDAExecutionProvider'])
 
@@ -112,7 +105,16 @@ for item in scores:
         scores.append(float(s))
     results.append({"labels": labels, "scores": scores})
 
-results
+
+res = []
+
+for result in results:
+    joined = list(zip(result['labels'], result['scores']))
+    max_score = max(joined, key=lambda x: x[1])    
+    res.append(max_score)
+
+res
+# [('anger', 0.9745745062828064), ('love', 0.9884329438209534)]
 ```
 # Training hyperparameters