Update README.md

README.md

@@ -12,8 +12,11 @@ datasets:
 license: mit
 widget:
 - text: I am not having a great day.
+library_name: transformers.js
 ---
 
+# This is a Transformers.js clone of [SamLowe/roberta-base-go_emotions](https://huggingface.co/SamLowe/roberta-base-go_emotions) ! 
+
 #### Overview
 
 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.
@@ -34,16 +37,17 @@ The model was trained using `AutoModelForSequenceClassification.from_pretrained`
 
 There are multiple ways to use this model in Huggingface Transformers. Possibly the simplest is using a pipeline:
 
-```python
-from transformers import pipeline
-
-classifier = pipeline(task="text-classification", model="SamLowe/roberta-base-go_emotions", top_k=None)
+```js
+const { pipeline } = await import('@xenova/transformers');
 
-sentences = ["I am not having a great day"]
+// Allocate pipeline
+const pipe = await pipeline('text-classification', "MicahB/roberta-base-go_emotions");
+console.log(await pipe("I love transformers!"));
+```
 
-model_outputs = classifier(sentences)
-print(model_outputs[0])
-# produces a list of dicts for each of the labels
+```js
+Output:
+[ { label: 'love', score: 0.9529242515563965 } ]
 ```
 
 #### Evaluation / metrics