Update README.md

README.md

@@ -40,4 +40,30 @@ No Evaluation is done for data with only text and no emojis.
 The model was fine-tuned with dataset: mteb/tweet_sentiment_extraction from huggingface
 converted to hinglish text.
 
-The model has a test loss of 0.6 and an f1 score of 0.74 on the unseen data from the dataset.
+The model has a test loss of 0.6 and an f1 score of 0.74 on the unseen data from the dataset.
+
+```
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+
+pipe = pipeline("text-classification", model="pascalrai/hinglish-twitter-roberta-base-sentiment")
+pipe("tu mujhe pasandh heh")
+```
+
+```
+# Load model directly
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("pascalrai/hinglish-twitter-roberta-base-sentiment")
+model = AutoModelForSequenceClassification.from_pretrained("pascalrai/hinglish-twitter-roberta-base-sentiment")
+
+inputs = ["tum kon ho bhai","tu mujhe pasandh heh"]
+outputs = model(**tokenizer(inputs, return_tensors='pt', padding=True))
+
+p = torch.nn.Softmax(dim = 1)(outputs.logits)
+for index, each in enumerate(p.detach().numpy()):
+    print(f"Text: {inputs[index]}")
+    print(f"Negative: {round(float(each[0]),2)}\nNeutral: {round(float(each[1]),2)}\nPositive: {round(float(each[2]),2)}\n")
+```