Update README.md

README.md

@@ -1,6 +1,26 @@
+---
+datasets:
+- argilla/tripadvisor-hotel-reviews
+language:
+- en
+metrics:
+- accuracy: 0.9018
+- F-1 score: 0.8956
+pipeline_tag: text-classification
+---
 Sentiment analysis model that uses MiniLM pre-trained (from https://huggingface.co/microsoft/MiniLM-L12-H384-uncased), and fine-tuned on a dataset containing Trip Advisor reviews (from https://www.kaggle.com/datasets/arnabchaki/tripadvisor-reviews-2023).
 
 Reviews with 1 or 2 stars are considered 'Negative', 3 stars are 'Neutral', and 4 or 5 stars are 'Positive'.
 
-Accuracy on test set == 0.9018
-F-1 score on test set == 0.8956
+Should be loaded with the following code:
+
+
+```
+# Load pre-trained model and tokenizer
+model_name = "gosorio/minilmFT_TripAdvisor_Sentiment"
+tokenizer_name = "microsoft/MiniLM-L12-H384-uncased" # the standard MiniLM
+device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+
+tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=3).to(device)
+```