Update README.md

README.md

@@ -39,6 +39,40 @@ This repository contains a fine-tuned DistilBERT model using the [esg-sentiment
 - Fine-tuned on the esg-sentiment dataset
 - Optimized for sentiment analysis in the context of ESG
 
+## Usage
+```
+from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
+import torch
+
+tokenizer = DistilBertTokenizer.from_pretrained('descartes100/distilBERT_ESG')
+model = DistilBertForSequenceClassification.from_pretrained('descartes100/distilBERT_ESG')
+
+text = "Our waste reduction initiatives aim to minimize environmental impact. From recycling programs to waste reduction technologies, we're dedicated to responsibly managing resources."
+encoding = tokenizer(text, return_tensors="pt")
+encoding = {k: v.to(trainer.model.device) for k,v in encoding.items()}
+outputs = model(**encoding)
+logits = outputs.logits
+sigmoid = torch.nn.Sigmoid()
+probs = sigmoid(logits.squeeze().cpu())
+predictions = np.zeros(probs.shape)
+predictions[np.where(probs >= 0.5)] = 1
+
+for idx, label in enumerate(predictions):
+  print(id2label[idx], ':', label)
+```
+### Result
+```
+Environmental Negative : 0.0
+Environmental Neutral : 0.0
+Environmental Positive : 1.0
+Social Negative : 0.0
+Social Neutral : 0.0
+Social Positive : 1.0
+Governance Negative : 0.0
+Governance Neutral : 0.0
+Governance Positive : 0.0
+```
+
 ## Intended uses & limitations
 
 ### Intended Uses