Update README.md

README.md

@@ -113,6 +113,57 @@ pip install sgnlp
 ## Examples
 For more full code (such as Causal Span Detection), please refer to this [SGNLP-Github](https://github.com/aisingapore/sgnlp). <br> Alternatively, you can also try out the [SGNLP-Demo](https://sgnlp.aisingapore.net/span-extraction) for Causal-Span-Detection.
 
+Example of Causal Span Detection (for surprise):
+
+```python
+from sgnlp.models.span_extraction import (
+    RecconSpanExtractionConfig,
+    RecconSpanExtractionModel,
+    RecconSpanExtractionTokenizer,
+    RecconSpanExtractionPreprocessor,
+    RecconSpanExtractionPostprocessor,
+)
+
+# Load model
+config = RecconSpanExtractionConfig.from_pretrained(
+    "https://storage.googleapis.com/sgnlp/models/reccon_span_extraction/config.json"
+)
+tokenizer = RecconSpanExtractionTokenizer.from_pretrained(
+    "mrm8488/spanbert-finetuned-squadv2"
+)
+model = RecconSpanExtractionModel.from_pretrained(
+    "https://storage.googleapis.com/sgnlp/models/reccon_span_extraction/pytorch_model.bin",
+    config=config,
+)
+preprocessor = RecconSpanExtractionPreprocessor(tokenizer)
+postprocessor = RecconSpanExtractionPostprocessor()
+
+# Model predict
+input_batch = {
+    "emotion": ["surprise", "surprise"],
+    "target_utterance": [
+        "Hi George ! It's good to see you !",
+        "Hi George ! It's good to see you !",
+    ],
+    "evidence_utterance": [
+        "Linda ? Is that you ? I haven't seen you in ages !",
+        "Hi George ! It's good to see you !",
+    ],
+    "conversation_history": [
+        "Linda ? Is that you ? I haven't seen you in ages ! Hi George ! It's good to see you !",
+        "Linda ? Is that you ? I haven't seen you in ages ! Hi George ! It's good to see you !",
+    ],
+}
+
+tensor_dict, evidences, examples, features = preprocessor(input_batch)
+raw_output = model(**tensor_dict)
+context, evidence_span, probability = postprocessor(
+    raw_output, evidences, examples, features)
+
+
+
+```
+
 # Training
 The train and evaluation datasets were derived from the RECCON dataset. The full dataset can be downloaded from the author's [github repository](https://github.com/declare-lab/RECCON/tree/main/data).