Update README.md

README.md

@@ -13,9 +13,26 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is intended to detect the presence of a present-moment experience a human or animal is experiencing in a sentence.
 
+## Usage
+
+Given a sentence, the model gives logits of whether or not that sentence contains a present-moment experience. Higher values correspond to the sentence having that experience.
+
+```
+model = transformers.AutoModelForSequenceClassification.from_pretrained('edmundmills/experience-model-v1')  # type: ignore
+tokenizer = transformers.AutoTokenizer.from_pretrained('edmundmills/experience-model-v1', use_fast=False)  # type: ignore
+sentence = "I am eating food."
+tokenized = tokenizer([sentence], return_tensors='pt', return_attention_mask=True)
+input_ids, masks = tokenized['input_ids'], tokenized['attention_mask']
+with torch.inference_mode():
+    out = model(input_ids, attention_mask=masks)
+probs = out.logits.sigmoid().squeeze().item()
+print(probs) # 0.92
+
+```
+
 ## Model description
 
-More information needed
+This model was fine-tuned from 'microsoft/deberta-v3-large'.
 
 ## Intended uses & limitations
 
@@ -27,7 +44,7 @@ This model was trained on 745 training samples, with ~10% of them containing pre
 
 ## Training procedure
 
-This model was fine-tuned from 'microsoft/deberta-v3-large' using https://github.com/AlignmentResearch/experience-model.
+The model was fine-tuned using https://github.com/AlignmentResearch/experience-model. It used BCE Loss.
 
 ### Training hyperparameters