Update README.md

README.md

@@ -2,10 +2,12 @@
 license: apache-2.0
 ---
 # Cross-Encoder for Hallucination Detection
-This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. This model is based on [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base).
+This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. 
+The model outputs a probabilitity from 0 to 1, 0 being a hallucination and 1 being factually consistent. 
+The predictions can be thresholded at 0.5 to predict whether a document is consistent with its source.
 
 ## Training Data
-The model was trained on the NLI data and a variety of datasets evaluating summarization accuracy for factual consistency, including [FEVER](https://huggingface.co/datasets/fever), [Vitamin C](https://huggingface.co/datasets/tals/vitaminc) and [PAWS](https://huggingface.co/datasets/paws).
+This model is based on [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) and is trained initially on NLI data to determine textual entailment, before being further fine tuned on summarization datasets with samples annotated for factual consistency including [FEVER](https://huggingface.co/datasets/fever), [Vitamin C](https://huggingface.co/datasets/tals/vitaminc) and [PAWS](https://huggingface.co/datasets/paws).
 
 ## Performance
 
@@ -20,7 +22,7 @@ The model can be used like this:
 ```python
 from sentence_transformers import CrossEncoder
 model = CrossEncoder('vectara/hallucination_evaluation_model')
-model.predict([
+scores = model.predict([
     ["A man walks into a bar and buys a drink", "A bloke swigs alcohol at a pub"],
     ["A person on a horse jumps over a broken down airplane.", "A person is at a diner, ordering an omelette."],
     ["A person on a horse jumps over a broken down airplane.", "A person is outdoors, on a horse."],
@@ -33,7 +35,7 @@ model.predict([
 
 This returns a numpy array:
 ```
-array([6.1051559e-01, 4.7493709e-04, 9.9639291e-01, 2.1221573e-04, 9.9599433e-01, 1.4127002e-03, 2.8262993e-03], dtype=float32)
+array([0.61051559, 0.00047493709, 0.99639291, 0.00021221573, 0.99599433, 0.0014127002, 0.002.8262993], dtype=float32)
 ```
 
 ## Usage with Transformers AutoModel
@@ -61,10 +63,11 @@ model.eval()
 with torch.no_grad():
     outputs = model(**inputs)
     logits = outputs.logits.cpu().detach().numpy()
+    # convert logits to probabilities
     scores = 1 / (1 + np.exp(-logits)).flatten()
 ```
 
 This returns a numpy array:
 ```
-array([6.1051559e-01, 4.7493709e-04, 9.9639291e-01, 2.1221573e-04, 9.9599433e-01, 1.4127002e-03, 2.8262993e-03], dtype=float32)
+array([0.61051559, 0.00047493709, 0.99639291, 0.00021221573, 0.99599433, 0.0014127002, 0.002.8262993], dtype=float32)
 ```