Create README.md

README.md

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+# Athena- Intent
+
+Classifies intent of the query for Athena
+
+## Architecture
+distilbert-base-uncased backbone, finetuned over a multiclass classification problem
+
+### Description
+
+Classifies user intent of queries into the following classes:
+0: Keyword Search
+1: Semantic Search
+2: Direct Question Answering
+
+
+## Uses
+
+This model is intended to be used in Athena for performing QA on enterprise document stores.
+
+
+## Bias, Risks, and Limitations
+
+Dataset was generated using ChatGPT (gpt-3.5-turbo). It consists of 5000 English sentences and the nature of their intent, annotated manually.
+
+## Usage
+
+```
+from transformers import AutoTokenizer
+from transformers import TFDistilBertForSequenceClassification
+import tensorflow as tf
+
+model = TFDistilBertForSequenceClassification.from_pretrained("sourcerersupreme/athena-intent")
+tokenizer = AutoTokenizer.from_pretrained("sourcerersupreme/athena-intent")
+
+class_semantic_mapping = {
+        0: "Keyword",
+        1: "Semantic",
+        2: "QA"
+    }
+
+# Get user input
+user_query = "What is a CDP?"
+
+# Encode the user input
+inputs = tokenizer(user_query, return_tensors="tf", truncation=True, padding=True)
+
+# Get model predictions
+predictions = model(inputs)[0]
+
+# Get predicted class
+predicted_class = tf.math.argmax(predictions, axis=-1)
+
+print(f"Predicted class: {class_semantic_mapping[int(predicted_class)]}")
+```