feat: replace with calibrated model

README.md

@@ -9,9 +9,9 @@ LionGuard is a classifier for detecting unsafe content in the Singapore context.
 
 # Usage
 
-1. Install `transformers` , `xgboost` and `huggingface_hub` libraries. 
+1. Install `transformers` , `onnxruntime` and `huggingface_hub` libraries. 
 ```
-pip install transformers xgboost huggingface_hub
+pip install transformers onnxruntime huggingface_hub
 ```
 
 2. Run inference.

config.json

@@ -1,6 +1,6 @@
 {
     "model_type": "ridge_classifier",
-    "model_name": "lionguard_self_harm.json",
+    "model_name": "lionguard_self_harm.onnx",
     "description": "Binary classifier on text encouraging self-harm in Singapore context",
     "tokenizer": "BAAI/bge-large-en-v1.5",
     "embedding_model": "BAAI/bge-large-en-v1.5",

inference.py

@@ -2,12 +2,13 @@ import numpy as np
 import pandas as pd
 import torch
 from transformers import AutoTokenizer, AutoModel
-from sklearn.linear_model import RidgeClassifier
 from huggingface_hub import hf_hub_download
 import sys
 import json
+import onnxruntime as rt
 
-config_path = hf_hub_download(repo_id="dsaidgovsg/lionguard-self_harm-v1.0", filename="config.json")
+repo_path = "govtech/lionguard-self_harm-v1.0"
+config_path = hf_hub_download(repo_id=repo_path, filename="config.json")
 with open(config_path, 'r') as f:
     config = json.load(f)
 
@@ -31,38 +32,25 @@ def get_embeddings(device, data):
         output.extend(sentence_embeddings.cpu().numpy())
     return np.array(output)
 
-def set_model_atttributes(model, attributes):
-    model.coef_ = np.array(attributes['coef_'])
-    model.intercept_ = np.array(attributes['intercept_'])
-    model.n_features_in_ = np.array(attributes['n_features_in_'])
-    return model
-
-def convert_decision_to_proba(x):
-    # # NOTE: use softmax to convert ridge distances, assuming negative class is negative distance of positive class
-    class_scores = np.exp(np.vstack([-x, x]).T)
-    probs = class_scores / np.sum(class_scores, axis=1, keepdims=True)
-    return probs
-
 def predict(batch_text):
     device = torch.device("cuda") if torch.cuda.is_available() else "cpu"
     embeddings = get_embeddings(device, batch_text)
     embeddings_df = pd.DataFrame(embeddings)
 
-    # Load the parameters from model JSON
-    model_fp = hf_hub_download(repo_id="dsaidgovsg/lionguard-self_harm-v1.0", filename=config['model_name'])
-    with open(model_fp, 'r') as json_file:
-        model_params = json.load(json_file)
+    # Load the model
+    model_fp = hf_hub_download(repo_id=repo_path, filename=config['model_name'])
+    session = rt.InferenceSession(model_fp)
 
-    # Initialize the scikit-learn model with the loaded parameters
-    model = RidgeClassifier() 
-    model_attributes = model_params["attributes"]
-    model_params.pop("attributes", None)
-    model.set_params(**model_params)
-    model = set_model_atttributes(model, model_attributes)
-    
-    # Predict 
-    preds = convert_decision_to_proba(model.decision_function(embeddings_df))[:,1]
-    return preds.tolist()
+    # Prepare input data
+    input_name = session.get_inputs()[0].name
+    X_input = np.array(embeddings_df, dtype=np.float32)
+
+    # Run inference
+    preds = session.run(None, {input_name: X_input})
+    probs = preds[1]
+    probs_harmful = [prob[1] for prob in probs]
+
+    return probs_harmful
 
 
 if __name__ == "__main__":

lionguard_self_harm.json

@@ -1 +0,0 @@
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lionguard_self_harm.onnx

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