Update README.md

README.md

@@ -11,27 +11,119 @@ model-index:
   results: []
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
 [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/josh-longenecker1-groundedai/phi3.5-hallucination/runs/re0kg3gs)
 # outputs
 
-This model is a fine-tuned version of [microsoft/Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct) on the None dataset.
-It achieves the following results on the evaluation set:
-- Loss: 1.3147
+This model is a fine-tuned version of [microsoft/Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct) on the HaluEval dataset for hallucination detection.
+
+## Merged Model Performance
+
+This repository contains our hallucination evaluation PEFT adapter model.
+
+### Hallucination Detection Metrics
+
+Our merged model achieves the following performance on a binary classification task for detecting hallucinations in language model outputs:
+
+```
+              precision    recall  f1-score   support
+
+           0       0.77      0.91      0.83       100
+           1       0.89      0.73      0.80       100
+
+    accuracy                           0.82       200
+   macro avg       0.83      0.82      0.82       200
+weighted avg       0.83      0.82      0.82       200
+```
+
+### Model Usage
+For best results, we recommend starting with the following prompting strategy (and encourage tweaks as you see fit):
+
+```python
+def format_input(reference, query, response):
+    prompt = f"""Your job is to evaluate whether a machine learning model has hallucinated or not.
+    A hallucination occurs when the response is coherent but factually incorrect or nonsensical
+    outputs that are not grounded in the provided context.
+    You are given the following information:
+    ####INFO####
+    [Knowledge]: {reference}
+    [User Input]: {query}
+    [Model Response]: {response}
+    ####END INFO####
+    Based on the information provided is the model output a hallucination? Respond with only "yes" or "no"
+    """
+    return input
+
+text = format_input(reference="The apple mac has the best hardware",
+          query='"What computer has the best software?",
+          response='Apple mac')
+
+messages = [
+    {"role": "user", "content": text}
+]
+
+pipe = pipeline(
+    "text-generation",
+    model=base_model,
+    model_kwargs={"attn_implementation": attn_implementation, "torch_dtype": torch.float16},
+    tokenizer=tokenizer,
+)
+generation_args = {
+      "max_new_tokens": 2,
+      "return_full_text": False,
+      "temperature": 0.01,
+      "do_sample": True,
+  }
+
+output = pipe(messages, **generation_args)
+print(f'Hallucination: {output['generated_text'].strip().lower()}')
+# Hallucination: yes
+```
+
+### Comparison with Other Models
+
+We compared our merged model's performance on the hallucination detection benchmark against several other state-of-the-art language models:
+
+| Model                 | Precision | Recall | F1     |
+|---------------------- |----------:|-------:|-------:|
+| Our Merged Model      | 0.77      | 0.91   | 0.83   |
+| GPT-4                 | 0.93      | 0.72   | 0.82   |
+| GPT-4 Turbo           | 0.97      | 0.70   | 0.81   |
+| Gemini Pro            | 0.89      | 0.53   | 0.67   |
+| GPT-3.5               | 0.89      | 0.65   | 0.75   |
+| GPT-3.5-turbo-instruct| 0.89      | 0.80   | 0.84   |
+| Palm 2 (Text Bison)   | 1.00      | 0.44   | 0.61   |
+| Claude V2             | 0.80      | 0.95   | 0.87   |
+
+Scores from arize/phoenix
+
+As shown in the table, our merged model achieves competitive performance, with an F1 score of 0.82, matching or outperforming several state-of-the-art language models on this hallucination detection task.
 
 ## Model description
 
-More information needed
+This model is a fine-tuned version of the Phi-3.5-mini-instruct model, specifically adapted for hallucination detection. It has been trained on the HaluEval dataset to identify when language model outputs contain hallucinations - responses that are coherent but factually incorrect or not grounded in the provided context.
 
 ## Intended uses & limitations
 
-More information needed
+This model is intended for use in evaluating the outputs of language models to detect potential hallucinations. It can be integrated into pipelines for content validation, fact-checking, or as a component in larger systems aimed at improving the reliability of AI-generated content.
+
+Limitations:
+- The model's performance may vary depending on the domain and complexity of the input.
+- It may not catch all types of hallucinations, especially those that are subtle or require extensive domain knowledge.
+- The model should be used as part of a broader strategy for ensuring AI output quality, not as a sole arbiter of truth.
 
 ## Training and evaluation data
 
-More information needed
+This model was trained using the HaluEval dataset:
+
+@misc{HaluEval,
+  author = {Junyi Li and Xiaoxue Cheng and Wayne Xin Zhao and Jian-Yun Nie and Ji-Rong Wen },
+  title = {HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models},
+  year = {2023},
+  journal={arXiv preprint arXiv:2305.11747},
+  url={https://arxiv.org/abs/2305.11747}
+}
+
+The HaluEval dataset is specifically designed for evaluating hallucinations in large language models, making it an ideal choice for training our hallucination detection model.
 
 ## Training procedure
 
@@ -67,7 +159,6 @@ The following hyperparameters were used during training:
 | 0.3138        | 6.3158 | 60   | 1.2195          |
 | 0.5315        | 6.8421 | 65   | 1.3147          |
 
-
 ### Framework versions
 
 - PEFT 0.12.0