Update README.md

README.md

@@ -17,13 +17,83 @@ It has been trained using [TRL](https://github.com/huggingface/trl).
 
 ## Quick start
 
+Example 1
+
 ```python
+from datasets import load_dataset
 from transformers import pipeline
 
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="plaguss/Mistral-7B-v0.1-Math-Shepherd-PRM-0.1", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
+pipe = pipeline("token-classification", model="plaguss/Mistral-7B-v0.1-Math-Shepherd-PRM-0.1")
+dataset = load_dataset("trl-lib/math_shepherd")
+example = dataset["test"][10]
+
+print("\n".join((example["prompt"], *example["completions"])))
+for idx in range(1, len(example["completions"])+1):
+    text = "\n".join((example["prompt"], *example["completions"][0:idx])) + "\n"
+    score = float(pipe(text)[-1]["score"])
+    print(f"Step {idx}\tScore: {score:.4f}\tLabel: {example['labels'][idx-1]}")
+
+# Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.
+# Step 1  Score: 1.00     Label: True
+# Step 2  Score: 1.00     Label: True
+# Step 3  Score: 1.00     Label: True
+# Step 4  Score: 0.96     Label: True
+# Step 5  Score: 0.95     Label: True
+# Step 6  Score: 0.88     Label: False
+# Step 7  Score: 0.73     Label: False
+# Step 8  Score: 0.86     Label: False
+# Step 9  Score: 0.96     Label: False
+```
+
+Original case from the Math-Shepherd paper
+
+```python
+from datasets import load_dataset
+from transformers import pipeline
+
+pipe = pipeline("token-classification", model="plaguss/Mistral-7B-v0.1-Math-Shepherd-PRM-0.1", device="cuda")
+
+examples = [
+    {
+        "prompt": "Janet\u2019s ducks lay 16 eggs per day. She eats three for breakfast every morning and bakes muffins for her friends every day with four. She sells the remainder at the farmers' market daily for $2 per fresh duck egg. How much in dollars does she make every day at the farmers' market?",
+        "completions": [
+            "Step 1: Janet's ducks lay 16 eggs per day.",
+            'Step 2: She eats three for breakfast every morning, so she has 16 - 3 = 13 eggs left.',
+            'Step 3: She bakes muffins for her friends every day with four eggs, so she has 13 - 4 = 9 eggs left.',
+            "Step 4: She sells the remainder at the farmers' market daily for $2 per fresh duck egg, so she makes 9 * $2 = $18 every day at the farmers' market. The answer is: 18"
+        ],
+        "labels": [True, True, True, True]
+    },
+    {
+        "prompt": "Janet\u2019s ducks lay 16 eggs per day. She eats three for breakfast every morning and bakes muffins for her friends every day with four. She sells the remainder at the farmers' market daily for $2 per fresh duck egg. How much in dollars does she make every day at the farmers' market?",
+        "completions": [
+            "Step 1: Janet's ducks lay 16 eggs per day.",
+            'Step 2: She eats three for breakfast every morning, so she has 16 - 3 = 13 eggs left.',
+            'Step 3: She bakes muffins for her friends every day with four eggs, so she has 13 - 4 = 9 eggs left.',
+            "Step 4: She sells the remainder at the farmers' market daily for $2 per fresh duck egg, so she makes 9 * $2 = $18 every day at the farmers' market. The answer is: 17"
+        ],
+        "labels": [True, True, True, False]
+    },
+    
+]
+
+for i, example in enumerate(examples):
+    print(f"- Example {i}:")
+    for idx in range(1, len(example["completions"])+1):
+        text = "\n".join((example["prompt"], *example["completions"][0:idx])) + "\n"
+        score = float(pipe(text)[-1]["score"])
+        print(f"Step {idx}\tScore: {score:.2f}\tLabel: {example['labels'][idx-1]}")
+
+# - Example 0:
+# Step 1  Score: 1.00     Label: True
+# Step 2  Score: 1.00     Label: True
+# Step 3  Score: 1.00     Label: True
+# Step 4  Score: 1.00     Label: True
+# - Example 1:
+# Step 1  Score: 1.00     Label: True
+# Step 2  Score: 1.00     Label: True
+# Step 3  Score: 1.00     Label: True
+# Step 4  Score: 0.98     Label: False
 ```
 
 ## Training procedure