Update README.md

README.md

@@ -3,24 +3,24 @@ library_name: transformers
 license: apache-2.0
 base_model: answerdotai/ModernBERT-base
 tags:
-- reasoning
-- reasoning-datasets-competition
+  - reasoning
+  - reasoning-datasets-competition
 datasets:
-- davanstrien/natural-reasoning-classifier
+  - davanstrien/natural-reasoning-classifier
 language:
-- en
+  - en
 metrics:
-- mse
-- mae
-- spearman
+  - mse
+  - mae
+  - spearman
 widget:
-- text: >-
-    The debate on artificial intelligence's role in society has become
-    increasingly polarized. Some argue that AI will lead to widespread
-    unemployment and concentration of power, while others contend it will create
-    new jobs and democratize access to knowledge. These viewpoints reflect
-    different assumptions about technological development, economic systems, and
-    human adaptability.
+  - text: >-
+      The debate on artificial intelligence's role in society has become
+      increasingly polarized. Some argue that AI will lead to widespread
+      unemployment and concentration of power, while others contend it will create
+      new jobs and democratize access to knowledge. These viewpoints reflect
+      different assumptions about technological development, economic systems, and
+      human adaptability.
 ---
 
 # ModernBERT Reasoning Complexity Regressor
@@ -29,7 +29,9 @@ widget:
 
 ## Model Description
 
-This model predicts the reasoning complexity level (0-4) required to engage with a given text. It's fine-tuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the [davanstrien/natural-reasoning-classifier](https://huggingface.co/datasets/davanstrien/natural-reasoning-classifier) dataset.
+This model predicts the reasoning complexity level (0-4) that a given web text suggests. It's fine-tuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the [davanstrien/natural-reasoning-classifier](https://huggingface.co/datasets/davanstrien/natural-reasoning-classifier) dataset. The intended use for the model is in a pipeline to try and identify text that may be useful for generating reasoning data.
+
+### Reasoning Complexity Scale
 
 The reasoning complexity scale ranges from:
 
@@ -60,10 +62,10 @@ This model can be used to:
 - Predictions are influenced by the original dataset's domain distribution
 - Reasoning complexity is subjective and context-dependent
 
-
 ## Training
 
 The model was fine-tuned using a regression objective with the following settings:
+
 - Learning rate: 5e-05
 - Batch size: 16
 - Optimizer: AdamW
@@ -75,20 +77,20 @@ The model was fine-tuned using a regression objective with the following setting
 ### Using the pipeline API
 
 ```python
-from transformers import pipeline 
+from transformers import pipeline
 pipe = pipeline("text-classification", model="davanstrien/ModernBERT-based-Reasoning-Required")
 
 def predict_reasoning_level(text, pipe):
     # Get the raw prediction
     result = pipe(text)
     score = result[0]['score']
-    
+
     # Round to nearest integer (optional)
     rounded_score = round(score)
-    
+
     # Clip to valid range (0-4)
     rounded_score = max(0, min(4, rounded_score))
-    
+
     # Create a human-readable interpretation (optional)
     reasoning_labels = {
         0: "No reasoning",
@@ -97,7 +99,7 @@ def predict_reasoning_level(text, pipe):
         3: "Strong reasoning",
         4: "Advanced reasoning"
     }
-    
+
     return {
         "raw_score": score,
         "reasoning_level": rounded_score,
@@ -130,10 +132,8 @@ text = "The debate on artificial intelligence's role in society has become incre
 inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
 with torch.no_grad():
     outputs = model(**inputs)
-    
+
 # Get regression score
 complexity_score = outputs.logits.item()
 print(f"Reasoning Complexity: {complexity_score:.2f}/4.00")
 ```
-
-