Developed by Zero Proof Labs; brand spacing; remove payments tag

README.md

@@ -7,7 +7,6 @@ library_name: peft
 pipeline_tag: text-classification
 tags:
   - intent-classification
-  - payments
   - ecommerce
   - lora
   - qlora
@@ -18,7 +17,7 @@ language:
 
 # Model Card for zeroproof-ecommerce-0.5b
 
-**E-commerce intent detection in a 0.5B model, the smallest member of the ZeroProof family.** Given a live customer conversation, `zeroproof-ecommerce-0.5b` returns a single JSON object with the customer's payment intent and its structured details, at the lowest cost and latency in the family.
+**E-commerce intent detection in a 0.5B model, the smallest member of the Zero Proof family.** Given a live customer conversation, `zeroproof-ecommerce-0.5b` returns a single JSON object with the customer's payment intent and its structured details, at the lowest cost and latency in the family.
 
 On a hard, held-out benchmark it reaches **70.3 macro-averaged intent-type accuracy, roughly 91% of our 1B**, from a base model that scores 10.7.
 
@@ -26,7 +25,7 @@ On a hard, held-out benchmark it reaches **70.3 macro-averaged intent-type accur
 
 ### Model Description
 
-- **Developed by:** ZeroProof
+- **Developed by:** Zero Proof Labs
 - **Model type:** E-commerce payment-intent classifier; structured JSON output over seven intent types
 - **Language:** English
 - **License:** Apache-2.0 (inherited from the base model)
@@ -71,7 +70,7 @@ Use greedy decoding (`temperature=0`). The system prompt and per-intent detail s
 
 ### Training Data
 
-The differentiator is the data. ZeroProof builds e-commerce intent training data as **randomized conversational simulations**: role-played customers with independently sampled personas, situations, and behaviors including adversarial actors, simulated turn by turn between two independently drawn models. Generation is label-blind, labels are assigned in a separate pass, and every candidate passes a structural data gate with zero train/eval leakage. This 0.5B was trained on the same class-rebalanced split as the 1B so it routes across all intent types rather than defaulting to the majority.
+The differentiator is the data. Zero Proof Labs builds e-commerce intent training data as **randomized conversational simulations**: role-played customers with independently sampled personas, situations, and behaviors including adversarial actors, simulated turn by turn between two independently drawn models. Generation is label-blind, labels are assigned in a separate pass, and every candidate passes a structural data gate with zero train/eval leakage. This 0.5B was trained on the same class-rebalanced split as the 1B so it routes across all intent types rather than defaulting to the majority.
 
 ### Training Procedure
 
@@ -103,4 +102,4 @@ A LoRA adapter over `Qwen2.5-0.5B-Instruct`, trained response-only to emit one s
 
 ## Model Card Contact
 
-ZeroProof, https://huggingface.co/zero-proof-ai
+Zero Proof Labs, https://huggingface.co/zero-proof-ai