Update README.md

README.md

@@ -1,44 +1,123 @@
 ---
 base_model: Qwen/Qwen3-0.6B
 library_name: transformers
-model_name: Qwen3-0.6B-SFT-AAT-Materials3
+model_name: Qwen3-0.6B-SFT-AAT-Materials
 tags:
 - generated_from_trainer
-- hf_jobs
-- trl
 - sft
-licence: license
+- trl
+- hf_jobs
+- cultural-heritage
+- aat
+- materials-identification
+- glam
+- digital-humanities
+licence: mit
 ---
 
-# Model Card for Qwen3-0.6B-SFT-AAT-Materials3
+# Model Card for Qwen3-0.6B-SFT-AAT-Materials
+
+This model is a fine-tuned version of [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) specialized for identifying materials in cultural heritage object descriptions according to Getty Art & Architecture Thesaurus (AAT) standards.
+
+It has been trained using [TRL](https://github.com/huggingface/trl) on synthetic data representing diverse cultural heritage objects from museums, galleries, libraries, archives, and museums (GLAM) collections.
+
+## Model Description
+
+This model excels at:
+
+- **Materials Identification**: Extracting and categorizing materials from cultural heritage object descriptions
+- **AAT Standardization**: Converting material descriptions to Getty Art & Architecture Thesaurus format
+- **Multi-material Recognition**: Identifying compound materials (e.g., "oil on canvas" → ["Oil paint", "Canvas"])
+- **Domain-specific Understanding**: Processing technical terminology from art history, archaeology, and museum cataloging
+
+## Use Cases
+
+### Primary Applications
+- **Museum Cataloging**: Automated material extraction from object descriptions
+- **Digital Collections**: Standardizing material metadata across cultural heritage databases
+- **Research Tools**: Supporting art historians and archaeologists in material analysis
+- **Data Migration**: Converting legacy catalog records to AAT standards
 
-This model is a fine-tuned version of [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B).
-It has been trained using [TRL](https://github.com/huggingface/trl).
+### Object Types Supported
+- Paintings (oil, tempera, watercolor, acrylic)
+- Sculptures (bronze, marble, wood, clay)
+- Textiles (wool, linen, silk, cotton)
+- Ceramics and pottery
+- Metalwork and jewelry
+- Glassware
+- Manuscripts and prints
+- Furniture and decorative objects
 
-## Quick start
+## Quick Start
 
 ```python
-from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import json
+
+# Load the model
+tokenizer = AutoTokenizer.from_pretrained("small-models-for-glam/Qwen3-0.6B-SFT-AAT-Materials")
+model = AutoModelForCausalLM.from_pretrained("small-models-for-glam/Qwen3-0.6B-SFT-AAT-Materials")
+
+# Example cultural heritage object description
+description = """A bronze sculpture from 1425, standing 150 cm tall. The figure is mounted on a marble base and features intricate details cast in the bronze medium. The sculpture shows traces of original gilding on selected areas."""
+
+# Format the prompt
+prompt = f"""Given this cultural heritage object description:
+
+{description}
+
+Identify the materials separate out materials as they would be found in Getty AAT"""
+
+# Generate materials identification
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(inputs.input_ids, max_length=512, temperature=0.3)
+result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Extract the materials output
+materials = result[len(prompt):].strip()
+print(json.loads(materials))
+# Expected output: [{"Bronze": ["bronze"]}, {"Marble": ["marble"]}, {"Gold leaf": ["gold", "leaf"]}]
+```
+
+## Expected Output Format
+
+The model outputs materials in JSON format where each material combination is mapped to its constituent AAT terms:
 
-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="small-models-for-glam/Qwen3-0.6B-SFT-AAT-Materials3", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
+```json
+[
+  {"oil on canvas": ["Oil paint", "Canvas"]},
+  {"tempera on wood": ["tempera paint", "wood (plant material)"]},
+  {"bronze": ["bronze"]}
+]
 ```
 
-## Training procedure
+## Training Procedure
 
- 
+This model was trained using Supervised Fine-Tuning (SFT) on the `small-models-for-glam/synthetic-aat-materials` dataset, which contains thousands of synthetic cultural heritage object descriptions paired with their corresponding AAT material classifications.
 
+### Training Details
+- **Base Model**: Qwen/Qwen3-0.6B
+- **Training Method**: Supervised Fine-Tuning (SFT) with TRL
+- **Dataset**: Synthetic AAT materials dataset
+- **Infrastructure**: Trained using Hugging Face Jobs
+- **Epochs**: 3
+- **Batch Size**: 4 (with gradient accumulation)
+- **Learning Rate**: 2e-5
+- **Context**: Cultural heritage object descriptions → AAT materials mapping
 
-This model was trained with SFT.
+### Dataset Characteristics
+The training dataset includes diverse object types:
+- Historical artifacts from various time periods
+- Multiple material combinations per object
+- Professional museum cataloging terminology
+- AAT-compliant material classifications
 
 ### Framework versions
 
 - TRL: 0.23.0
 - Transformers: 4.56.1
 - Pytorch: 2.8.0
-- Datasets: 4.1.1
+- Datasets: 4.1.0
 - Tokenizers: 0.22.0
 
 ## Citations