Upload README.md

README.md

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+---
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+- agent-routing
+- conversation-matching
+language: en
+license: apache-2.0
+datasets:
+- custom
+metrics:
+- cosine_similarity
+base_model: sentence-transformers/all-MiniLM-L12-v2
+---
+
+# Gatekeeper Agent Responding Model
+
+This is a fine-tuned [sentence-transformers](https://www.SBERT.net) model based on **all-MiniLM-L12-v2** that has been specifically trained for **agent routing and conversation matching**. The model determines whether agents should respond to conversations based on semantic similarity.
+
+## Model Details
+
+### Base Model
+- **Base Model**: [sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2)
+- **Model Architecture**: MiniLM-L12 (Microsoft)
+- **Embedding Dimensions**: 384
+- **Max Sequence Length**: 256 tokens
+
+### Training Data
+The model was fine-tuned on two custom datasets using triplet training:
+- **semantic_triplet_training_data_round1.pkl**: 469 samples
+- **inverse_semantic_triplet_training_data.pkl**: 475 samples
+
+Each sample contains:
+- `anchor`: Conversation text or agent description
+- `positive`: Similar/relevant text to the anchor
+- `negative`: Dissimilar/irrelevant text to the anchor
+
+### Training Configuration
+- **Loss Function**: MultipleNegativesRankingLoss
+- **Batch Size**: 16
+- **Learning Rate**: 2e-5
+- **Epochs**: 1
+- **Warmup Ratio**: 0.1
+- **Training Framework**: sentence-transformers v2.7.0+
+
+### Performance
+Evaluation results on held-out test sets:
+- **Semantic Triplets Accuracy**: 97.87%
+- **Inverse Semantic Triplets Accuracy**: 100.00%
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+```python
+from sentence_transformers import SentenceTransformer
+
+# Load the model
+model = SentenceTransformer('msugimura/gatekeeper_agent_responding')
+
+# Example: Agent routing for conversation
+conversation = "I've been feeling anxious and need help with stress management"
+agent_descriptions = [
+    "Licensed therapist specializing in anxiety and stress management",
+    "Fitness trainer who creates workout routines for stress relief",
+    "Financial advisor who helps with investment planning"
+]
+
+# Get embeddings
+conversation_embedding = model.encode(conversation)
+agent_embeddings = model.encode(agent_descriptions)
+
+# Calculate similarities
+from sentence_transformers.util import cos_sim
+similarities = cos_sim(conversation_embedding, agent_embeddings)
+
+print("Similarity scores:", similarities)
+# Expected: Highest similarity with the therapist
+```
+
+### API Usage (Portcullis Service)
+```python
+import requests
+
+# Example API call to Portcullis service
+response = requests.post("http://localhost:8000/should_agents_respond", json={
+    "conversation": "I've been feeling anxious and need help",
+    "agent_descriptions": [
+        "Licensed therapist specializing in anxiety treatment",
+        "Fitness trainer for workout routines",
+        "Financial advisor for investments"
+    ],
+    "threshold": 0.4
+})
+
+result = response.json()
+print("Qualified agents:", result["qualified_agents"])
+```
+
+## Intended Use Cases
+
+1. **Agent Routing**: Automatically route conversations to appropriate specialist agents
+2. **Conversation Matching**: Match user queries with relevant service providers
+3. **Semantic Search**: Find similar conversations or agent descriptions
+4. **Content Recommendation**: Recommend agents based on conversation context
+
+## Limitations
+
+- **Domain Specific**: Optimized for agent-conversation matching scenarios
+- **English Only**: Trained primarily on English text
+- **Context Length**: Limited to 256 tokens per input
+- **Training Data**: Performance depends on similarity to training domain
+
+## Technical Details
+
+### Model Architecture
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
+  (2): Normalize()
+)
+```
+
+### Training Process
+1. **Data Preprocessing**: Cleaned triplet datasets, removed extraneous columns
+2. **Multi-Dataset Training**: Combined training on both semantic and inverse semantic data
+3. **Loss Function**: MultipleNegativesRankingLoss with in-batch negatives
+4. **Evaluation**: TripletEvaluator on held-out validation sets
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{gatekeeper_agent_responding_2024,
+  title={Gatekeeper Agent Responding Model},
+  author={Michael Sugimura},
+  year={2024},
+  publisher={Hugging Face},
+  url={https://huggingface.co/msugimura/gatekeeper_agent_responding}
+}
+```
+
+## Contact
+
+For questions or issues, please contact [your-email] or open an issue in the model repository.