EphAsad
/

DomainEmbedder

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----
-license: mit
-language:
-- en
-tags:
-- sentence-transformers
-- sentence-embeddings
-- multi-task-learning
-- reinforcement-learning
-- semantic-similarity
-- nli
-- paraphrase-detection
-datasets:
-- sentence-transformers/stsb
-- nyu-mll/multi_nli
-- quora
-- google-research-datasets/paws
-- nyu-mll/glue
-pipeline_tag: sentence-similarity
-base_model: sentence-transformers/all-MiniLM-L6-v2
-model-index:
-- name: FireDevourerEmbedder-RL-v3.6
-  results:
-  - task:
-      type: semantic-similarity
-      name: Semantic Textual Similarity
-    dataset:
-      type: sentence-transformers/stsb
-      name: STS-B
-    metrics:
-    - type: pearson_spearman_avg
-      value: 0.3366
-  - task:
-      type: natural-language-inference
-      name: Natural Language Inference
-    dataset:
-      type: nyu-mll/multi_nli
-      name: MultiNLI
-    metrics:
-    - type: accuracy
-      value: 0.7465
-  - task:
-      type: text-classification
-      name: Question Duplicate Detection
-    dataset:
-      type: quora
-      name: QQP
-    metrics:
-    - type: accuracy
-      value: 0.8636
-  - task:
-      type: text-classification
-      name: Paraphrase Detection
-    dataset:
-      type: google-research-datasets/paws
-      name: PAWS
-    metrics:
-    - type: accuracy
-      value: 0.8459
-  - task:
-      type: text-classification
-      name: Paraphrase Detection
-    dataset:
-      type: nyu-mll/glue
-      name: MRPC
-    metrics:
-    - type: accuracy
-      value: 0.7744
----
-# FireDevourerEmbedder-RL-v3.6
-A multi-task sentence embedding model that uses **Reinforcement Learning** to dynamically optimize task weights during training. The model learns to balance multiple NLU tasks simultaneously, producing robust sentence embeddings suitable for semantic similarity, natural language inference, and paraphrase detection.
-## Key Innovation
-FireDevourerEmbedder introduces an **RL-based adaptive task weighting system** that automatically adjusts the importance of each training task based on validation performance. Instead of using fixed task weights, a policy network learns optimal weight distributions during training, leading to better overall performance across diverse NLU benchmarks.
-## Why Multi-Task? Information-Dense Embeddings
-The core philosophy behind FireDevourerEmbedder is that **multi-task learning creates richer, more information-dense embeddings** than single-task approaches.
-By training with multiple task heads simultaneously, the shared encoder is forced to learn representations that capture:
-| Dimension | Learned From | What It Captures |
-|-----------|--------------|------------------|
-| **Semantic Similarity** | STS-B | Fine-grained meaning overlap |
-| **Logical Relationships** | MultiNLI | Entailment, contradiction, neutrality |
-| **Question Semantics** | QQP | Intent and duplicate detection |
-| **Adversarial Patterns** | PAWS | Word-order sensitivity, paraphrase robustness |
-| **Domain Awareness** | All datasets | Context-appropriate representations |
-This results in embeddings that are:
-- **More robust** - trained to handle diverse linguistic phenomena
-- **More transferable** - generalize better to unseen tasks
-- **More informative** - each dimension of the embedding vector carries meaningful semantic signal
-Unlike single-task embedders that optimize for one objective, FireDevourerEmbedder's embeddings simultaneously encode multiple facets of meaning, making them suitable for a wide range of downstream applications without fine-tuning.
-## Model Details
-| Property | Value |
-|----------|-------|
-| **Base Model** | [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) |
-| **Hidden Size** | 384 |
-| **Version** | v3.6 |
-| **Training Steps** | 80,000 |
-| **Total Parameters** | ~22M |
-## Architecture
-The model consists of a shared BERT encoder with task-specific output heads:
-```
-Input Sentence(s)
-       │
-       ▼
-┌─────────────────────────┐
-│   MiniLM-L6-v2 Encoder  │
-│     (384-dim output)    │
-└─────────────────────────┘
-       │
-       ▼
-   Mean Pooling
-       │
-       ├──► STS Head (384→1) ──► Similarity Score [0,1]
-       ├──► NLI Head (384→3) ──► [Contradiction, Neutral, Entailment]
-       ├──► QQP Head (384→2) ──► [Not Duplicate, Duplicate]
-       ├──► PAWS Head (384→2) ──► [Not Paraphrase, Paraphrase]
-       └──► Domain Head (384→5) ──► [General, Entailment, Questions, Adversarial, News]
-```
-## Performance
-| Task | Dataset | Metric | Score |
-|------|---------|--------|-------|
-| Question Duplicate Detection | QQP | Accuracy + F1 | **0.8636** |
-| Paraphrase Detection | PAWS | Accuracy + F1 | **0.8459** |
-| Paraphrase Detection | MRPC | Accuracy + F1 | **0.7744** |
-| Natural Language Inference | MultiNLI | Accuracy + F1 | **0.7465** |
-| Semantic Textual Similarity | STS-B | Pearson/Spearman | **0.3366** |
-| | | **Average** | **0.7134** |
-## Training Details
-### Datasets
-The model was trained on 5 balanced datasets with 100,000 samples each (500,000 total):
-| Dataset | Task Type | Domain | Samples |
-|---------|-----------|--------|---------|
-| [STS-B](https://huggingface.co/datasets/sentence-transformers/stsb) | Semantic Similarity | General | 100,000 |
-| [MultiNLI](https://huggingface.co/datasets/nyu-mll/multi_nli) | Natural Language Inference | Entailment | 100,000 |
-| [QQP](https://huggingface.co/datasets/quora) | Duplicate Question Detection | Questions | 100,000 |
-| [PAWS](https://huggingface.co/datasets/google-research-datasets/paws) | Paraphrase Detection | Adversarial | 100,000 |
-| [MRPC](https://huggingface.co/datasets/nyu-mll/glue) | Paraphrase Detection | News | 100,000 |
-### Data Augmentation Strategy
-To prevent training bias, all datasets were balanced to exactly **100,000 samples** each:
-| Dataset | Original Size | Augmentation Method |
-|---------|---------------|---------------------|
-| STS-B | ~8,600 | Repetition (~12x) + pair swapping |
-| MultiNLI | ~433,000 | Subsampling |
-| QQP | ~400,000 | Subsampling |
-| PAWS | ~49,000 | Repetition (~2x) + pair swapping |
-| MRPC | ~3,600 | Repetition (~10x, capped) + pair swapping |
-**Why this matters:**
-- Without balancing, larger datasets (QQP, MultiNLI) would dominate training
-- Smaller but valuable datasets (MRPC, STS-B) would be underrepresented
-- Equal representation ensures the model learns equally from all task types
-**Augmentation techniques:**
-- **Repetition**: Smaller datasets repeated up to 10x maximum to prevent memorization
-- **Sentence pair swapping**: For symmetric tasks, (A, B) pairs also trained as (B, A)
-### Training Configuration
-| Parameter | Value |
-|-----------|-------|
-| Epochs | 3 |
-| Batch Size | 16 |
-| Learning Rate | 2e-5 |
-| Total Steps | 93,750 |
-| Warmup Steps | 9,375 (10%) |
-| Evaluation Frequency | Every 10,000 steps |
-| Early Stopping | 3 consecutive decreases |
-| Training Time | 3.29 hours |
-### RL Weight Adaptation System
-The model uses a policy network to dynamically adjust task weights during training:
-| Parameter | Value |
-|-----------|-------|
-| RL Learning Rate | 0.001 |
-| State Dimension | 6 (5 task scores + average) |
-| Action Dimension | 5 (weight deltas) |
-| Hidden Dimension | 32 |
-| Delta Scale | ±5% per update |
-| Update Frequency | Every 10,000 steps |
-**Weight Evolution During Training:**
-| Task | Initial Weight | Final Weight | Change |
-|------|---------------|--------------|--------|
-| STS | 0.250 | 0.282 | +0.032 |
-| NLI | 0.300 | 0.337 | +0.037 |
-| QQP | 0.200 | 0.063 | -0.137 |
-| PAWS | 0.150 | 0.173 | +0.023 |
-| MRPC | 0.100 | 0.145 | +0.045 |
-The RL system learned to reduce QQP weight (already high-performing) while increasing weights for harder tasks.
-## Training Progress
-| Version | Step | Average Score | Best Task | Improvement |
-|---------|------|---------------|-----------|-------------|
-| v3.1 | 10,000 | 0.6133 | QQP (0.8093) | +0.6133 |
-| v3.2 | 20,000 | 0.6430 | QQP (0.8351) | +0.0297 |
-| v3.3 | 30,000 | 0.6813 | QQP (0.8391) | +0.0383 |
-| v3.4 | 40,000 | 0.6925 | QQP (0.8527) | +0.0111 |
-| v3.5 | 50,000 | 0.7099 | QQP (0.8579) | +0.0175 |
-| **v3.6** | **80,000** | **0.7134** | **QQP (0.8636)** | **+0.0035** |
-## Usage
-### Installation
-```bash
-pip install torch transformers
-```
-### Loading the Model
-```python
-import torch
-from transformers import AutoTokenizer, AutoModel
-# Load tokenizer and base model
-tokenizer = AutoTokenizer.from_pretrained("path/to/FireDevourerEmbedder-RL-v3.6")
-base_model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
-# Load checkpoint
-checkpoint = torch.load("path/to/FireDevourerEmbedder-RL-v3.6/full_checkpoint.pt")
-# Load model weights (you'll need to reconstruct the full model class)
-# See the training script for the complete FireDevourerEmbedder class definition
-```
-### Computing Embeddings
-```python
-def mean_pooling(model_output, attention_mask):
-    """Apply mean pooling to get sentence embeddings."""
-    token_embeddings = model_output[0]
-    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
-    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
-def get_embedding(text, model, tokenizer):
-    """Get sentence embedding for a single text."""
-    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
-    with torch.no_grad():
-        outputs = model(**inputs)
-    return mean_pooling(outputs, inputs["attention_mask"])
-# Example
-embedding = get_embedding("This is a sample sentence.", model, tokenizer)
-print(f"Embedding shape: {embedding.shape}")  # [1, 384]
-```
-### Computing Similarity
-```python
-from torch.nn.functional import cosine_similarity
-sentence1 = "A man is eating pizza"
-sentence2 = "A person is eating food"
-emb1 = get_embedding(sentence1, model, tokenizer)
-emb2 = get_embedding(sentence2, model, tokenizer)
-similarity = cosine_similarity(emb1, emb2)
-print(f"Similarity: {similarity.item():.4f}")  # ~0.9448
-```
-### Task-Specific Predictions
-```python
-# After loading the full model with task heads:
-def predict_nli(sentence1, sentence2, model, tokenizer):
-    """Predict entailment relationship."""
-    # Get embeddings for both sentences
-    emb1 = get_embedding(sentence1, model, tokenizer)
-    emb2 = get_embedding(sentence2, model, tokenizer)
-    # Combine embeddings (concatenate with element-wise difference and product)
-    combined = torch.cat([emb1, emb2, torch.abs(emb1 - emb2), emb1 * emb2], dim=-1)
-    # Pass through NLI head
-    logits = model.nli_head(combined)
-    prediction = torch.argmax(logits, dim=-1)
-    labels = ["Contradiction", "Neutral", "Entailment"]
-    return labels[prediction.item()]
-# Example
-result = predict_nli("It's raining outside", "The weather is sunny", model, tokenizer)
-print(f"NLI Prediction: {result}")  # Contradiction
-```
-## Evaluation Results
-### Test Suite Statistics (20 diverse test cases)
-**Cosine Similarity:**
-| Statistic | Value |
-|-----------|-------|
-| Mean | 0.8001 |
-| Std | 0.1562 |
-| Min | 0.3139 |
-| Max | 0.9831 |
-| Median | 0.8149 |
-**STS Score:**
-| Statistic | Value |
-|-----------|-------|
-| Mean | 0.5672 |
-| Std | 0.2270 |
-| Min | 0.0182 |
-| Max | 0.9468 |
-| Median | 0.5788 |
-### Example Predictions
-| Sentence 1 | Sentence 2 | Cosine Sim | NLI | Domain |
-|------------|------------|------------|-----|--------|
-| "A man is eating pizza" | "A person is eating food" | 0.9448 | Entailment | General |
-| "It's raining outside" | "The weather is sunny" | 0.7124 | Contradiction | Entailment |
-| "How do I learn Python?" | "What's the best way to learn Python?" | 0.8915 | Entailment | Questions |
-| "The quick brown fox jumps..." | "A fast brown fox leaps..." | 0.7837 | Entailment | General |
-## Intended Use
-### Best Use Cases
-- **Semantic Search**: Finding similar documents or passages
-- **Duplicate Detection**: Identifying duplicate questions or content
-- **Paraphrase Mining**: Finding paraphrased text pairs
-- **Clustering**: Grouping similar sentences or documents
-- **Natural Language Inference**: Determining textual entailment
-### Limitations
-- **STS-B Performance**: The model shows lower performance on fine-grained semantic similarity regression (0.3366). For tasks requiring precise similarity scores, consider using dedicated STS models.
-- **English Only**: Trained exclusively on English data.
-- **Max Length**: 512 tokens maximum input length.
-- **Adversarial Robustness**: While trained on PAWS adversarial data, performance on novel adversarial examples may vary.
-## Training Loss Progression
-| Epoch | STS Loss | NLI Loss | QQP Loss | PAWS Loss | MRPC Loss | Domain Loss | Total Loss |
-|-------|----------|----------|----------|-----------|-----------|-------------|------------|
-| 1 | 0.0073 | 0.2508 | 0.0742 | 0.0966 | 0.0287 | 0.0529 | 0.4977 |
-| 2 | 0.0038 | 0.1970 | 0.0430 | 0.0638 | 0.0025 | 0.0196 | 0.3211 |
-| 3 | 0.0031 | 0.1822 | 0.0221 | 0.0479 | 0.0009 | 0.0141 | 0.2631 |
-## Citation
-If you use this model in your research, please cite:
-```bibtex
-@misc{firedevourerembedder2025,
-  author = {Asad, Zain},
-  title = {FireDevourerEmbedder: Multi-Task Sentence Embeddings with RL-Adaptive Task Weighting},
-  year = {2025},
-  publisher = {Hugging Face},
-  url = {https://huggingface.co/zainasad/FireDevourerEmbedder-RL-v3.6}
-}
-```
-## Author
-**Zain Asad**
-## License
-MIT License