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CECorrelationEvaluator_sts-validation_results.csv ADDED
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+ # ModernBERT Cross-Encoder: Semantic Similarity (STS)
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+
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+ Cross encoders are high performing encoder models that compare two texts and output a 0-1 score.
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+ I've found the `cross-encoders/roberta-large-stsb` model to be very useful in creating evaluators for LLM outputs.
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+ They're simple to use, fast and very accurate.
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+
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+ Like many people, I was excited about the architecture and training uplift from the ModernBERT architecture (`answerdotai/ModernBERT-base`).
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+ So I've applied it to the stsb cross encoder, which is a very handy model. Additionally, I've added
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+ pretraining from my much larger semi-synthetic dataset `dleemiller/wiki-sim` that targets this kind of objective.
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+
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+ ---
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+
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+ ## Features
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+ - **High performing:** Achieves **Pearson: 0.9162** and **Spearman: 0.9122** on the STS-Benchmark test set.
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+ - **Efficient architecture:** Based on the ModernBERT-base design (149M parameters), offering faster inference speeds.
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+ - **Extended context length:** Processes sequences up to 8192 tokens, great for LLM output evals.
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+ - **Diversified training:** Pretrained on `dleemiller/wiki-sim` and fine-tuned on `sentence-transformers/stsb`.
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+
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+ ---
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+
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+ ## Performance
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+
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+ | Model | STS-B Test Pearson | STS-B Test Spearman | Context Length | Parameters | Speed* |
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+ |--------------------------------|--------------------|---------------------|----------------|------------|---------|
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+ | **ModernCE-base-sts** | **0.9162** | **0.9122** | **8192** | 149M | **Fast** |
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+ | `roberta-large-stsb` | 0.9147 | 0.9115 | 512 | 355M | Slow |
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+ | `distilroberta-base-stsb` | 0.8792 | 0.8765 | 512 | 66M | Fast |
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+
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+
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+ ---
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+
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+ ## Usage
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+
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+ To use ModernCE for semantic similarity tasks, you can load the model with the Hugging Face `sentence-transformers` library:
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+
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+ ```python
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+ from sentence_transformers import CrossEncoder
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+
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+ # Load ModernCE model
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+ model = CrossEncoder("dleemiller/ModernCE-base-sts")
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+
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+ # Predict similarity scores for sentence pairs
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+ sentence_pairs = [
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+ ("It's a wonderful day outside.", "It's so sunny today!"),
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+ ("It's a wonderful day outside.", "He drove to work earlier."),
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+ ]
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+ scores = model.predict(sentence_pairs)
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+
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+ print(scores) # Outputs: array([0.9184, 0.0123], dtype=float32)
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+ ```
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+
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+ ### Output
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+ The model returns similarity scores in the range `[0, 1]`, where higher scores indicate stronger semantic similarity.
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+
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+ ---
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+
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+ ## Training Details
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+
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+ ### Pretraining
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+ The model was pretrained on the `pair-score-sampled` subset of the [`dleemiller/wiki-sim`](https://huggingface.co/datasets/dleemiller/wiki-sim) dataset. This dataset provides diverse sentence pairs with semantic similarity scores, helping the model build a robust understanding of relationships between sentences.
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+ - **Classifier Dropout:** 0.3, to introduce regularization and reduce overfitting.
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+ - **Objective:** STS-B scores from `roberta-large-stsb`.
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+
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+ ### Fine-Tuning
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+ Fine-tuning was performed on the [`sentence-transformers/stsb`](https://huggingface.co/datasets/sentence-transformers/stsb) dataset.
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+
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+ ### Validation Results
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+ The model achieved the following test set performance after fine-tuning:
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+ - **Pearson Correlation:** 0.9162
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+ - **Spearman Correlation:** 0.9122
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+
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+ Logs for training and evaluation are included in the [training logs](output/eval/sts-test-results.csv).
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+
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+ ---
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+
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+ ## Applications
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+
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+ 1. **Semantic Search:** Retrieve relevant documents or text passages based on query similarity.
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+ 2. **Retrieval-Augmented Generation (RAG):** Enhance generative models by providing contextually relevant information.
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+ 3. **Content Moderation:** Automatically classify or rank content based on similarity to predefined guidelines.
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+ 4. **Code Search:** Leverage the model's ability to understand code and natural language for large-scale programming tasks.
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+
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+ ---
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+
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+ ## Model Card
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+
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+ - **Architecture:** ModernBERT-base
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+ - **Tokenizer:** Custom tokenizer trained with modern techniques for long-context handling.
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+ - **Pretraining Data:** `dleemiller/wiki-sim (pair-score-sampled)`
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+ - **Fine-Tuning Data:** `sentence-transformers/stsb`
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+
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+ ---
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+
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+ ## Thank You
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+
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+ Thanks to the AnswerAI team for providing the ModernBERT models, and the Sentence Transformers team for their leadership in transformer encoder models.
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+
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+ ---
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+
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+ ## Citation
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+
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+ If you use this model in your research, please cite:
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+
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+ ```bibtex
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+ @misc{moderncestsb2025,
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+ author = {Miller, D. Lee},
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+ title = {ModernCE STS: An STS cross encoder model},
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+ year = {2025},
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+ publisher = {Hugging Face Hub},
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+ url = {https://huggingface.co/dleemiller/ModernCE-base-sts},
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+ }
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+ ```
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+
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+ ---
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+
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+ ## License
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+
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+ This model is licensed under the [MIT License](LICENSE).
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