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--- |
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license: apache-2.0 |
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base_model: sentence-transformers/all-MiniLM-L6-v2 |
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library_name: sentence-transformers |
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pipeline_tag: sentence-similarity |
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--- |
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# HAI - HelpingAI Semantic Similarity Model |
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This is a **custom Sentence Transformer model** fine-tuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). Designed as part of the **HelpingAI ecosystem**, it enhances **semantic similarity and contextual understanding**, with an emphasis on **emotionally intelligent responses**. |
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## Model Highlights |
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- **Base Model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) |
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## Model Details |
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### Features: |
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- **Input Dimensionality:** Handles up to 256 tokens per input. |
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- **Output Dimensionality:** 384-dimensional dense embeddings. |
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### Full Architecture |
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```python |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) |
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(1): Pooling({'pooling_mode_mean_tokens': True}) |
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(2): Normalize() |
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) |
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``` |
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## Training Overview |
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### Dataset: |
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- **Size:** 75897 samples |
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- **Structure:** `<sentence_0, sentence_1, similarity_score>` |
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- **Labels:** Float values between 0 (no similarity) and 1 (high similarity). |
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### Training Method: |
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- **Loss Function:** Cosine Similarity Loss |
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- **Batch Size:** 16 |
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- **Epochs:** 20 |
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- **Optimization:** AdamW optimizer with a learning rate of `5e-5`. |
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## Getting Started |
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### Installation |
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Ensure you have the `sentence-transformers` library installed: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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### Quick Start |
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Load and use the model in your Python environment: |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Load the HelpingAI semantic similarity model |
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model = SentenceTransformer("HelpingAI/HAI") |
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# Encode sentences |
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sentences = [ |
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"A woman is slicing a pepper.", |
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"A girl is styling her hair.", |
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"The sun is shining brightly today." |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) # Output: (3, 384) |
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# Calculate similarity |
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from sklearn.metrics.pairwise import cosine_similarity |
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similarity_scores = cosine_similarity([embeddings[0]], embeddings[1:]) |
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print(similarity_scores) |
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``` |
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high accuracy in sentiment-informed response tests. |
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## Citation |
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If you use the HAI model, please cite the original Sentence-BERT paper: |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
