Create README.md

README.md

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