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1_Pooling/config.json

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+{
+    "word_embedding_dimension": 768,
+    "pooling_mode_cls_token": true,
+    "pooling_mode_mean_tokens": false,
+    "pooling_mode_max_tokens": false,
+    "pooling_mode_mean_sqrt_len_tokens": false,
+    "pooling_mode_weightedmean_tokens": false,
+    "pooling_mode_lasttoken": false,
+    "include_prompt": true
+}

Information-Retrieval_evaluation_vmware-dev_results.csv

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README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- information-retrieval
+- semantic-search
+base_model: BAAI/bge-base-en-v1.5
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+license: mit
+language:
+- en
+metrics:
+- ndcg
+- recall
+- precision
+---
+
+# VMware Technical Documentation Embeddings
+
+A specialized sentence-transformers model fine-tuned for semantic search and information retrieval in technical documentation, with a focus on enterprise infrastructure and virtualization technologies.
+
+## Model Details
+
+### Description
+
+This model extends [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) with domain-specific fine-tuning for technical documentation retrieval. It generates 768-dimensional dense embeddings optimized for semantic similarity in enterprise technology contexts.
+
+- **Model Type:** Sentence Transformer (BERT-based)
+- **Base Model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5)
+- **Embedding Dimension:** 768
+- **Max Sequence Length:** 512 tokens
+- **Language:** English
+- **License:** MIT
+
+### Intended Use
+
+**Primary Use Cases:**
+- Semantic search over technical documentation
+- Information retrieval for enterprise infrastructure queries
+- RAG (Retrieval-Augmented Generation) pipelines
+- Technical support knowledge bases
+- Enterprise search systems
+
+**Optimized For:**
+- Natural language queries about technical topics
+- Documentation retrieval and ranking
+- Question answering systems
+- Knowledge management platforms
+
+### Out-of-Scope
+
+This model is specialized for technical documentation and may not perform optimally for:
+- General domain text
+- Non-English languages
+- Code search or generation
+- Creative writing or entertainment content
+
+## Quick Start
+
+### Installation
+
+```bash
+pip install sentence-transformers
+```
+
+### Basic Usage
+
+```python
+from sentence_transformers import SentenceTransformer, util
+
+# Load model
+model = SentenceTransformer('your-username/vmware-embeddings-large-v1')
+
+# Example queries and documents
+queries = [
+    "How to configure high availability?",
+    "Steps to install guest tools"
+]
+
+documents = [
+    "High availability can be configured through the management interface...",
+    "To install guest tools, first mount the ISO image..."
+]
+
+# Generate embeddings
+query_embeddings = model.encode(queries)
+doc_embeddings = model.encode(documents)
+
+# Calculate similarity
+similarities = util.cos_sim(query_embeddings, doc_embeddings)
+print(similarities)
+```
+
+### Semantic Search Example
+
+```python
+from sentence_transformers import SentenceTransformer, util
+
+model = SentenceTransformer('your-username/vmware-embeddings-large-v1')
+
+# Your document corpus
+corpus = [
+    "Documentation about high availability features...",
+    "Guide for load balancing configuration...",
+    "Instructions for live migration procedures..."
+]
+
+# Encode corpus
+corpus_embeddings = model.encode(corpus, convert_to_tensor=True)
+
+# Query
+query = "How to enable high availability?"
+query_embedding = model.encode(query, convert_to_tensor=True)
+
+# Search
+hits = util.semantic_search(query_embedding, corpus_embeddings, top_k=3)
+
+# Display results
+for hit in hits[0]:
+    print(f"Score: {hit['score']:.4f}")
+    print(f"Document: {corpus[hit['corpus_id']]}\n")
+```
+
+
+## Performance
+
+### Evaluation Metrics
+
+Evaluated on a held-out test set of 2,000 diverse technical queries:
+
+| Metric | Base Model | Fine-tuned | Improvement |
+|--------|-----------|------------|-------------|
+| **Recall@1** | 0.637 | **0.759** | +19.2% |
+| **Recall@3** | 0.805 | **0.927** | +15.2% |
+| **Recall@5** | 0.853 | **0.956** | +12.1% |
+| **Recall@10** | 0.906 | **0.979** | +8.0% |
+| **NDCG@10** | 0.775 | **0.879** | +13.4% |
+
+### Key Performance Indicators
+
+- ✅ **75.9%** top-1 accuracy
+- ✅ **92.7%** top-3 recall
+- ✅ **97.9%** top-10 recall
+- ✅ **0.879** NDCG@10 (excellent ranking quality)
+
+### Comparison with Base Model
+
+The fine-tuned model shows consistent improvements across all metrics:
+- Higher recall at all k values
+- Better ranking quality (NDCG)
+- More accurate top-1 predictions
+
+## Training Details
+
+### Training Configuration
+
+- **Framework:** sentence-transformers
+- **Loss Function:** MultipleNegativesRankingLoss
+- **Training Strategy:** Contrastive learning with hard negative mining
+- **Epochs:** 1
+- **Batch Size:** 64
+- **Learning Rate:** 2e-5
+- **Training Samples:** 671,972 query-document pairs
+- **Precision:** FP16
+- **Hardware:** NVIDIA RTX A6000 (49GB VRAM)
+
+### Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True})
+  (1): Pooling({'pooling_mode_cls_token': True})
+  (2): Normalize()
+)
+```
+
+## Limitations
+
+### Known Limitations
+
+- **Domain-Specific:** Optimized for technical documentation; general domain performance not guaranteed
+- **English Only:** No multi-language support
+- **Context Length:** Limited to 512 tokens
+- **Recency:** Knowledge current as of training date
+
+### Recommendations
+
+For optimal results:
+
+1. **Query Formulation:**
+   - Use natural language questions
+   - Include relevant technical terms
+   - Keep queries under 512 tokens
+
+2. **Hybrid Search:**
+   - Combine with keyword search (BM25) for best results
+   - Use semantic search for understanding, keyword for precision
+
+3. **Batch Processing:**
+   - Use `encode(..., batch_size=32)` for large collections
+   - Enable `convert_to_tensor=True` for GPU acceleration
+
+4. **Reranking:**
+   - Consider using a cross-encoder for final reranking
+   - Retrieve top-100 with this model, rerank to top-10
+
+## Technical Specifications
+
+### Model Information
+
+- **Parameters:** ~110M
+- **Architecture:** BERT-base
+- **Pooling:** CLS token
+- **Normalization:** L2
+- **Similarity Function:** Cosine similarity
+
+### Performance Benchmarks
+
+| Hardware | Batch Size | Throughput |
+|----------|-----------|------------|
+| RTX 3090 | 32 | ~850 docs/sec |
+| A100 | 128 | ~2,100 docs/sec |
+| CPU (16 cores) | 8 | ~180 docs/sec |
+
+### Resource Requirements
+
+**Minimum:**
+- GPU: 4GB VRAM (batch size 16)
+- CPU: 4 cores, 8GB RAM
+
+**Recommended:**
+- GPU: 8GB+ VRAM (batch size 32+)
+- CPU: 8+ cores, 16GB+ RAM
+
+## Citation
+
+```bibtex
+@misc{vmware-embeddings-2024,
+  author = {Your Name},
+  title = {VMware Technical Documentation Embeddings},
+  year = {2024},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/your-username/vmware-embeddings-large-v1}}
+}
+```
+
+### Base Model Citation
+
+```bibtex
+@misc{bge-base-en-v1.5,
+  author = {BAAI},
+  title = {BGE Base English v1.5},
+  year = {2023},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/BAAI/bge-base-en-v1.5}}
+}
+```
+
+## Acknowledgments
+
+- **Base Model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) by Beijing Academy of Artificial Intelligence
+- **Framework:** [sentence-transformers](https://www.sbert.net/) by UKPLab
+
+## License
+
+MIT License
+
+Copyright (c) 2024 [Your Name]
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+---
+
+**Note:** This model is intended for research and development. For production use, ensure compliance with your organization's policies and applicable regulations.

config.json

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+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.57.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "5.1.2",
+    "transformers": "4.57.3",
+    "pytorch": "2.9.1+cu128"
+  },
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

eval/Information-Retrieval_evaluation_vmware-dev_results.csv

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model.safetensors

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+size 437951328

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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+{
+    "max_seq_length": 512,
+    "do_lower_case": true
+}

special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}