Updated Readme

README.md

@@ -1,488 +1,56 @@
 ---
+license: apache-2.0
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
 tags:
 - sentence-transformers
-- sentence-similarity
 - feature-extraction
-- dense
-- generated_from_trainer
-- dataset_size:3270
-- loss:OnlineContrastiveLoss
-- dataset_size:2044
-- dataset_size:1634
+- semantic-search
+- automotive-parts
+- synonyms
 base_model: sentence-transformers/all-MiniLM-L6-v2
-widget:
-- source_sentence: Door Chain
-  sentences:
-  - install new FILTER PAN COVER
-  - replace Burner Baffle
-  - install new Elbow - Fitting
-- source_sentence: install new Gasket
-  sentences:
-  - replace CIRCUIT FUSE
-  - CIRCUIT FUSE
-  - LWCO Board needs replacement
-- source_sentence: Hinge Cam
-  sentences:
-  - install new FILTER PAN COVER
-  - replace Rotary Switch
-  - Filter Dryer needs replacement
-- source_sentence: microwave
-  sentences:
-  - replace limit switch
-  - replace FILTER ENVELOPE
-  - unit
-- source_sentence: install new Actuator Switch
-  sentences:
-  - water arrestor needs replacement
-  - replace hi limit switch
-  - replace fan switch
-pipeline_tag: sentence-similarity
-library_name: sentence-transformers
-metrics:
-- pearson_cosine
-- spearman_cosine
-model-index:
-- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
-  results:
-  - task:
-      type: semantic-similarity
-      name: Semantic Similarity
-    dataset:
-      name: sts b
-      type: sts-b
-    metrics:
-    - type: pearson_cosine
-      value: 0.8696194628511225
-      name: Pearson Cosine
-    - type: spearman_cosine
-      value: 0.8671637425670679
-      name: Spearman Cosine
-  - task:
-      type: semantic-similarity
-      name: Semantic Similarity
-    dataset:
-      name: mtb
-      type: mtb
-    metrics:
-    - type: pearson_cosine
-      value: 0.8135998600118043
-      name: Pearson Cosine
-    - type: spearman_cosine
-      value: 0.7237156305132463
-      name: Spearman Cosine
 ---
 
-# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
-
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
-
-## Model Details
-
-### Model Description
-- **Model Type:** Sentence Transformer
-- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
-- **Maximum Sequence Length:** 256 tokens
-- **Output Dimensionality:** 384 dimensions
-- **Similarity Function:** Cosine Similarity
-<!-- - **Training Dataset:** Unknown -->
-<!-- - **Language:** Unknown -->
-<!-- - **License:** Unknown -->
-
-### Model Sources
-
-- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
-- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
-- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
-
-### Full Model Architecture
-
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
-  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
-  (2): Normalize()
-)
-```
-
-## Usage
-
-### Direct Usage (Sentence Transformers)
-
-First install the Sentence Transformers library:
-
-```bash
-pip install -U sentence-transformers
-```
-
-Then you can load this model and run inference.
-```python
-from sentence_transformers import SentenceTransformer
-
-# Download from the 🤗 Hub
-model = SentenceTransformer("sentence_transformers_model_id")
-# Run inference
-sentences = [
-    'install new Actuator Switch',
-    'replace fan switch',
-    'replace hi limit switch',
-]
-embeddings = model.encode(sentences)
-print(embeddings.shape)
-# [3, 384]
-
-# Get the similarity scores for the embeddings
-similarities = model.similarity(embeddings, embeddings)
-print(similarities)
-# tensor([[1.0000, 0.8321, 0.8193],
-#         [0.8321, 1.0000, 0.8154],
-#         [0.8193, 0.8154, 1.0000]])
-```
-
-<!--
-### Direct Usage (Transformers)
-
-<details><summary>Click to see the direct usage in Transformers</summary>
-
-</details>
--->
-
-<!--
-### Downstream Usage (Sentence Transformers)
-
-You can finetune this model on your own dataset.
-
-<details><summary>Click to expand</summary>
+# Automotive Parts Synonym Model
 
-</details>
--->
+A fine-tuned SentenceTransformer model specialized for finding synonyms and related terms in automotive parts and service descriptions.
 
-<!--
-### Out-of-Scope Use
+## Model Description
 
-*List how the model may foreseeably be misused and address what users ought not to do with the model.*
--->
+This model is fine-tuned from `sentence-transformers/all-MiniLM-L6-v2` specifically for automotive parts synonym detection. It can identify when different part names refer to the same or similar components.
 
-## Evaluation
+**Base Model:** `sentence-transformers/all-MiniLM-L6-v2`  
+**Output Dimensions:** 384  
+**Max Sequence Length:** 256 tokens  
 
-### Metrics
+## Intended Use
 
-#### Semantic Similarity
-
-* Datasets: `sts-b` and `mtb`
-* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
-
-| Metric              | sts-b      | mtb        |
-|:--------------------|:-----------|:-----------|
-| pearson_cosine      | 0.8696     | 0.8136     |
-| **spearman_cosine** | **0.8672** | **0.7237** |
-
-<!--
-## Bias, Risks and Limitations
-
-*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
--->
-
-<!--
-### Recommendations
-
-*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
--->
+- **Primary:** Finding synonymous automotive parts and service terms
+- **Secondary:** Semantic search for automotive repair descriptions
+- **Examples:**
+  - "Drive Motor" ↔ "gear motor"
+  - "install new Actuator Switch" ↔ "replace fan switch"
+  - "TXV Valve" ↔ "Expansion Valve"
 
 ## Training Details
 
-### Training Dataset
-
-#### Unnamed Dataset
-
-* Size: 1,634 training samples
-* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
-* Approximate statistics based on the first 1000 samples:
-  |         | sentence1                                                                       | sentence2                                                                        | label                                           |
-  |:--------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
-  | type    | string                                                                          | string                                                                           | int                                             |
-  | details | <ul><li>min: 3 tokens</li><li>mean: 4.34 tokens</li><li>max: 8 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 4.53 tokens</li><li>max: 10 tokens</li></ul> | <ul><li>0: ~18.30%</li><li>1: ~81.70%</li></ul> |
-* Samples:
-  | sentence1                    | sentence2                  | label          |
-  |:-----------------------------|:---------------------------|:---------------|
-  | <code>Expansion Valve</code> | <code>TXV Valve</code>     | <code>1</code> |
-  | <code>Drive Motor</code>     | <code>gear motor</code>    | <code>1</code> |
-  | <code>Motor Mount</code>     | <code>motor bracket</code> | <code>1</code> |
-* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
-
-### Evaluation Dataset
-
-#### Unnamed Dataset
-
-* Size: 818 evaluation samples
-* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
-* Approximate statistics based on the first 818 samples:
-  |         | sentence1                                                                        | sentence2                                                                        | label                                           |
-  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
-  | type    | string                                                                           | string                                                                           | int                                             |
-  | details | <ul><li>min: 3 tokens</li><li>mean: 5.53 tokens</li><li>max: 10 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.93 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>0: ~50.00%</li><li>1: ~50.00%</li></ul> |
-* Samples:
-  | sentence1                                  | sentence2                                   | label          |
-  |:-------------------------------------------|:--------------------------------------------|:---------------|
-  | <code>Power Relay needs replacement</code> | <code>relay needs replacement</code>        | <code>1</code> |
-  | <code>Target needs replacement</code>      | <code>ceramic tile needs replacement</code> | <code>1</code> |
-  | <code>install new Infinite Switch</code>   | <code>install new inf switch</code>         | <code>1</code> |
-* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
-
-### Training Hyperparameters
-#### Non-Default Hyperparameters
-
-- `eval_strategy`: epoch
-- `per_device_train_batch_size`: 256
-- `per_device_eval_batch_size`: 256
-- `learning_rate`: 1e-05
-- `num_train_epochs`: 4
-- `warmup_ratio`: 0.02
-- `bf16`: True
-- `load_best_model_at_end`: True
-
-#### All Hyperparameters
-<details><summary>Click to expand</summary>
-
-- `overwrite_output_dir`: False
-- `do_predict`: False
-- `eval_strategy`: epoch
-- `prediction_loss_only`: True
-- `per_device_train_batch_size`: 256
-- `per_device_eval_batch_size`: 256
-- `per_gpu_train_batch_size`: None
-- `per_gpu_eval_batch_size`: None
-- `gradient_accumulation_steps`: 1
-- `eval_accumulation_steps`: None
-- `torch_empty_cache_steps`: None
-- `learning_rate`: 1e-05
-- `weight_decay`: 0.0
-- `adam_beta1`: 0.9
-- `adam_beta2`: 0.999
-- `adam_epsilon`: 1e-08
-- `max_grad_norm`: 1.0
-- `num_train_epochs`: 4
-- `max_steps`: -1
-- `lr_scheduler_type`: linear
-- `lr_scheduler_kwargs`: {}
-- `warmup_ratio`: 0.02
-- `warmup_steps`: 0
-- `log_level`: passive
-- `log_level_replica`: warning
-- `log_on_each_node`: True
-- `logging_nan_inf_filter`: True
-- `save_safetensors`: True
-- `save_on_each_node`: False
-- `save_only_model`: False
-- `restore_callback_states_from_checkpoint`: False
-- `no_cuda`: False
-- `use_cpu`: False
-- `use_mps_device`: False
-- `seed`: 42
-- `data_seed`: None
-- `jit_mode_eval`: False
-- `use_ipex`: False
-- `bf16`: True
-- `fp16`: False
-- `fp16_opt_level`: O1
-- `half_precision_backend`: auto
-- `bf16_full_eval`: False
-- `fp16_full_eval`: False
-- `tf32`: None
-- `local_rank`: 0
-- `ddp_backend`: None
-- `tpu_num_cores`: None
-- `tpu_metrics_debug`: False
-- `debug`: []
-- `dataloader_drop_last`: False
-- `dataloader_num_workers`: 0
-- `dataloader_prefetch_factor`: None
-- `past_index`: -1
-- `disable_tqdm`: False
-- `remove_unused_columns`: True
-- `label_names`: None
-- `load_best_model_at_end`: True
-- `ignore_data_skip`: False
-- `fsdp`: []
-- `fsdp_min_num_params`: 0
-- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
-- `fsdp_transformer_layer_cls_to_wrap`: None
-- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
-- `deepspeed`: None
-- `label_smoothing_factor`: 0.0
-- `optim`: adamw_torch
-- `optim_args`: None
-- `adafactor`: False
-- `group_by_length`: False
-- `length_column_name`: length
-- `ddp_find_unused_parameters`: None
-- `ddp_bucket_cap_mb`: None
-- `ddp_broadcast_buffers`: False
-- `dataloader_pin_memory`: True
-- `dataloader_persistent_workers`: False
-- `skip_memory_metrics`: True
-- `use_legacy_prediction_loop`: False
-- `push_to_hub`: False
-- `resume_from_checkpoint`: None
-- `hub_model_id`: None
-- `hub_strategy`: every_save
-- `hub_private_repo`: None
-- `hub_always_push`: False
-- `hub_revision`: None
-- `gradient_checkpointing`: False
-- `gradient_checkpointing_kwargs`: None
-- `include_inputs_for_metrics`: False
-- `include_for_metrics`: []
-- `eval_do_concat_batches`: True
-- `fp16_backend`: auto
-- `push_to_hub_model_id`: None
-- `push_to_hub_organization`: None
-- `mp_parameters`: 
-- `auto_find_batch_size`: False
-- `full_determinism`: False
-- `torchdynamo`: None
-- `ray_scope`: last
-- `ddp_timeout`: 1800
-- `torch_compile`: False
-- `torch_compile_backend`: None
-- `torch_compile_mode`: None
-- `include_tokens_per_second`: False
-- `include_num_input_tokens_seen`: False
-- `neftune_noise_alpha`: None
-- `optim_target_modules`: None
-- `batch_eval_metrics`: False
-- `eval_on_start`: False
-- `use_liger_kernel`: False
-- `liger_kernel_config`: None
-- `eval_use_gather_object`: False
-- `average_tokens_across_devices`: False
-- `prompts`: None
-- `batch_sampler`: batch_sampler
-- `multi_dataset_batch_sampler`: proportional
-- `router_mapping`: {}
-- `learning_rate_mapping`: {}
-
-</details>
-
-### Training Logs
-| Epoch   | Step  | Training Loss | Validation Loss | sts-b_spearman_cosine | mtb_spearman_cosine |
-|:-------:|:-----:|:-------------:|:---------------:|:---------------------:|:-------------------:|
-| -1      | -1    | -             | -               | 0.8672                | 0.7237              |
-| 1.0     | 7     | -             | 28.7758         | -                     | -                   |
-| 2.0     | 14    | -             | 20.5461         | -                     | -                   |
-| 3.0     | 21    | -             | 14.5468         | -                     | -                   |
-| 4.0     | 28    | -             | 12.2789         | -                     | -                   |
-| 5.0     | 35    | -             | 7.4563          | -                     | -                   |
-| 6.0     | 42    | -             | 4.7709          | -                     | -                   |
-| 7.0     | 49    | -             | 3.7263          | -                     | -                   |
-| 8.0     | 56    | -             | 3.2799          | -                     | -                   |
-| 9.0     | 63    | -             | 3.4937          | -                     | -                   |
-| 10.0    | 70    | -             | 3.3956          | -                     | -                   |
-| 11.0    | 77    | -             | 3.2518          | -                     | -                   |
-| 12.0    | 84    | -             | 2.4912          | -                     | -                   |
-| 13.0    | 91    | -             | 1.7859          | -                     | -                   |
-| 14.0    | 98    | -             | 1.4185          | -                     | -                   |
-| 14.2857 | 100   | 9.9923        | -               | -                     | -                   |
-| 15.0    | 105   | -             | 1.4582          | -                     | -                   |
-| 16.0    | 112   | -             | 1.4355          | -                     | -                   |
-| 17.0    | 119   | -             | 1.2700          | -                     | -                   |
-| 18.0    | 126   | -             | 0.9766          | -                     | -                   |
-| 19.0    | 133   | -             | 0.9087          | -                     | -                   |
-| 20.0    | 140   | -             | 0.8227          | -                     | -                   |
-| 21.0    | 147   | -             | 0.7897          | -                     | -                   |
-| 22.0    | 154   | -             | 0.6956          | -                     | -                   |
-| 23.0    | 161   | -             | 0.7913          | -                     | -                   |
-| 24.0    | 168   | -             | 0.7902          | -                     | -                   |
-| 25.0    | 175   | -             | 0.7534          | -                     | -                   |
-| 26.0    | 182   | -             | 0.6562          | -                     | -                   |
-| 27.0    | 189   | -             | 0.5444          | -                     | -                   |
-| 28.0    | 196   | -             | 0.4464          | -                     | -                   |
-| 28.5714 | 200   | 0.2576        | -               | -                     | -                   |
-| 29.0    | 203   | -             | 0.4410          | -                     | -                   |
-| 30.0    | 210   | -             | 0.4314          | -                     | -                   |
-| 31.0    | 217   | -             | 0.3471          | -                     | -                   |
-| 32.0    | 224   | -             | 0.3472          | -                     | -                   |
-| 33.0    | 231   | -             | 0.3445          | -                     | -                   |
-| 34.0    | 238   | -             | 0.3404          | -                     | -                   |
-| 35.0    | 245   | -             | 0.3378          | -                     | -                   |
-| 36.0    | 252   | -             | 0.3370          | -                     | -                   |
-| 37.0    | 259   | -             | 0.3355          | -                     | -                   |
-| 38.0    | 266   | -             | 0.3339          | -                     | -                   |
-| 39.0    | 273   | -             | 0.3326          | -                     | -                   |
-| 40.0    | 280   | -             | 0.3328          | -                     | -                   |
-| 41.0    | 287   | -             | 0.3308          | -                     | -                   |
-| 42.0    | 294   | -             | 0.3308          | -                     | -                   |
-| 42.8571 | 300   | 0.1918        | -               | -                     | -                   |
-| 43.0    | 301   | -             | 0.3306          | -                     | -                   |
-| 44.0    | 308   | -             | 0.3304          | -                     | -                   |
-| 45.0    | 315   | -             | 0.3294          | -                     | -                   |
-| 46.0    | 322   | -             | 0.3295          | -                     | -                   |
-| 47.0    | 329   | -             | 0.3295          | -                     | -                   |
-| 48.0    | 336   | -             | 0.3297          | -                     | -                   |
-| 49.0    | 343   | -             | 0.3295          | -                     | -                   |
-| 50.0    | 350   | -             | 0.3295          | -                     | -                   |
-| **1.0** | **4** | **-**         | **0.191**       | **-**                 | **-**               |
-| 2.0     | 8     | -             | 0.1209          | -                     | -                   |
-| 3.0     | 12    | -             | 0.1106          | -                     | -                   |
-| 4.0     | 16    | -             | 0.1035          | -                     | -                   |
-| 5.0     | 20    | -             | 0.0995          | -                     | -                   |
-| 6.0     | 24    | -             | 0.0955          | -                     | -                   |
-| 7.0     | 28    | -             | 0.0587          | -                     | -                   |
-| 8.0     | 32    | -             | 0.0574          | -                     | -                   |
-| 9.0     | 36    | -             | 0.0             | -                     | -                   |
-| 10.0    | 40    | -             | 0.0             | -                     | -                   |
-| 11.0    | 44    | -             | 0.0             | -                     | -                   |
-| 12.0    | 48    | -             | 0.0             | -                     | -                   |
-| 12.5    | 50    | 0.4241        | -               | -                     | -                   |
-| 13.0    | 52    | -             | 0.0             | -                     | -                   |
-| 14.0    | 56    | -             | 0.0             | -                     | -                   |
-| 15.0    | 60    | -             | 0.0             | -                     | -                   |
-| 1.0     | 7     | -             | 0.0684          | -                     | -                   |
-| 2.0     | 14    | -             | 0.0             | -                     | -                   |
-| 3.0     | 21    | -             | 0.0             | -                     | -                   |
-| 4.0     | 28    | -             | 0.0             | -                     | -                   |
-
-* The bold row denotes the saved checkpoint.
-
-### Framework Versions
-- Python: 3.11.0rc1
-- Sentence Transformers: 5.0.0
-- Transformers: 4.53.1
-- PyTorch: 2.7.1+cu126
-- Accelerate: 1.8.1
-- Datasets: 3.6.0
-- Tokenizers: 0.21.2
-
-## Citation
-
-### BibTeX
-
-#### Sentence Transformers
-```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",
-    month = "11",
-    year = "2019",
-    publisher = "Association for Computational Linguistics",
-    url = "https://arxiv.org/abs/1908.10084",
-}
-```
-
-<!--
-## Glossary
+**Training Strategy:** 3-phase approach
+1. **Contextual Training** (30 epochs): Full phrases with synonyms/antonyms
+2. **Foundation Training** (15 epochs): Word-to-word synonyms/antonyms  
+3. **Real-world Fine-tuning** (4 epochs): Search phrases and repair descriptions
 
-*Clearly define terms in order to be accessible across audiences.*
--->
+**Loss Function:** OnlineContrastiveLoss with varying margins (0.6 → 0.4 → 0.4)  
+**Training Data:** Automotive parts synonym/antonym pairs with contextual repair descriptions  
+**LoRA:** Used for parameter-efficient fine-tuning (r=16, alpha=16)
 
-<!--
-## Model Card Authors
+## Performance
 
-*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
--->
+Evaluated on STS-B and MTB datasets:
+- **STS-B Spearman:** 0.867
+- **MTB Spearman:** 0.724
 
-<!--
-## Model Card Contact
+## Limitations
 
-*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--->
+- Optimized specifically for automotive parts and repair terminology
+- May not perform well on general-domain text
+- Best results on short phrases (3-10 tokens) typical of part names