smart-tab-embedding-fine-tuned / README.md

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	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:429643
	- loss:CosineSimilarityLoss
	base_model: sentence-transformers/all-MiniLM-L6-v2
	widget:
	- source_sentence: Oracle Cloud - Infrastructure and Platform Services for Enterprises
	sentences:
	- PulseAudio - Ubuntu Wiki
	- Documentation page not found - Read the Docs
	- Dwarf Fortress beginner tips - Video Games on Sports Illustrated
	- source_sentence: Suggest opt in User Test - Google Slides
	sentences:
	- ReleaseEngineering/TryServer - MozillaWiki
	- Dwarf Fortress beginner tips - Video Games on Sports Illustrated
	- Tutanota - Private Mailbox with End-to-End Encryption and Calendar
	- source_sentence: https://portal.naviabenefits.com/part/prioritytasks.aspx
	sentences:
	- What to Expect - Pregnancy and Parenting Tips, Week-by-Week Guides
	- Parents.com - Articles, Recipes, and Ideas for Family Activities
	- Pinterest - Boards for Collecting and Sharing Inspiration on Any Topic
	- source_sentence: ‎Apple Music - Web Player
	sentences:
	- BMW Connected Drive - Home Assistant
	- Mary Stewart Phillips (1862-1928) - Find a Grave Memorial
	- Sky Sports - Football, Formula 1, Cricket, and More
	- source_sentence: Tidal - High-Fidelity Music Streaming with Master Quality Audio
	sentences:
	- Walmart - Everyday Low Prices on Groceries, Electronics, and More
	- Notion - Integrated Workspace for Notes, Tasks, Databases, and Wikis
	- Ambient Dreams Playlist on Amazon Music
	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: Unknown
	type: unknown
	metrics:
	- type: pearson_cosine
	value: 0.9822505655251419
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.2607864200673379
	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 fa97f6e7cb1a59073dff9e6b13e2715cf7475ac9 -->
	- 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}) with Transformer model: 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("vazish/all-MiniLM-L6-v2-fine-tuned_0")
	# Run inference
	sentences = [
	'Tidal - High-Fidelity Music Streaming with Master Quality Audio',
	'Walmart - Everyday Low Prices on Groceries, Electronics, and More',
	'Notion - Integrated Workspace for Notes, Tasks, Databases, and Wikis',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 384]

	# Get the similarity scores for the embeddings
	similarities = model.similarity(embeddings, embeddings)
	print(similarities.shape)
	# [3, 3]
	```

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	## Evaluation

	### Metrics

	#### Semantic Similarity

	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.9823 \|
	\| spearman_cosine \| 0.2608 \|

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	## Training Details

	### Training Dataset

	#### Unnamed Dataset

	* Size: 49,800 training samples
	* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence_0 \| sentence_1 \| label \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:---------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 10 tokens</li><li>mean: 14.76 tokens</li><li>max: 21 tokens</li></ul> \| <ul><li>min: 10 tokens</li><li>mean: 14.64 tokens</li><li>max: 21 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.04</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \| label \|
	\|:----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------\|:-----------------\|
	\| <code>TripAdvisor - Hotel Reviews, Photos, and Travel Forums</code> \| <code>Docker Hub - Container Image Repository for DevOps Environments</code> \| <code>0.0</code> \|
	\| <code>Mastodon - Decentralized Social Media for Niche Communities</code> \| <code>Allrecipes - User-Submitted Recipes, Reviews, and Cooking Tips</code> \| <code>0.0</code> \|
	\| <code>YouTube Music - Music Videos, Official Albums, and Live Performances</code> \| <code>ESPN - Sports News, Live Scores, Stats, and Highlights</code> \| <code>0.0</code> \|
	* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
	```json
	{
	"loss_fct": "torch.nn.modules.loss.MSELoss"
	}
	```

	### Training Hyperparameters
	#### Non-Default Hyperparameters

	- `per_device_train_batch_size`: 32
	- `per_device_eval_batch_size`: 32
	- `multi_dataset_batch_sampler`: round_robin

	#### All Hyperparameters
	<details><summary>Click to expand</summary>

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: no
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 32
	- `per_device_eval_batch_size`: 32
	- `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`: 5e-05
	- `weight_decay`: 0.0
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `max_grad_norm`: 1
	- `num_train_epochs`: 3
	- `max_steps`: -1
	- `lr_scheduler_type`: linear
	- `lr_scheduler_kwargs`: {}
	- `warmup_ratio`: 0.0
	- `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`: False
	- `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`: False
	- `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
	- `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
	- `dispatch_batches`: None
	- `split_batches`: 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
	- `eval_use_gather_object`: False
	- `average_tokens_across_devices`: False
	- `prompts`: None
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: round_robin

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| spearman_cosine \|
	\|:------:\|:-----:\|:-------------:\|:---------------:\|
	\| 0.0372 \| 500 \| 0.0218 \| - \|
	\| 0.0745 \| 1000 \| 0.0151 \| - \|
	\| 0.1117 \| 1500 \| 0.0113 \| - \|
	\| 0.1490 \| 2000 \| 0.0076 \| - \|
	\| 0.1862 \| 2500 \| 0.0063 \| - \|
	\| 0.2234 \| 3000 \| 0.0054 \| - \|
	\| 0.2607 \| 3500 \| 0.0045 \| - \|
	\| 0.2979 \| 4000 \| 0.0041 \| - \|
	\| 0.3351 \| 4500 \| 0.0027 \| - \|
	\| 0.3724 \| 5000 \| 0.0028 \| - \|
	\| 0.4096 \| 5500 \| 0.0026 \| - \|
	\| 0.4469 \| 6000 \| 0.0021 \| - \|
	\| 0.4841 \| 6500 \| 0.0019 \| - \|
	\| 0.5213 \| 7000 \| 0.0022 \| - \|
	\| 0.5586 \| 7500 \| 0.0017 \| - \|
	\| 0.5958 \| 8000 \| 0.0018 \| - \|
	\| 0.6331 \| 8500 \| 0.0015 \| - \|
	\| 0.6703 \| 9000 \| 0.0015 \| - \|
	\| 0.7075 \| 9500 \| 0.0018 \| - \|
	\| 0.7448 \| 10000 \| 0.0014 \| - \|
	\| 0.7820 \| 10500 \| 0.0017 \| - \|
	\| 0.8192 \| 11000 \| 0.0012 \| - \|
	\| 0.8565 \| 11500 \| 0.0014 \| - \|
	\| 0.8937 \| 12000 \| 0.001 \| - \|
	\| 0.9310 \| 12500 \| 0.0011 \| - \|
	\| 0.9682 \| 13000 \| 0.001 \| - \|
	\| 1.0054 \| 13500 \| 0.0009 \| - \|
	\| 1.0427 \| 14000 \| 0.0011 \| - \|
	\| 1.0799 \| 14500 \| 0.001 \| - \|
	\| 1.1172 \| 15000 \| 0.0009 \| - \|
	\| 1.1544 \| 15500 \| 0.0008 \| - \|
	\| 1.1916 \| 16000 \| 0.001 \| - \|
	\| 1.2289 \| 16500 \| 0.0011 \| - \|
	\| 1.2661 \| 17000 \| 0.0011 \| - \|
	\| 1.3033 \| 17500 \| 0.0006 \| - \|
	\| 1.3406 \| 18000 \| 0.0011 \| - \|
	\| 1.3778 \| 18500 \| 0.0008 \| - \|
	\| 1.4151 \| 19000 \| 0.0011 \| - \|
	\| 1.4523 \| 19500 \| 0.0009 \| - \|
	\| 1.4895 \| 20000 \| 0.0011 \| - \|
	\| 1.5268 \| 20500 \| 0.0009 \| - \|
	\| 1.5640 \| 21000 \| 0.0009 \| - \|
	\| 1.6013 \| 21500 \| 0.0008 \| - \|
	\| 1.6385 \| 22000 \| 0.0005 \| - \|
	\| 1.6757 \| 22500 \| 0.001 \| - \|
	\| 1.7130 \| 23000 \| 0.0008 \| - \|
	\| 1.7502 \| 23500 \| 0.0007 \| - \|
	\| 1.7874 \| 24000 \| 0.0007 \| - \|
	\| 1.8247 \| 24500 \| 0.0008 \| - \|
	\| 1.8619 \| 25000 \| 0.001 \| - \|
	\| 1.8992 \| 25500 \| 0.0009 \| - \|
	\| 1.9364 \| 26000 \| 0.0008 \| - \|
	\| 1.9736 \| 26500 \| 0.0009 \| - \|
	\| 2.0109 \| 27000 \| 0.0007 \| - \|
	\| 2.0481 \| 27500 \| 0.0006 \| - \|
	\| 2.0854 \| 28000 \| 0.0007 \| - \|
	\| 2.1226 \| 28500 \| 0.0006 \| - \|
	\| 2.1598 \| 29000 \| 0.0007 \| - \|
	\| 2.1971 \| 29500 \| 0.001 \| - \|
	\| 2.2343 \| 30000 \| 0.0006 \| - \|
	\| 2.2715 \| 30500 \| 0.0006 \| - \|
	\| 2.3088 \| 31000 \| 0.001 \| - \|
	\| 2.3460 \| 31500 \| 0.0007 \| - \|
	\| 2.3833 \| 32000 \| 0.0008 \| - \|
	\| 2.4205 \| 32500 \| 0.0006 \| - \|
	\| 2.4577 \| 33000 \| 0.0007 \| - \|
	\| 2.4950 \| 33500 \| 0.0007 \| - \|
	\| 2.5322 \| 34000 \| 0.001 \| - \|
	\| 2.5694 \| 34500 \| 0.0007 \| - \|
	\| 2.6067 \| 35000 \| 0.0007 \| - \|
	\| 2.6439 \| 35500 \| 0.0008 \| - \|
	\| 2.6812 \| 36000 \| 0.0007 \| - \|
	\| 2.7184 \| 36500 \| 0.0006 \| - \|
	\| 2.7556 \| 37000 \| 0.0007 \| - \|
	\| 2.7929 \| 37500 \| 0.0007 \| - \|
	\| 2.8301 \| 38000 \| 0.0005 \| - \|
	\| 2.8674 \| 38500 \| 0.0009 \| - \|
	\| 2.9046 \| 39000 \| 0.0006 \| - \|
	\| 2.9418 \| 39500 \| 0.0007 \| - \|
	\| 2.9791 \| 40000 \| 0.0008 \| - \|
	\| -1 \| -1 \| - \| 0.2608 \|


	### Framework Versions
	- Python: 3.11.11
	- Sentence Transformers: 3.4.1
	- Transformers: 4.48.2
	- PyTorch: 2.5.1+cu124
	- Accelerate: 1.3.0
	- Datasets: 3.2.0
	- Tokenizers: 0.21.0

	## 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",
	}
	```

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