Updating model weights

ac4be89 verified 12 days ago

26.3 kB

	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- dense
	- generated_from_trainer
	- dataset_size:169967
	- loss:MultipleNegativesSymmetricRankingLoss
	base_model: sentence-transformers/all-MiniLM-L6-v2
	widget:
	- source_sentence: blue dianne
	sentences:
	- soap
	- maximize the freshness of your food for 12 hours with the blue dianne thermal
	bag. its triple compartments, spacious storage, heat resistance, and 100% leakproof
	design will keep it fresh. this bpa-free and pvc-free bag is also 100% non-toxic
	and comes with a 3-month guarantee. ideal for everyday food storage.
	- trolley backpack coral high colors 17 l 3 zippers 23977
	- source_sentence: the forty-fifth minute
	sentences:
	- 'literature book '
	- turkish dress.
	- the making of modern middle
	- source_sentence: snowflake pralines & cream
	sentences:
	- smoked turkey sandwich
	- walnut cupcake
	- chicken burrito
	- source_sentence: amytis indigo cushion
	sentences:
	- christian lacroix cushion
	- sealy boats 300 tc cotton bedsheet
	- boys lunch bag
	- source_sentence: hiit biker shorts - black
	sentences:
	- sweet pastry
	- black shorts
	- winter slippers for ladies christmas themed
	pipeline_tag: sentence-similarity
	library_name: sentence-transformers
	metrics:
	- cosine_accuracy
	model-index:
	- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
	results:
	- task:
	type: triplet
	name: Triplet
	dataset:
	name: Unknown
	type: unknown
	metrics:
	- type: cosine_accuracy
	value: 0.9472126364707947
	name: Cosine Accuracy
	---

	# 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/huggingface/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("LamaDiab/MiniLM-SemanticEngine")
	# Run inference
	sentences = [
	'hiit biker shorts - black',
	'black shorts',
	'winter slippers for ladies christmas themed',
	]
	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, 0.7103, -0.0705],
	# [ 0.7103, 1.0000, -0.0356],
	# [-0.0705, -0.0356, 1.0000]])
	```

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	## Evaluation

	### Metrics

	#### Triplet

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

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| cosine_accuracy \| 0.9472 \|

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	## Training Details

	### Training Dataset

	#### Unnamed Dataset

	* Size: 169,967 training samples
	* Columns: <code>anchor</code> and <code>positive</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| anchor \| positive \|
	\|:--------\|:----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 3 tokens</li><li>mean: 8.82 tokens</li><li>max: 237 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 14.99 tokens</li><li>max: 256 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \|
	\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:--------------------------\|
	\| <code>orasi barista almond milk is a premium, plant-based milk designed specifically for coffee lovers. crafted to create the perfect froth, it delivers a smooth and creamy texture that enhances the flavor of your lattes, cappuccinos, and other coffee drinks.</code> \| <code>groceries</code> \|
	\| <code>this toy is a "modern fashion" doll, combining beauty and innovation in its design. the doll has long and pink hair that adds a modern and attractive character to it. it comes with a wide variety of clothes and cool accessories that allow children to switch outfits and try different looks. <br>features: <br>modern and attractive design: the doll has a stylish and modern design that suits the tastes of children of different ages. <br>long and colorful hair: long and colorful hair gives the doll a distinctive and beautiful look, enhancing the possibilities of play and creativity. <br>wide range of clothes: the game has a large assortment of clothes that allow children to choose the appropriate outfits for the doll character according to their imagination. <br>multiple accessories: it comes with various accessories that add a touch of distinction and elegance to the doll, allowing to experiment with different styles. <br>stimulate creativity and imagination: the game helps enhance children's imagination by...</code> \| <code>kids</code> \|
	\| <code>zinnia ice box vivid gen.2 - blue</code> \| <code>blue ice box</code> \|
	* Loss: [<code>MultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativessymmetricrankingloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "cos_sim",
	"gather_across_devices": true
	}
	```

	### Evaluation Dataset

	#### Unnamed Dataset

	* Size: 16,216 evaluation samples
	* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| anchor \| positive \| negative \|
	\|:--------\|:---------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| details \| <ul><li>min: 3 tokens</li><li>mean: 9.79 tokens</li><li>max: 52 tokens</li></ul> \| <ul><li>min: 2 tokens</li><li>mean: 19.21 tokens</li><li>max: 256 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 9.76 tokens</li><li>max: 67 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \| negative \|
	\|:--------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------\|
	\| <code>dosado ring</code> \| <code>dosado or dos- à- dos: a wavy movement of two people around eachother, without turning & facing the same direction. material: 18k gold plated hammered brass. size: one size, adjustable. care instructions: to keep the jewelry pieces looking as good as new, please make sure that you store them in an airtight container. they should not come in contact with sweat, water or pefume, alcohol, sanitizers etc. polish with a microfiber cloth.</code> \| <code>kiprun ks light men's running shoes - black</code> \|
	\| <code>puzzle city of fog</code> \| <code>this amazing puzzle offers a unique opportunity to explore the beauty of san francisco, also known as the "city by the bay," through assembling a 2000-piece jigsaw. you'll immerse yourself in a world full of colors and details, as your eyes wander across the iconic golden gate bridge, towering buildings, distinctive hilly streets, and sailing ships in the harbor. it’s a panoramic depiction of san francisco, providing a comprehensive view of the city and its landmarks.<br>features:<br>explore san francisco: enjoy a virtual exploration of san francisco without leaving your home. get up close with famous landmarks such as the golden gate bridge and the harbor.<br>improves cognitive skills: assembling the puzzle enhances focus, memory, and fine motor skills while boosting problem-solving and decision-making abilities.<br>relaxation and stress relief: puzzle assembly is a fun and engaging activity that helps to relax and reduce stress, especially when concentrating on the appealing details of san franc...</code> \| <code>unicorn</code> \|
	\| <code>my fault series</code> \| <code>mercedes ron book</code> \| <code>sophie's world</code> \|
	* Loss: [<code>MultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativessymmetricrankingloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "cos_sim",
	"gather_across_devices": true
	}
	```

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

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 64
	- `per_device_eval_batch_size`: 64
	- `weight_decay`: 0.01
	- `num_train_epochs`: 5
	- `warmup_ratio`: 0.2
	- `fp16`: True
	- `dataloader_num_workers`: 2
	- `dataloader_prefetch_factor`: 2
	- `push_to_hub`: True
	- `hub_model_id`: LamaDiab/MiniLM-SemanticEngine
	- `batch_sampler`: no_duplicates

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

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: steps
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 64
	- `per_device_eval_batch_size`: 64
	- `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`: 0.01
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `max_grad_norm`: 1.0
	- `num_train_epochs`: 5
	- `max_steps`: -1
	- `lr_scheduler_type`: linear
	- `lr_scheduler_kwargs`: {}
	- `warmup_ratio`: 0.2
	- `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`: True
	- `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`: 2
	- `dataloader_prefetch_factor`: 2
	- `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`: True
	- `resume_from_checkpoint`: None
	- `hub_model_id`: LamaDiab/MiniLM-SemanticEngine
	- `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`: no_duplicates
	- `multi_dataset_batch_sampler`: proportional
	- `router_mapping`: {}
	- `learning_rate_mapping`: {}

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| Validation Loss \| cosine_accuracy \|
	\|:------:\|:-----:\|:-------------:\|:---------------:\|:---------------:\|
	\| 0.0004 \| 1 \| 1.6989 \| - \| - \|
	\| 0.1883 \| 500 \| 1.6103 \| 1.4441 \| 0.9124 \|
	\| 0.3765 \| 1000 \| 1.1942 \| 1.3155 \| 0.9233 \|
	\| 0.5648 \| 1500 \| 0.9831 \| 1.2584 \| 0.9257 \|
	\| 0.7530 \| 2000 \| 0.8867 \| 1.2368 \| 0.9254 \|
	\| 0.9413 \| 2500 \| 0.8094 \| 1.1874 \| 0.9274 \|
	\| 1.1295 \| 3000 \| 0.5818 \| 1.1431 \| 0.9348 \|
	\| 1.3178 \| 3500 \| 0.6978 \| 1.1291 \| 0.9374 \|
	\| 1.5060 \| 4000 \| 0.6652 \| 1.0936 \| 0.9389 \|
	\| 1.6943 \| 4500 \| 0.6287 \| 1.0889 \| 0.9369 \|
	\| 1.8825 \| 5000 \| 0.5986 \| 1.0780 \| 0.9404 \|
	\| 2.0708 \| 5500 \| 0.4376 \| 1.0783 \| 0.9386 \|
	\| 2.2590 \| 6000 \| 0.511 \| 1.0674 \| 0.9405 \|
	\| 2.4473 \| 6500 \| 0.4997 \| 1.0412 \| 0.9427 \|
	\| 2.6355 \| 7000 \| 0.4985 \| 1.0160 \| 0.9441 \|
	\| 2.8238 \| 7500 \| 0.4798 \| 1.0264 \| 0.9434 \|
	\| 3.0120 \| 8000 \| 0.3477 \| 1.0153 \| 0.9455 \|
	\| 3.2003 \| 8500 \| 0.4117 \| 1.0177 \| 0.9461 \|
	\| 3.3886 \| 9000 \| 0.4302 \| 1.0071 \| 0.9451 \|
	\| 3.5768 \| 9500 \| 0.4046 \| 1.0171 \| 0.9460 \|
	\| 3.7651 \| 10000 \| 0.414 \| 0.9819 \| 0.9474 \|
	\| 3.9533 \| 10500 \| 0.3786 \| 0.9982 \| 0.9463 \|
	\| 4.1416 \| 11000 \| 0.2952 \| 0.9920 \| 0.9461 \|
	\| 4.3298 \| 11500 \| 0.3655 \| 0.9959 \| 0.9455 \|
	\| 4.5181 \| 12000 \| 0.3655 \| 0.9961 \| 0.9464 \|
	\| 4.7063 \| 12500 \| 0.3662 \| 0.9826 \| 0.9467 \|
	\| 4.8946 \| 13000 \| 0.3545 \| 0.9864 \| 0.9472 \|


	### Framework Versions
	- Python: 3.11.13
	- Sentence Transformers: 5.1.2
	- Transformers: 4.53.3
	- PyTorch: 2.6.0+cu124
	- Accelerate: 1.9.0
	- Datasets: 4.4.1
	- 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",
	}
	```

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