Updating model weights

0452b39 verified 3 months ago

17.2 kB

	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- dense
	- generated_from_trainer
	- dataset_size:529974
	- loss:MultipleNegativesSymmetricRankingLoss
	base_model: sentence-transformers/all-MiniLM-L6-v2
	widget:
	- source_sentence: essence multi task concealer 15 natural nude
	sentences:
	- ahc vitamin c sheet mask
	- ' concealer'
	- face make-up
	- source_sentence: casa chandelier
	sentences:
	- hand braided chandelier
	- chandlier
	- disney princess belle styling head playset, brown hair
	- source_sentence: fender squier classic vibe '50s stratocaster, maple fingerboard
	sentences:
	- guitar
	- right handed guitar
	- endowments of the two holy sanctuaries
	- source_sentence: faber castell jumbo colored pencil, metallic copper
	sentences:
	- pencil
	- ' faber castell colored pencil'
	- the essential notebook-green
	- source_sentence: farm frites potato chips
	sentences:
	- juhayna - long life tangerine mandarin fruit drink - 1 l
	- farm frites chips
	- snacks
	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: 0.9672941565513611
	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/NewMiniLM-V21Data-128ConstantBATCH-SemanticEngine")
	# Run inference
	sentences = [
	'farm frites potato chips',
	'farm frites chips',
	'juhayna - long life tangerine mandarin fruit drink - 1 l',
	]
	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, 0.9611, 0.0994],
	# [0.9611, 1.0000, 0.0641],
	# [0.0994, 0.0641, 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 \| 0.9673 \|

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	## Training Details

	### Training Dataset

	#### Unnamed Dataset

	* Size: 529,974 training samples
	* Columns: <code>anchor</code>, <code>positive</code>, and <code>itemCategory</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| anchor \| positive \| itemCategory \|
	\|:--------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| details \| <ul><li>min: 3 tokens</li><li>mean: 10.64 tokens</li><li>max: 44 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 4.83 tokens</li><li>max: 105 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 3.88 tokens</li><li>max: 9 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \| itemCategory \|
	\|:----------------------------------------------------------\|:---------------------------------------------\|:----------------------\|
	\| <code>girls’ ski base layer top bl 100 black</code> \| <code>high neck top</code> \| <code>top</code> \|
	\| <code>lamar tom barista coffee milk no added sugar</code> \| <code>naturally sweetened almond milk</code> \| <code>dairy</code> \|
	\| <code>powder drink</code> \| <code>beverage</code> \| <code>beverage</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": false
	}
	```

	### Evaluation Dataset

	#### Unnamed Dataset

	* Size: 9,509 evaluation samples
	* Columns: <code>anchor</code>, <code>positive</code>, <code>negative</code>, and <code>itemCategory</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| anchor \| positive \| negative \| itemCategory \|
	\|:--------\|:---------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \| string \|
	\| details \| <ul><li>min: 3 tokens</li><li>mean: 9.63 tokens</li><li>max: 43 tokens</li></ul> \| <ul><li>min: 2 tokens</li><li>mean: 6.34 tokens</li><li>max: 150 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 9.35 tokens</li><li>max: 60 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 3.88 tokens</li><li>max: 10 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \| negative \| itemCategory \|
	\|:---------------------------------------------------------------------\|:-----------------------------------\|:-------------------------------------------------\|:------------------------------------\|
	\| <code>pilot mechanical pencil progrex h-127 - 0.7 mm</code> \| <code> mechanical pencil </code> \| <code>colorful sky bitch medal</code> \| <code>pencil</code> \|
	\| <code>superior drawing marker -pen - set of 12 colors - 2 nib</code> \| <code> nib marker pen</code> \| <code>plastic holder with capsule</code> \| <code>marker</code> \|
	\| <code>first person singular author: haruki murakami</code> \| <code>first person singular</code> \| <code>metal single rods - rustic cage rod</code> \| <code>literature and fiction</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": false
	}
	```

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

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 128
	- `per_device_eval_batch_size`: 128
	- `learning_rate`: 2e-05
	- `weight_decay`: 0.001
	- `num_train_epochs`: 5
	- `warmup_ratio`: 0.1
	- `fp16`: True
	- `dataloader_num_workers`: 1
	- `dataloader_prefetch_factor`: 2
	- `dataloader_persistent_workers`: True
	- `push_to_hub`: True
	- `hub_model_id`: LamaDiab/NewMiniLM-V21Data-128ConstantBATCH-SemanticEngine
	- `hub_strategy`: all_checkpoints

	#### 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`: 128
	- `per_device_eval_batch_size`: 128
	- `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`: 2e-05
	- `weight_decay`: 0.001
	- `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`: 0.1
	- `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`: 1
	- `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`: True
	- `skip_memory_metrics`: True
	- `use_legacy_prediction_loop`: False
	- `push_to_hub`: True
	- `resume_from_checkpoint`: None
	- `hub_model_id`: LamaDiab/NewMiniLM-V21Data-128ConstantBATCH-SemanticEngine
	- `hub_strategy`: all_checkpoints
	- `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 \| cosine_accuracy \|
	\|:------:\|:-----:\|:-------------:\|:---------------:\|:---------------:\|
	\| 0.0002 \| 1 \| 3.6713 \| - \| - \|
	\| 0.2415 \| 1000 \| 2.8183 \| 0.5858 \| 0.9434 \|
	\| 0.4830 \| 2000 \| 2.1179 \| 0.5328 \| 0.9497 \|
	\| 0.7245 \| 3000 \| 1.4826 \| 0.4932 \| 0.9538 \|
	\| 0.9660 \| 4000 \| 0.949 \| 0.4724 \| 0.9547 \|
	\| 1.2073 \| 5000 \| 1.1823 \| 0.4633 \| 0.9600 \|
	\| 1.4487 \| 6000 \| 1.1665 \| 0.4432 \| 0.9617 \|
	\| 1.6901 \| 7000 \| 1.1042 \| 0.4388 \| 0.9626 \|
	\| 1.9315 \| 8000 \| 1.0525 \| 0.4345 \| 0.9643 \|
	\| 2.1728 \| 9000 \| 0.9752 \| 0.4346 \| 0.9641 \|
	\| 2.4142 \| 10000 \| 0.9177 \| 0.4276 \| 0.9636 \|
	\| 2.6556 \| 11000 \| 0.9044 \| 0.4256 \| 0.9653 \|
	\| 2.8969 \| 12000 \| 0.8924 \| 0.4223 \| 0.9665 \|
	\| 3.1383 \| 13000 \| 0.8378 \| 0.4251 \| 0.9656 \|
	\| 3.3797 \| 14000 \| 0.831 \| 0.4247 \| 0.9662 \|
	\| 3.6210 \| 15000 \| 0.8012 \| 0.4249 \| 0.9660 \|
	\| 3.8624 \| 16000 \| 0.7952 \| 0.4210 \| 0.9661 \|
	\| 4.1038 \| 17000 \| 0.7858 \| 0.4188 \| 0.9666 \|
	\| 4.3452 \| 18000 \| 0.7592 \| 0.4181 \| 0.9672 \|
	\| 4.5865 \| 19000 \| 0.7562 \| 0.4184 \| 0.9674 \|
	\| 4.8279 \| 20000 \| 0.7512 \| 0.4192 \| 0.9673 \|


	### 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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