Add new SentenceTransformer model.

488d0c8 verified 5 months ago

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	---
	language:
	- en
	license: apache-2.0
	library_name: sentence-transformers
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:1625
	- loss:CosineSimilarityLoss
	base_model: BAAI/bge-base-en-v1.5
	datasets: []
	metrics:
	- pearson_cosine
	- spearman_cosine
	- pearson_manhattan
	- spearman_manhattan
	- pearson_euclidean
	- spearman_euclidean
	- pearson_dot
	- spearman_dot
	- pearson_max
	- spearman_max
	widget:
	- source_sentence: Boron Steel
	sentences:
	- Rock Bit
	- Spalling Test
	- Excavator Bucket
	- source_sentence: Friction Wear
	sentences:
	- Tool Steel
	- Medium Carbon Steel
	- Diffusion Bonding
	- source_sentence: Delamination
	sentences:
	- Subsea Christmas Tree
	- Low Alloyed Steel
	- Screw Conveyors
	- source_sentence: Nitriding
	sentences:
	- Subsea Manifold
	- Trencher Chain
	- Cylinder
	- source_sentence: Corrosion Resistant Coatings
	sentences:
	- Mower Blade
	- Gas Metal Arc Welding (GMAW)
	- Corrosion Resistant Coatings
	pipeline_tag: sentence-similarity
	model-index:
	- name: BGE base Financial Matryoshka
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: dim 768
	type: dim_768
	metrics:
	- type: pearson_cosine
	value: 0.9548051644723275
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.6620048542679903
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.985909077336812
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.6620048542679903
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.9863519709955113
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.6620048542679903
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.9548051701614557
	name: Pearson Dot
	- type: spearman_dot
	value: 0.6610658947764548
	name: Spearman Dot
	- type: pearson_max
	value: 0.9863519709955113
	name: Pearson Max
	- type: spearman_max
	value: 0.6620048542679903
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: dim 512
	type: dim_512
	metrics:
	- type: pearson_cosine
	value: 0.9544417196413574
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.6620048542679903
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.9855825558550574
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.6620048542679903
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.9862004412296757
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.6620048542679903
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.9501184326722917
	name: Pearson Dot
	- type: spearman_dot
	value: 0.6607798700248341
	name: Spearman Dot
	- type: pearson_max
	value: 0.9862004412296757
	name: Pearson Max
	- type: spearman_max
	value: 0.6620048542679903
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: dim 256
	type: dim_256
	metrics:
	- type: pearson_cosine
	value: 0.9494511778471465
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.6620048542679903
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.9830259644213172
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.6620048542679903
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.9835562939431381
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.6620048542679903
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.9469313992827345
	name: Pearson Dot
	- type: spearman_dot
	value: 0.6607798700248341
	name: Spearman Dot
	- type: pearson_max
	value: 0.9835562939431381
	name: Pearson Max
	- type: spearman_max
	value: 0.6620048542679903
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: dim 128
	type: dim_128
	metrics:
	- type: pearson_cosine
	value: 0.9397052405386266
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.6620048542679903
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.9762184586055923
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.6620048542679903
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.9781975526221939
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.6620048542679903
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.9271211389022183
	name: Pearson Dot
	- type: spearman_dot
	value: 0.6607798700248341
	name: Spearman Dot
	- type: pearson_max
	value: 0.9781975526221939
	name: Pearson Max
	- type: spearman_max
	value: 0.6620048542679903
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: dim 64
	type: dim_64
	metrics:
	- type: pearson_cosine
	value: 0.9149032642312528
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.6620048542679903
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.968215524939354
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.6620048542679903
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.9708485057392984
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.6620048542679903
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.8940456314300972
	name: Pearson Dot
	- type: spearman_dot
	value: 0.6602255244962898
	name: Spearman Dot
	- type: pearson_max
	value: 0.9708485057392984
	name: Pearson Max
	- type: spearman_max
	value: 0.6620048542679903
	name: Spearman Max
	---

	# BGE base Financial Matryoshka

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co./BAAI/bge-base-en-v1.5). It maps sentences & paragraphs to a 768-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: [BAAI/bge-base-en-v1.5](https://huggingface.co./BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 768 tokens
	- Similarity Function: Cosine Similarity
	<!-- - Training Dataset: Unknown -->
	- Language: en
	- License: apache-2.0

	### 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': 512, 'do_lower_case': True}) with Transformer model: BertModel
	(1): Pooling({'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})
	(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("thetayne/finetuned_model_0613")
	# Run inference
	sentences = [
	'Corrosion Resistant Coatings',
	'Corrosion Resistant Coatings',
	'Mower Blade',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 768]

	# 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
	* Dataset: `dim_768`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:----------\|
	\| pearson_cosine \| 0.9548 \|
	\| spearman_cosine \| 0.662 \|
	\| pearson_manhattan \| 0.9859 \|
	\| spearman_manhattan \| 0.662 \|
	\| pearson_euclidean \| 0.9864 \|
	\| spearman_euclidean \| 0.662 \|
	\| pearson_dot \| 0.9548 \|
	\| spearman_dot \| 0.6611 \|
	\| pearson_max \| 0.9864 \|
	\| spearman_max \| 0.662 \|

	#### Semantic Similarity
	* Dataset: `dim_512`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:----------\|
	\| pearson_cosine \| 0.9544 \|
	\| spearman_cosine \| 0.662 \|
	\| pearson_manhattan \| 0.9856 \|
	\| spearman_manhattan \| 0.662 \|
	\| pearson_euclidean \| 0.9862 \|
	\| spearman_euclidean \| 0.662 \|
	\| pearson_dot \| 0.9501 \|
	\| spearman_dot \| 0.6608 \|
	\| pearson_max \| 0.9862 \|
	\| spearman_max \| 0.662 \|

	#### Semantic Similarity
	* Dataset: `dim_256`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:----------\|
	\| pearson_cosine \| 0.9495 \|
	\| spearman_cosine \| 0.662 \|
	\| pearson_manhattan \| 0.983 \|
	\| spearman_manhattan \| 0.662 \|
	\| pearson_euclidean \| 0.9836 \|
	\| spearman_euclidean \| 0.662 \|
	\| pearson_dot \| 0.9469 \|
	\| spearman_dot \| 0.6608 \|
	\| pearson_max \| 0.9836 \|
	\| spearman_max \| 0.662 \|

	#### Semantic Similarity
	* Dataset: `dim_128`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:----------\|
	\| pearson_cosine \| 0.9397 \|
	\| spearman_cosine \| 0.662 \|
	\| pearson_manhattan \| 0.9762 \|
	\| spearman_manhattan \| 0.662 \|
	\| pearson_euclidean \| 0.9782 \|
	\| spearman_euclidean \| 0.662 \|
	\| pearson_dot \| 0.9271 \|
	\| spearman_dot \| 0.6608 \|
	\| pearson_max \| 0.9782 \|
	\| spearman_max \| 0.662 \|

	#### Semantic Similarity
	* Dataset: `dim_64`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:----------\|
	\| pearson_cosine \| 0.9149 \|
	\| spearman_cosine \| 0.662 \|
	\| pearson_manhattan \| 0.9682 \|
	\| spearman_manhattan \| 0.662 \|
	\| pearson_euclidean \| 0.9708 \|
	\| spearman_euclidean \| 0.662 \|
	\| pearson_dot \| 0.894 \|
	\| spearman_dot \| 0.6602 \|
	\| pearson_max \| 0.9708 \|
	\| spearman_max \| 0.662 \|

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	## Training Details

	### Training Dataset

	#### Unnamed Dataset


	* Size: 1,625 training samples
	* Columns: <code>sentence_A</code>, <code>sentence_B</code>, and <code>score</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence_A \| sentence_B \| score \|
	\|:--------\|:---------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|:------------------------------------------------\|
	\| type \| string \| string \| int \|
	\| details \| <ul><li>min: 3 tokens</li><li>mean: 5.68 tokens</li><li>max: 36 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 5.73 tokens</li><li>max: 36 tokens</li></ul> \| <ul><li>0: ~83.30%</li><li>1: ~16.70%</li></ul> \|
	* Samples:
	\| sentence_A \| sentence_B \| score \|
	\|:-----------------------------------\|:--------------------------------------\|:---------------\|
	\| <code>Thermal Fatigue</code> \| <code>Ferritic Stainless Steel</code> \| <code>0</code> \|
	\| <code>High Temperature Wear</code> \| <code>Drill String</code> \| <code>0</code> \|
	\| <code>Carbide Coatings</code> \| <code>Carbide Coatings</code> \| <code>1</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

	- `eval_strategy`: epoch
	- `per_device_train_batch_size`: 32
	- `per_device_eval_batch_size`: 16
	- `gradient_accumulation_steps`: 16
	- `learning_rate`: 2e-05
	- `num_train_epochs`: 4
	- `lr_scheduler_type`: cosine
	- `warmup_ratio`: 0.1
	- `bf16`: True
	- `tf32`: True
	- `load_best_model_at_end`: True
	- `optim`: adamw_torch_fused
	- `batch_sampler`: no_duplicates

	#### 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`: 32
	- `per_device_eval_batch_size`: 16
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 16
	- `eval_accumulation_steps`: None
	- `learning_rate`: 2e-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`: cosine
	- `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`: True
	- `fp16`: False
	- `fp16_opt_level`: O1
	- `half_precision_backend`: auto
	- `bf16_full_eval`: False
	- `fp16_full_eval`: False
	- `tf32`: True
	- `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_fused
	- `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`: False
	- `hub_always_push`: False
	- `gradient_checkpointing`: False
	- `gradient_checkpointing_kwargs`: None
	- `include_inputs_for_metrics`: False
	- `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
	- `batch_sampler`: no_duplicates
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| dim_128_spearman_cosine \| dim_256_spearman_cosine \| dim_512_spearman_cosine \| dim_64_spearman_cosine \| dim_768_spearman_cosine \|
	\|:----------:\|:------:\|:-------------:\|:-----------------------:\|:-----------------------:\|:-----------------------:\|:----------------------:\|:-----------------------:\|
	\| 0 \| 0 \| - \| 0.6626 \| 0.6626 \| 0.6626 \| 0.6626 \| 0.6626 \|
	\| 0.9412 \| 3 \| - \| 0.6620 \| 0.6620 \| 0.6620 \| 0.6620 \| 0.6620 \|
	\| 1.8627 \| 6 \| - \| 0.6620 \| 0.6620 \| 0.6620 \| 0.6620 \| 0.6620 \|
	\| 2.7843 \| 9 \| - \| 0.6620 \| 0.6620 \| 0.6620 \| 0.6620 \| 0.6620 \|
	\| 3.0784 \| 10 \| 0.156 \| - \| - \| - \| - \| - \|
	\| 3.7059 \| 12 \| - \| 0.662 \| 0.662 \| 0.662 \| 0.662 \| 0.662 \|

	* The bold row denotes the saved checkpoint.

	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.0.1
	- Transformers: 4.41.2
	- PyTorch: 2.1.2+cu121
	- Accelerate: 0.31.0
	- Datasets: 2.19.1
	- Tokenizers: 0.19.1

	## 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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