Add new SentenceTransformer model.

ab5d9db verified 4 months ago

20.3 kB

	---
	language: []
	library_name: sentence-transformers
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:2036
	- loss:MultipleNegativesRankingLoss
	base_model: google-bert/bert-base-uncased
	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: Proven ability to establish and lead complex projects and programs
	within a multilayered, hierarchical organization.
	sentences:
	- Managed multiple concurrent projects in a large healthcare organization
	- Assisted in project documentation without direct management responsibilities
	- Skilled in creating presentations using Microsoft PowerPoint
	- source_sentence: Experience in evaluating and planning projects to minimize scheduled
	overtime requirements.
	sentences:
	- Validated release packages and coordinated Salesforce release cycles
	- Oversaw daily housekeeping operations
	- Successfully managed facility renovation projects to reduce overtime
	- source_sentence: Candidates should have significant experience in a commercial construction
	environment, ideally with a minimum of 10 years in the field.
	sentences:
	- Built strong partnerships with cross-functional teams to deliver projects
	- over 12 years of experience managing commercial construction projects
	- 2 years of experience in residential construction
	- source_sentence: Possession of strong leadership skills in a Workday professional
	context.
	sentences:
	- 3 years of experience with cardiac mapping technologies
	- Managed Workday implementation projects and trained team members
	- Developed marketing strategies for new products
	- source_sentence: Ability to manage TikTok Shop setup and troubleshoot operational
	issues effectively.
	sentences:
	- Troubleshot various operational issues during the setup of a TikTok Shop
	- Handled customer support queries for social media platforms
	- Consistently maintained client trust through transparent communication
	pipeline_tag: sentence-similarity
	model-index:
	- name: SentenceTransformer based on google-bert/bert-base-uncased
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev
	type: sts-dev
	metrics:
	- type: pearson_cosine
	value: 0.7481079446812986
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.7505186904322839
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.7554763601200802
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.758901200634132
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7545320893124581
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.7581291583714751
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.6010864985986635
	name: Pearson Dot
	- type: spearman_dot
	value: 0.5940811367263572
	name: Spearman Dot
	- type: pearson_max
	value: 0.7554763601200802
	name: Pearson Max
	- type: spearman_max
	value: 0.758901200634132
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test
	type: sts-test
	metrics:
	- type: pearson_cosine
	value: 0.7078369274551736
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.6860532079702527
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.7195614364247788
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.6992090523383406
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7199683293098692
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.699729559217933
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.4876300833689144
	name: Pearson Dot
	- type: spearman_dot
	value: 0.47135994215107385
	name: Spearman Dot
	- type: pearson_max
	value: 0.7199683293098692
	name: Pearson Max
	- type: spearman_max
	value: 0.699729559217933
	name: Spearman Max
	---

	# SentenceTransformer based on google-bert/bert-base-uncased

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased). 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: [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) <!-- at revision 86b5e0934494bd15c9632b12f734a8a67f723594 -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 768 tokens
	- 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': 512, 'do_lower_case': False}) with Transformer model: BertModel
	(1): Pooling({'word_embedding_dimension': 768, '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})
	)
	```

	## 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("trbeers/bert-base-uncased-nli-v0")
	# Run inference
	sentences = [
	'Ability to manage TikTok Shop setup and troubleshoot operational issues effectively.',
	'Troubleshot various operational issues during the setup of a TikTok Shop',
	'Handled customer support queries for social media platforms',
	]
	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: `sts-dev`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.7481 \|
	\| spearman_cosine \| 0.7505 \|
	\| pearson_manhattan \| 0.7555 \|
	\| spearman_manhattan \| 0.7589 \|
	\| pearson_euclidean \| 0.7545 \|
	\| spearman_euclidean \| 0.7581 \|
	\| pearson_dot \| 0.6011 \|
	\| spearman_dot \| 0.5941 \|
	\| pearson_max \| 0.7555 \|
	\| spearman_max \| 0.7589 \|

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

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.7078 \|
	\| spearman_cosine \| 0.6861 \|
	\| pearson_manhattan \| 0.7196 \|
	\| spearman_manhattan \| 0.6992 \|
	\| pearson_euclidean \| 0.72 \|
	\| spearman_euclidean \| 0.6997 \|
	\| pearson_dot \| 0.4876 \|
	\| spearman_dot \| 0.4714 \|
	\| pearson_max \| 0.72 \|
	\| spearman_max \| 0.6997 \|

	<!--
	## 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.
	-->

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	### Recommendations

	What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.
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	## Training Details

	### Training Dataset

	#### Unnamed Dataset


	* Size: 2,036 training 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: 7 tokens</li><li>mean: 16.07 tokens</li><li>max: 39 tokens</li></ul> \| <ul><li>min: 7 tokens</li><li>mean: 11.23 tokens</li><li>max: 24 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 8.39 tokens</li><li>max: 15 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \| negative \|
	\|:-----------------------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------\|
	\| <code>Sensitivity to the needs of patients, families, and physicians to deliver compassionate care.</code> \| <code>worked closely with families to address patient concerns</code> \| <code>specialized in technical equipment management without direct patient contact</code> \|
	\| <code>Ability to lift 25 lbs. or more as required for handling athletic equipment.</code> \| <code>Handled and organized equipment, ensuring safe lifting of heavy items</code> \| <code>Coordinated scheduling for team practices and meetings</code> \|
	\| <code>The candidate should have significant development experience, preferably around 10 years.</code> \| <code>developed and implemented data architecture projects for a decade</code> \| <code>worked in customer service for 5 years</code> \|
	* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "cos_sim"
	}
	```

	### Evaluation Dataset

	#### Unnamed Dataset


	* Size: 510 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: 8 tokens</li><li>mean: 16.39 tokens</li><li>max: 34 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 11.34 tokens</li><li>max: 20 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 8.41 tokens</li><li>max: 16 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \| negative \|
	\|:-----------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------\|:---------------------------------------------------------------\|
	\| <code>Qualified to provide personalized and friendly client interactions</code> \| <code>Assisted clients with inquiries and ensured a welcoming environment</code> \| <code>Conducted market research for product development</code> \|
	\| <code>Understanding of network architecture principles and design patterns is critical.</code> \| <code>Designed and implemented network architectures for cloud-based solutions</code> \| <code>Managed on-premises server infrastructure</code> \|
	\| <code>Knowledge of cloud technologies and their implications for customer engagement.</code> \| <code>Managed customer onboarding for cloud-based services</code> \| <code>Handled sales inquiries for software licenses</code> \|
	* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "cos_sim"
	}
	```

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

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 128
	- `per_device_eval_batch_size`: 128
	- `num_train_epochs`: 1
	- `warmup_ratio`: 0.1
	- `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`: 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
	- `learning_rate`: 5e-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`: 1
	- `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`: 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`: 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 \| loss \| sts-dev_spearman_cosine \| sts-test_spearman_cosine \|
	\|:-----:\|:----:\|:------:\|:-----------------------:\|:------------------------:\|
	\| 0 \| 0 \| - \| 0.5931 \| - \|
	\| 0.625 \| 10 \| 1.4252 \| 0.7505 \| - \|
	\| 1.0 \| 16 \| - \| - \| 0.6861 \|


	### Framework Versions
	- Python: 3.10.11
	- Sentence Transformers: 3.0.1
	- Transformers: 4.41.2
	- PyTorch: 2.3.1
	- 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",
	}
	```

	#### MultipleNegativesRankingLoss
	```bibtex
	@misc{henderson2017efficient,
	title={Efficient Natural Language Response Suggestion for Smart Reply},
	author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
	year={2017},
	eprint={1705.00652},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	```

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