Add new SentenceTransformer model.

006e61b verified 3 months ago

13.3 kB

	---
	base_model: google-bert/bert-base-uncased
	datasets: []
	language: []
	library_name: sentence-transformers
	pipeline_tag: sentence-similarity
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:100000
	- loss:DenoisingAutoEncoderLoss
	widget:
	- source_sentence: 1109/icnsurv
	sentences:
	- 1109/icnsurv
	- A cost function is needed to assign a performance metric value to a particular
	test run
	- Aircraft OperationsFuture aircraft will sense, control, communicate, and navigate
	with increasing levels of autonomy, enabling new concepts in air traffic management
	- source_sentence: Table 1 of and to well as the median taxi from STBO KDFW
	sentences:
	- Table 1 Metrics of accuracy, median and MAD of residuals as compared to STBO predictions,
	as well as the median taxi time from STBO for KDFW and KCLT airports
	- ', IEEE, 2005, pp'
	- 'RESULTS: EFFICIENCY ANALYSIS'
	- source_sentence: gate time to known
	sentences:
	- 3FIVE INPUT VARIABLESParameterDescriptionHead windHead WindGust windGust WindCeiling_ftForecast
	CeilingVis_ftForecast VisibilityAct_Land_Wgt Actual Landing Weightfive parameters
	listed in
	- Instead, gate departure time was assumed to be known
	- The proof is very similar to that presented for the NP-completeness of ASP, and
	is based on reduction from PLANAR-P3( 6), hence we simply provide the main idea
	of the proof
	- source_sentence: ', Hough" Pattern Recognition, Vol'
	sentences:
	- 9 Station Keeping scores
	- "\t\tAGARD CD-410"
	- ', "Generalizing the Hough Transform to Detect Arbitrary Shapes," Pattern Recognition,
	Vol'
	- source_sentence: Airlines often ferry from locations fuel prices
	sentences:
	- Scheduler Inputs and Order of ConsiderationThe surface model provides EOBT, UOBT,
	UTOT and other detailed flight-specific modeled input
	- "\t\t\tKeithWichman"
	- Airlines often ferry fuel from locations where fuel prices are cheapest
	---

	# 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': 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})
	)
	```

	## 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("kathleenge/tsdae-bert-base-uncased")
	# Run inference
	sentences = [
	'Airlines often ferry from locations fuel prices',
	'Airlines often ferry fuel from locations where fuel prices are cheapest',
	'\t\t\tKeithWichman',
	]
	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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	### Downstream Usage (Sentence Transformers)

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	<details><summary>Click to expand</summary>

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	## Training Details

	### Training Dataset

	#### Unnamed Dataset


	* Size: 100,000 training samples
	* Columns: <code>sentence_0</code> and <code>sentence_1</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence_0 \| sentence_1 \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 3 tokens</li><li>mean: 10.95 tokens</li><li>max: 106 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 23.39 tokens</li><li>max: 239 tokens</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \|
	\|:------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------------------------\|
	\| <code>selected and reviewed for value current on metroplex</code> \| <code>The literature was selected and reviewed for its value to the current research on metroplex operations</code> \|
	\| <code>and</code> \| <code>, and Dulchinos, V</code> \|
	\| <code>,</code> \| <code>, Atkins, S</code> \|
	* Loss: [<code>DenoisingAutoEncoderLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#denoisingautoencoderloss)

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

	- `num_train_epochs`: 1
	- `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`: 8
	- `per_device_eval_batch_size`: 8
	- `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
	- `num_train_epochs`: 1
	- `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`: 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
	- `eval_on_start`: False
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: round_robin

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \|
	\|:-----:\|:-----:\|:-------------:\|
	\| 0.04 \| 500 \| 7.3777 \|
	\| 0.08 \| 1000 \| 6.9771 \|
	\| 0.12 \| 1500 \| 6.8481 \|
	\| 0.16 \| 2000 \| 6.7737 \|
	\| 0.2 \| 2500 \| 6.6935 \|
	\| 0.24 \| 3000 \| 6.6264 \|
	\| 0.28 \| 3500 \| 6.5918 \|
	\| 0.32 \| 4000 \| 6.5504 \|
	\| 0.36 \| 4500 \| 6.4805 \|
	\| 0.4 \| 5000 \| 6.4539 \|
	\| 0.44 \| 5500 \| 6.4242 \|
	\| 0.48 \| 6000 \| 6.4017 \|
	\| 0.52 \| 6500 \| 6.3808 \|
	\| 0.56 \| 7000 \| 6.3595 \|
	\| 0.6 \| 7500 \| 6.3174 \|
	\| 0.64 \| 8000 \| 6.2911 \|
	\| 0.68 \| 8500 \| 6.2917 \|
	\| 0.72 \| 9000 \| 6.2555 \|
	\| 0.76 \| 9500 \| 6.2314 \|
	\| 0.8 \| 10000 \| 6.2223 \|
	\| 0.84 \| 10500 \| 6.1852 \|
	\| 0.88 \| 11000 \| 6.2067 \|
	\| 0.92 \| 11500 \| 6.1562 \|
	\| 0.96 \| 12000 \| 6.1563 \|
	\| 1.0 \| 12500 \| 6.092 \|


	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.0.1
	- Transformers: 4.42.4
	- PyTorch: 2.3.1+cu121
	- Accelerate: 0.32.1
	- Datasets: 2.20.0
	- 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",
	}
	```

	#### DenoisingAutoEncoderLoss
	```bibtex
	@inproceedings{wang-2021-TSDAE,
	title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning",
	author = "Wang, Kexin and Reimers, Nils and Gurevych, Iryna",
	booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
	month = nov,
	year = "2021",
	address = "Punta Cana, Dominican Republic",
	publisher = "Association for Computational Linguistics",
	pages = "671--688",
	url = "https://arxiv.org/abs/2104.06979",
	}
	```

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