Add new SentenceTransformer model.

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	---
	language: []
	library_name: sentence-transformers
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- dataset_size:10K<n<100K
	- loss:MultipleNegativesSymmetricRankingLoss
	base_model: distilbert/distilbert-base-uncased
	metrics:
	- pearson_cosine
	- spearman_cosine
	- pearson_manhattan
	- spearman_manhattan
	- pearson_euclidean
	- spearman_euclidean
	- pearson_dot
	- spearman_dot
	- pearson_max
	- spearman_max
	widget:
	- source_sentence: what is GOGO
	sentences:
	- What is Viasat
	- are we flying into Tel Aviv
	- how do i correct a name in term
	- source_sentence: What is EU 261
	sentences:
	- is puj a EU compensation country
	- can i take my bicycle on af
	- flight delays over 6 hours
	- source_sentence: Can i get wifi
	sentences:
	- which aircrafts do not have wifi
	- military traveling with pet
	- baggage delay to carousel
	- source_sentence: austin airport
	sentences:
	- What time is IAH open
	- amex card free checked bag
	- what is upgrade companion
	- source_sentence: pets in cargo
	sentences:
	- can a pet travel in cargo
	- baggage exceptions for Amex
	- how do I get sky priority
	pipeline_tag: sentence-similarity
	model-index:
	- name: SentenceTransformer based on distilbert/distilbert-base-uncased
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: eval examples
	type: eval_examples
	metrics:
	- type: pearson_cosine
	value: .nan
	name: Pearson Cosine
	- type: spearman_cosine
	value: .nan
	name: Spearman Cosine
	- type: pearson_manhattan
	value: .nan
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: .nan
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: .nan
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: .nan
	name: Spearman Euclidean
	- type: pearson_dot
	value: .nan
	name: Pearson Dot
	- type: spearman_dot
	value: .nan
	name: Spearman Dot
	- type: pearson_max
	value: .nan
	name: Pearson Max
	- type: spearman_max
	value: .nan
	name: Spearman Max
	---

	# SentenceTransformer based on distilbert/distilbert-base-uncased

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-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: [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) <!-- at revision 12040accade4e8a0f71eabdb258fecc2e7e948be -->
	- 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: DistilBertModel
	(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("pjbhaumik/biencoder-finetune-model-v9")
	# Run inference
	sentences = [
	'pets in cargo',
	'can a pet travel in cargo',
	'baggage exceptions for Amex',
	]
	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)

	You can finetune this model on your own dataset.

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

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	## Evaluation

	### Metrics

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

	\| Metric \| Value \|
	\|:-------------------\|:--------\|
	\| pearson_cosine \| nan \|
	\| spearman_cosine \| nan \|
	\| pearson_manhattan \| nan \|
	\| spearman_manhattan \| nan \|
	\| pearson_euclidean \| nan \|
	\| spearman_euclidean \| nan \|
	\| pearson_dot \| nan \|
	\| spearman_dot \| nan \|
	\| pearson_max \| nan \|
	\| spearman_max \| nan \|

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	## 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: 15,488 training samples
	* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence_0 \| sentence_1 \| label \|
	\|:--------\|:---------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:-----------------------------\|
	\| type \| string \| string \| int \|
	\| details \| <ul><li>min: 4 tokens</li><li>mean: 10.4 tokens</li><li>max: 47 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 10.14 tokens</li><li>max: 37 tokens</li></ul> \| <ul><li>1: 100.00%</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \| label \|
	\|:-----------------------------------------------------------------\|:-------------------------------------------------------------------------------------\|:---------------\|
	\| <code>how to use a companion certificate on delta.com</code> \| <code>SHOPPING ON DELTA.COM FOR AMEX CERT</code> \| <code>1</code> \|
	\| <code>is jamaica can be booked with companion certificate</code> \| <code>what areas can the American Express companion certificate be applied to</code> \| <code>1</code> \|
	\| <code>how do i book award travel on klm</code> \| <code>can you book an air france ticket with miles</code> \| <code>1</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"
	}
	```

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

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 16
	- `per_device_eval_batch_size`: 16
	- `num_train_epochs`: 12
	- `multi_dataset_batch_sampler`: round_robin

	#### 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`: 16
	- `per_device_eval_batch_size`: 16
	- `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`: 12
	- `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
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: round_robin

	</details>

	### Training Logs
	<details><summary>Click to expand</summary>

	\| Epoch \| Step \| Training Loss \| eval_examples_spearman_max \|
	\|:-------:\|:-----:\|:-------------:\|:--------------------------:\|
	\| 0.1033 \| 100 \| - \| nan \|
	\| 0.2066 \| 200 \| - \| nan \|
	\| 0.3099 \| 300 \| - \| nan \|
	\| 0.4132 \| 400 \| - \| nan \|
	\| 0.5165 \| 500 \| 0.7655 \| nan \|
	\| 0.6198 \| 600 \| - \| nan \|
	\| 0.7231 \| 700 \| - \| nan \|
	\| 0.8264 \| 800 \| - \| nan \|
	\| 0.9298 \| 900 \| - \| nan \|
	\| 1.0 \| 968 \| - \| nan \|
	\| 1.0331 \| 1000 \| 0.3727 \| nan \|
	\| 1.1364 \| 1100 \| - \| nan \|
	\| 1.2397 \| 1200 \| - \| nan \|
	\| 1.3430 \| 1300 \| - \| nan \|
	\| 1.4463 \| 1400 \| - \| nan \|
	\| 1.5496 \| 1500 \| 0.2686 \| nan \|
	\| 1.6529 \| 1600 \| - \| nan \|
	\| 1.7562 \| 1700 \| - \| nan \|
	\| 1.8595 \| 1800 \| - \| nan \|
	\| 1.9628 \| 1900 \| - \| nan \|
	\| 2.0 \| 1936 \| - \| nan \|
	\| 2.0661 \| 2000 \| 0.2709 \| nan \|
	\| 2.1694 \| 2100 \| - \| nan \|
	\| 2.2727 \| 2200 \| - \| nan \|
	\| 2.3760 \| 2300 \| - \| nan \|
	\| 2.4793 \| 2400 \| - \| nan \|
	\| 2.5826 \| 2500 \| 0.231 \| nan \|
	\| 2.6860 \| 2600 \| - \| nan \|
	\| 2.7893 \| 2700 \| - \| nan \|
	\| 2.8926 \| 2800 \| - \| nan \|
	\| 2.9959 \| 2900 \| - \| nan \|
	\| 3.0 \| 2904 \| - \| nan \|
	\| 3.0992 \| 3000 \| 0.2461 \| nan \|
	\| 3.2025 \| 3100 \| - \| nan \|
	\| 3.3058 \| 3200 \| - \| nan \|
	\| 3.4091 \| 3300 \| - \| nan \|
	\| 3.5124 \| 3400 \| - \| nan \|
	\| 3.6157 \| 3500 \| 0.2181 \| nan \|
	\| 3.7190 \| 3600 \| - \| nan \|
	\| 3.8223 \| 3700 \| - \| nan \|
	\| 3.9256 \| 3800 \| - \| nan \|
	\| 4.0 \| 3872 \| - \| nan \|
	\| 4.0289 \| 3900 \| - \| nan \|
	\| 4.1322 \| 4000 \| 0.2288 \| nan \|
	\| 4.2355 \| 4100 \| - \| nan \|
	\| 4.3388 \| 4200 \| - \| nan \|
	\| 4.4421 \| 4300 \| - \| nan \|
	\| 4.5455 \| 4400 \| - \| nan \|
	\| 4.6488 \| 4500 \| 0.2123 \| nan \|
	\| 4.7521 \| 4600 \| - \| nan \|
	\| 4.8554 \| 4700 \| - \| nan \|
	\| 4.9587 \| 4800 \| - \| nan \|
	\| 5.0 \| 4840 \| - \| nan \|
	\| 5.0620 \| 4900 \| - \| nan \|
	\| 5.1653 \| 5000 \| 0.2254 \| nan \|
	\| 5.2686 \| 5100 \| - \| nan \|
	\| 5.3719 \| 5200 \| - \| nan \|
	\| 5.4752 \| 5300 \| - \| nan \|
	\| 5.5785 \| 5400 \| - \| nan \|
	\| 5.6818 \| 5500 \| 0.2077 \| nan \|
	\| 5.7851 \| 5600 \| - \| nan \|
	\| 5.8884 \| 5700 \| - \| nan \|
	\| 5.9917 \| 5800 \| - \| nan \|
	\| 6.0 \| 5808 \| - \| nan \|
	\| 6.0950 \| 5900 \| - \| nan \|
	\| 6.1983 \| 6000 \| 0.218 \| nan \|
	\| 6.3017 \| 6100 \| - \| nan \|
	\| 6.4050 \| 6200 \| - \| nan \|
	\| 6.5083 \| 6300 \| - \| nan \|
	\| 6.6116 \| 6400 \| - \| nan \|
	\| 6.7149 \| 6500 \| 0.206 \| nan \|
	\| 6.8182 \| 6600 \| - \| nan \|
	\| 6.9215 \| 6700 \| - \| nan \|
	\| 7.0 \| 6776 \| - \| nan \|
	\| 7.0248 \| 6800 \| - \| nan \|
	\| 7.1281 \| 6900 \| - \| nan \|
	\| 7.2314 \| 7000 \| 0.2126 \| nan \|
	\| 7.3347 \| 7100 \| - \| nan \|
	\| 7.4380 \| 7200 \| - \| nan \|
	\| 7.5413 \| 7300 \| - \| nan \|
	\| 7.6446 \| 7400 \| - \| nan \|
	\| 7.7479 \| 7500 \| 0.2065 \| nan \|
	\| 7.8512 \| 7600 \| - \| nan \|
	\| 7.9545 \| 7700 \| - \| nan \|
	\| 8.0 \| 7744 \| - \| nan \|
	\| 8.0579 \| 7800 \| - \| nan \|
	\| 8.1612 \| 7900 \| - \| nan \|
	\| 8.2645 \| 8000 \| 0.2068 \| nan \|
	\| 8.3678 \| 8100 \| - \| nan \|
	\| 8.4711 \| 8200 \| - \| nan \|
	\| 8.5744 \| 8300 \| - \| nan \|
	\| 8.6777 \| 8400 \| - \| nan \|
	\| 8.7810 \| 8500 \| 0.2014 \| nan \|
	\| 8.8843 \| 8600 \| - \| nan \|
	\| 8.9876 \| 8700 \| - \| nan \|
	\| 9.0 \| 8712 \| - \| nan \|
	\| 9.0909 \| 8800 \| - \| nan \|
	\| 9.1942 \| 8900 \| - \| nan \|
	\| 9.2975 \| 9000 \| 0.2057 \| nan \|
	\| 9.4008 \| 9100 \| - \| nan \|
	\| 9.5041 \| 9200 \| - \| nan \|
	\| 9.6074 \| 9300 \| - \| nan \|
	\| 9.7107 \| 9400 \| - \| nan \|
	\| 9.8140 \| 9500 \| 0.1969 \| nan \|
	\| 9.9174 \| 9600 \| - \| nan \|
	\| 10.0 \| 9680 \| - \| nan \|
	\| 10.0207 \| 9700 \| - \| nan \|
	\| 10.1240 \| 9800 \| - \| nan \|
	\| 10.2273 \| 9900 \| - \| nan \|
	\| 10.3306 \| 10000 \| 0.2023 \| nan \|
	\| 10.4339 \| 10100 \| - \| nan \|
	\| 10.5372 \| 10200 \| - \| nan \|
	\| 10.6405 \| 10300 \| - \| nan \|
	\| 10.7438 \| 10400 \| - \| nan \|
	\| 10.8471 \| 10500 \| 0.1946 \| nan \|
	\| 10.9504 \| 10600 \| - \| nan \|
	\| 11.0 \| 10648 \| - \| nan \|
	\| 11.0537 \| 10700 \| - \| nan \|
	\| 11.1570 \| 10800 \| - \| nan \|
	\| 11.2603 \| 10900 \| - \| nan \|
	\| 11.3636 \| 11000 \| 0.1982 \| nan \|
	\| 11.4669 \| 11100 \| - \| nan \|
	\| 11.5702 \| 11200 \| - \| nan \|
	\| 11.6736 \| 11300 \| - \| nan \|
	\| 11.7769 \| 11400 \| - \| nan \|
	\| 11.8802 \| 11500 \| 0.1919 \| nan \|
	\| 11.9835 \| 11600 \| - \| nan \|
	\| 12.0 \| 11616 \| - \| nan \|

	</details>

	### Framework Versions
	- Python: 3.10.14
	- Sentence Transformers: 3.0.0
	- Transformers: 4.41.2
	- PyTorch: 2.1.0
	- Accelerate: 0.30.1
	- 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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