Add new SentenceTransformer model.

1d1ac71 verified 3 months ago

26.2 kB

	---
	base_model: dbourget/pb-small-10e-tsdae6e-philsim-cosine-6e-beatai-cosine-50e
	library_name: sentence-transformers
	metrics:
	- cosine_accuracy
	- dot_accuracy
	- manhattan_accuracy
	- euclidean_accuracy
	- max_accuracy
	pipeline_tag: sentence-similarity
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:9504
	- loss:TripletLoss
	widget:
	- source_sentence: cap product
	sentences:
	- method of adjoining a chain of degree p with a co-chain of degree q, where q is
	less than or equal to p, to form a composite chain of degree p-q
	- 'Ontology '
	- hat commodity
	- source_sentence: cognitivism
	sentences:
	- supporting cognitive science
	- study of changes in organisms caused by modification of gene expression rather
	than alteration of the genetic code
	- 'the idea that mind works like an algorithmic symbol manipulation '
	- source_sentence: doxastic voluntarism
	sentences:
	- Land surrounded by water
	- belief one is free
	- the ability to will beliefs
	- source_sentence: conceptual role
	sentences:
	- concept
	- inferential role
	- 'Theory of knowledge '
	- source_sentence: scientific revolutions
	sentences:
	- scientific realism
	- Universal moral principles govern legal systems
	- paradigm shifts
	model-index:
	- name: SentenceTransformer based on dbourget/pb-small-10e-tsdae6e-philsim-cosine-6e-beatai-cosine-50e
	results:
	- task:
	type: triplet
	name: Triplet
	dataset:
	name: beatai dev
	type: beatai-dev
	metrics:
	- type: cosine_accuracy
	value: 0.813973063973064
	name: Cosine Accuracy
	- type: dot_accuracy
	value: 0.22727272727272727
	name: Dot Accuracy
	- type: manhattan_accuracy
	value: 0.8198653198653199
	name: Manhattan Accuracy
	- type: euclidean_accuracy
	value: 0.8156565656565656
	name: Euclidean Accuracy
	- type: max_accuracy
	value: 0.8198653198653199
	name: Max Accuracy
	---

	# SentenceTransformer based on dbourget/pb-small-10e-tsdae6e-philsim-cosine-6e-beatai-cosine-50e

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [dbourget/pb-small-10e-tsdae6e-philsim-cosine-6e-beatai-cosine-50e](https://huggingface.co/dbourget/pb-small-10e-tsdae6e-philsim-cosine-6e-beatai-cosine-50e). It maps sentences & paragraphs to a 1024-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: [dbourget/pb-small-10e-tsdae6e-philsim-cosine-6e-beatai-cosine-50e](https://huggingface.co/dbourget/pb-small-10e-tsdae6e-philsim-cosine-6e-beatai-cosine-50e) <!-- at revision 86e3b91181f7c10aa5a92184184dc50f0f25aa57 -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 1024 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': 1024, '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("dbourget/pb-small-10e-tsdae6e-philsim-cosine-6e-beatai-cosine-80e")
	# Run inference
	sentences = [
	'scientific revolutions',
	'paradigm shifts',
	'scientific realism',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 1024]

	# Get the similarity scores for the embeddings
	similarities = model.similarity(embeddings, embeddings)
	print(similarities.shape)
	# [3, 3]
	```

	<!--
	### Direct Usage (Transformers)

	<details><summary>Click to see the direct usage in Transformers</summary>

	</details>
	-->

	<!--
	### Downstream Usage (Sentence Transformers)

	You can finetune this model on your own dataset.

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

	</details>
	-->

	<!--
	### Out-of-Scope Use

	List how the model may foreseeably be misused and address what users ought not to do with the model.
	-->

	## Evaluation

	### Metrics

	#### Triplet
	* Dataset: `beatai-dev`
	* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:----------\|
	\| cosine_accuracy \| 0.814 \|
	\| dot_accuracy \| 0.2273 \|
	\| manhattan_accuracy \| 0.8199 \|
	\| euclidean_accuracy \| 0.8157 \|
	\| max_accuracy \| 0.8199 \|

	<!--
	## 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.
	-->

	<!--
	### Recommendations

	What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.
	-->

	## Training Details

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

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 138
	- `per_device_eval_batch_size`: 138
	- `learning_rate`: 5e-07
	- `weight_decay`: 0.01
	- `num_train_epochs`: 30
	- `lr_scheduler_type`: constant
	- `bf16`: True
	- `dataloader_drop_last`: True
	- `resume_from_checkpoint`: True

	#### 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`: 138
	- `per_device_eval_batch_size`: 138
	- `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`: 5e-07
	- `weight_decay`: 0.01
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `max_grad_norm`: 1.0
	- `num_train_epochs`: 30
	- `max_steps`: -1
	- `lr_scheduler_type`: constant
	- `lr_scheduler_kwargs`: {}
	- `warmup_ratio`: 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`: True
	- `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`: True
	- `dataloader_num_workers`: 0
	- `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`: False
	- `skip_memory_metrics`: True
	- `use_legacy_prediction_loop`: False
	- `push_to_hub`: False
	- `resume_from_checkpoint`: True
	- `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
	- `use_liger_kernel`: False
	- `eval_use_gather_object`: False
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: proportional

	</details>

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

	\| Epoch \| Step \| Training Loss \| loss \| beatai-dev_cosine_accuracy \|
	\|:-------:\|:----:\|:-------------:\|:------:\|:--------------------------:\|
	\| 0 \| 0 \| - \| - \| 0.7904 \|
	\| 0.1471 \| 10 \| 0.0721 \| - \| - \|
	\| 0.2941 \| 20 \| 0.0708 \| - \| - \|
	\| 0.4412 \| 30 \| 0.0736 \| - \| - \|
	\| 0.5882 \| 40 \| 0.0704 \| - \| - \|
	\| 0.7353 \| 50 \| 0.0732 \| 0.0971 \| 0.7929 \|
	\| 0.8824 \| 60 \| 0.0716 \| - \| - \|
	\| 1.0294 \| 70 \| 0.0665 \| - \| - \|
	\| 1.1765 \| 80 \| 0.0698 \| - \| - \|
	\| 1.3235 \| 90 \| 0.0699 \| - \| - \|
	\| 1.4706 \| 100 \| 0.0691 \| 0.0968 \| 0.7912 \|
	\| 1.6176 \| 110 \| 0.0687 \| - \| - \|
	\| 1.7647 \| 120 \| 0.0701 \| - \| - \|
	\| 1.9118 \| 130 \| 0.0689 \| - \| - \|
	\| 2.0588 \| 140 \| 0.0696 \| - \| - \|
	\| 2.2059 \| 150 \| 0.071 \| 0.0966 \| 0.7929 \|
	\| 2.3529 \| 160 \| 0.078 \| - \| - \|
	\| 2.5 \| 170 \| 0.0675 \| - \| - \|
	\| 2.6471 \| 180 \| 0.065 \| - \| - \|
	\| 2.7941 \| 190 \| 0.0684 \| - \| - \|
	\| 2.9412 \| 200 \| 0.0689 \| 0.0963 \| 0.7938 \|
	\| 3.0882 \| 210 \| 0.0736 \| - \| - \|
	\| 3.2353 \| 220 \| 0.0684 \| - \| - \|
	\| 3.3824 \| 230 \| 0.0669 \| - \| - \|
	\| 3.5294 \| 240 \| 0.0688 \| - \| - \|
	\| 3.6765 \| 250 \| 0.0678 \| 0.0959 \| 0.7963 \|
	\| 3.8235 \| 260 \| 0.0682 \| - \| - \|
	\| 3.9706 \| 270 \| 0.0678 \| - \| - \|
	\| 4.1176 \| 280 \| 0.0686 \| - \| - \|
	\| 4.2647 \| 290 \| 0.0664 \| - \| - \|
	\| 4.4118 \| 300 \| 0.0703 \| 0.0957 \| 0.7980 \|
	\| 4.5588 \| 310 \| 0.065 \| - \| - \|
	\| 4.7059 \| 320 \| 0.0719 \| - \| - \|
	\| 4.8529 \| 330 \| 0.0685 \| - \| - \|
	\| 5.0 \| 340 \| 0.0639 \| - \| - \|
	\| 5.1471 \| 350 \| 0.0667 \| 0.0957 \| 0.7971 \|
	\| 5.2941 \| 360 \| 0.0661 \| - \| - \|
	\| 5.4412 \| 370 \| 0.0678 \| - \| - \|
	\| 5.5882 \| 380 \| 0.0725 \| - \| - \|
	\| 5.7353 \| 390 \| 0.0655 \| - \| - \|
	\| 5.8824 \| 400 \| 0.0649 \| 0.0953 \| 0.7980 \|
	\| 6.0294 \| 410 \| 0.0661 \| - \| - \|
	\| 6.1765 \| 420 \| 0.0662 \| - \| - \|
	\| 6.3235 \| 430 \| 0.0671 \| - \| - \|
	\| 6.4706 \| 440 \| 0.0698 \| - \| - \|
	\| 6.6176 \| 450 \| 0.0636 \| 0.0951 \| 0.7980 \|
	\| 6.7647 \| 460 \| 0.0644 \| - \| - \|
	\| 6.9118 \| 470 \| 0.0633 \| - \| - \|
	\| 7.0588 \| 480 \| 0.0679 \| - \| - \|
	\| 7.2059 \| 490 \| 0.067 \| - \| - \|
	\| 7.3529 \| 500 \| 0.0713 \| 0.0948 \| 0.7963 \|
	\| 7.5 \| 510 \| 0.0677 \| - \| - \|
	\| 7.6471 \| 520 \| 0.0666 \| - \| - \|
	\| 7.7941 \| 530 \| 0.065 \| - \| - \|
	\| 7.9412 \| 540 \| 0.0665 \| - \| - \|
	\| 8.0882 \| 550 \| 0.0656 \| 0.0946 \| 0.7963 \|
	\| 8.2353 \| 560 \| 0.0649 \| - \| - \|
	\| 8.3824 \| 570 \| 0.0649 \| - \| - \|
	\| 8.5294 \| 580 \| 0.0653 \| - \| - \|
	\| 8.6765 \| 590 \| 0.0648 \| - \| - \|
	\| 8.8235 \| 600 \| 0.0622 \| 0.0944 \| 0.7946 \|
	\| 8.9706 \| 610 \| 0.0689 \| - \| - \|
	\| 9.1176 \| 620 \| 0.0711 \| - \| - \|
	\| 9.2647 \| 630 \| 0.0611 \| - \| - \|
	\| 9.4118 \| 640 \| 0.0697 \| - \| - \|
	\| 9.5588 \| 650 \| 0.0645 \| 0.0942 \| 0.7963 \|
	\| 9.7059 \| 660 \| 0.0639 \| - \| - \|
	\| 9.8529 \| 670 \| 0.0643 \| - \| - \|
	\| 10.0 \| 680 \| 0.0644 \| - \| - \|
	\| 10.1471 \| 690 \| 0.0599 \| - \| - \|
	\| 10.2941 \| 700 \| 0.0723 \| 0.0940 \| 0.7955 \|
	\| 10.4412 \| 710 \| 0.0652 \| - \| - \|
	\| 10.5882 \| 720 \| 0.0646 \| - \| - \|
	\| 10.7353 \| 730 \| 0.0602 \| - \| - \|
	\| 10.8824 \| 740 \| 0.0644 \| - \| - \|
	\| 11.0294 \| 750 \| 0.066 \| 0.0938 \| 0.7971 \|
	\| 11.1765 \| 760 \| 0.0624 \| - \| - \|
	\| 11.3235 \| 770 \| 0.0652 \| - \| - \|
	\| 11.4706 \| 780 \| 0.0649 \| - \| - \|
	\| 11.6176 \| 790 \| 0.0624 \| - \| - \|
	\| 11.7647 \| 800 \| 0.0626 \| 0.0937 \| 0.7988 \|
	\| 11.9118 \| 810 \| 0.0635 \| - \| - \|
	\| 12.0588 \| 820 \| 0.0643 \| - \| - \|
	\| 12.2059 \| 830 \| 0.0663 \| - \| - \|
	\| 12.3529 \| 840 \| 0.0641 \| - \| - \|
	\| 12.5 \| 850 \| 0.0614 \| 0.0933 \| 0.8005 \|
	\| 12.6471 \| 860 \| 0.0613 \| - \| - \|
	\| 12.7941 \| 870 \| 0.0648 \| - \| - \|
	\| 12.9412 \| 880 \| 0.065 \| - \| - \|
	\| 13.0882 \| 890 \| 0.0589 \| - \| - \|
	\| 13.2353 \| 900 \| 0.0632 \| 0.0931 \| 0.7997 \|
	\| 13.3824 \| 910 \| 0.0649 \| - \| - \|
	\| 13.5294 \| 920 \| 0.0612 \| - \| - \|
	\| 13.6765 \| 930 \| 0.0634 \| - \| - \|
	\| 13.8235 \| 940 \| 0.0637 \| - \| - \|
	\| 13.9706 \| 950 \| 0.0626 \| 0.0930 \| 0.7997 \|
	\| 14.1176 \| 960 \| 0.0593 \| - \| - \|
	\| 14.2647 \| 970 \| 0.0662 \| - \| - \|
	\| 14.4118 \| 980 \| 0.0644 \| - \| - \|
	\| 14.5588 \| 990 \| 0.0582 \| - \| - \|
	\| 14.7059 \| 1000 \| 0.0626 \| 0.0927 \| 0.8013 \|
	\| 14.8529 \| 1010 \| 0.0605 \| - \| - \|
	\| 15.0 \| 1020 \| 0.0615 \| - \| - \|
	\| 15.1471 \| 1030 \| 0.0676 \| - \| - \|
	\| 15.2941 \| 1040 \| 0.0633 \| - \| - \|
	\| 15.4412 \| 1050 \| 0.06 \| 0.0927 \| 0.8047 \|
	\| 15.5882 \| 1060 \| 0.0572 \| - \| - \|
	\| 15.7353 \| 1070 \| 0.0579 \| - \| - \|
	\| 15.8824 \| 1080 \| 0.0594 \| - \| - \|
	\| 16.0294 \| 1090 \| 0.063 \| - \| - \|
	\| 16.1765 \| 1100 \| 0.0581 \| 0.0927 \| 0.8030 \|
	\| 16.3235 \| 1110 \| 0.0564 \| - \| - \|
	\| 16.4706 \| 1120 \| 0.0632 \| - \| - \|
	\| 16.6176 \| 1130 \| 0.065 \| - \| - \|
	\| 16.7647 \| 1140 \| 0.0602 \| - \| - \|
	\| 16.9118 \| 1150 \| 0.0581 \| 0.0926 \| 0.8039 \|
	\| 17.0588 \| 1160 \| 0.0623 \| - \| - \|
	\| 17.2059 \| 1170 \| 0.06 \| - \| - \|
	\| 17.3529 \| 1180 \| 0.0562 \| - \| - \|
	\| 17.5 \| 1190 \| 0.0627 \| - \| - \|
	\| 17.6471 \| 1200 \| 0.056 \| 0.0924 \| 0.8013 \|
	\| 17.7941 \| 1210 \| 0.0586 \| - \| - \|
	\| 17.9412 \| 1220 \| 0.0576 \| - \| - \|
	\| 18.0882 \| 1230 \| 0.056 \| - \| - \|
	\| 18.2353 \| 1240 \| 0.0611 \| - \| - \|
	\| 18.3824 \| 1250 \| 0.0551 \| 0.0922 \| 0.8047 \|
	\| 18.5294 \| 1260 \| 0.058 \| - \| - \|
	\| 18.6765 \| 1270 \| 0.0571 \| - \| - \|
	\| 18.8235 \| 1280 \| 0.0616 \| - \| - \|
	\| 18.9706 \| 1290 \| 0.0599 \| - \| - \|
	\| 19.1176 \| 1300 \| 0.0604 \| 0.0920 \| 0.8081 \|
	\| 19.2647 \| 1310 \| 0.0633 \| - \| - \|
	\| 19.4118 \| 1320 \| 0.0573 \| - \| - \|
	\| 19.5588 \| 1330 \| 0.0549 \| - \| - \|
	\| 19.7059 \| 1340 \| 0.0591 \| - \| - \|
	\| 19.8529 \| 1350 \| 0.0585 \| 0.0918 \| 0.8089 \|
	\| 20.0 \| 1360 \| 0.057 \| - \| - \|
	\| 20.1471 \| 1370 \| 0.057 \| - \| - \|
	\| 20.2941 \| 1380 \| 0.0625 \| - \| - \|
	\| 20.4412 \| 1390 \| 0.0589 \| - \| - \|
	\| 20.5882 \| 1400 \| 0.0577 \| 0.0918 \| 0.8098 \|
	\| 20.7353 \| 1410 \| 0.0583 \| - \| - \|
	\| 20.8824 \| 1420 \| 0.0567 \| - \| - \|
	\| 21.0294 \| 1430 \| 0.0619 \| - \| - \|
	\| 21.1765 \| 1440 \| 0.0572 \| - \| - \|
	\| 21.3235 \| 1450 \| 0.0594 \| 0.0917 \| 0.8123 \|
	\| 21.4706 \| 1460 \| 0.0567 \| - \| - \|
	\| 21.6176 \| 1470 \| 0.0611 \| - \| - \|
	\| 21.7647 \| 1480 \| 0.0533 \| - \| - \|
	\| 21.9118 \| 1490 \| 0.0595 \| - \| - \|
	\| 22.0588 \| 1500 \| 0.0521 \| 0.0913 \| 0.8114 \|
	\| 22.2059 \| 1510 \| 0.0586 \| - \| - \|
	\| 22.3529 \| 1520 \| 0.0603 \| - \| - \|
	\| 22.5 \| 1530 \| 0.0601 \| - \| - \|
	\| 22.6471 \| 1540 \| 0.0567 \| - \| - \|
	\| 22.7941 \| 1550 \| 0.0551 \| 0.0911 \| 0.8114 \|
	\| 22.9412 \| 1560 \| 0.0542 \| - \| - \|
	\| 23.0882 \| 1570 \| 0.057 \| - \| - \|
	\| 23.2353 \| 1580 \| 0.0541 \| - \| - \|
	\| 23.3824 \| 1590 \| 0.0586 \| - \| - \|
	\| 23.5294 \| 1600 \| 0.0573 \| 0.0912 \| 0.8106 \|
	\| 23.6765 \| 1610 \| 0.0543 \| - \| - \|
	\| 23.8235 \| 1620 \| 0.0578 \| - \| - \|
	\| 23.9706 \| 1630 \| 0.0563 \| - \| - \|
	\| 24.1176 \| 1640 \| 0.0549 \| - \| - \|
	\| 24.2647 \| 1650 \| 0.0549 \| 0.0909 \| 0.8140 \|
	\| 24.4118 \| 1660 \| 0.056 \| - \| - \|
	\| 24.5588 \| 1670 \| 0.0599 \| - \| - \|
	\| 24.7059 \| 1680 \| 0.0543 \| - \| - \|
	\| 24.8529 \| 1690 \| 0.0547 \| - \| - \|
	\| 25.0 \| 1700 \| 0.0575 \| 0.0906 \| 0.8114 \|
	\| 25.1471 \| 1710 \| 0.0544 \| - \| - \|
	\| 25.2941 \| 1720 \| 0.0574 \| - \| - \|
	\| 25.4412 \| 1730 \| 0.0565 \| - \| - \|
	\| 25.5882 \| 1740 \| 0.0587 \| - \| - \|
	\| 25.7353 \| 1750 \| 0.0559 \| 0.0905 \| 0.8157 \|
	\| 25.8824 \| 1760 \| 0.0551 \| - \| - \|
	\| 26.0294 \| 1770 \| 0.0569 \| - \| - \|
	\| 26.1765 \| 1780 \| 0.0516 \| - \| - \|
	\| 26.3235 \| 1790 \| 0.0561 \| - \| - \|
	\| 26.4706 \| 1800 \| 0.0567 \| 0.0906 \| 0.8165 \|
	\| 26.6176 \| 1810 \| 0.0599 \| - \| - \|
	\| 26.7647 \| 1820 \| 0.0577 \| - \| - \|
	\| 26.9118 \| 1830 \| 0.0532 \| - \| - \|
	\| 27.0588 \| 1840 \| 0.0554 \| - \| - \|
	\| 27.2059 \| 1850 \| 0.0579 \| 0.0906 \| 0.8123 \|
	\| 27.3529 \| 1860 \| 0.0532 \| - \| - \|
	\| 27.5 \| 1870 \| 0.0493 \| - \| - \|
	\| 27.6471 \| 1880 \| 0.0552 \| - \| - \|
	\| 27.7941 \| 1890 \| 0.0532 \| - \| - \|
	\| 27.9412 \| 1900 \| 0.0569 \| 0.0904 \| 0.8089 \|
	\| 28.0882 \| 1910 \| 0.0568 \| - \| - \|
	\| 28.2353 \| 1920 \| 0.052 \| - \| - \|
	\| 28.3824 \| 1930 \| 0.0555 \| - \| - \|
	\| 28.5294 \| 1940 \| 0.0563 \| - \| - \|
	\| 28.6765 \| 1950 \| 0.0555 \| 0.0903 \| 0.8140 \|
	\| 28.8235 \| 1960 \| 0.0535 \| - \| - \|
	\| 28.9706 \| 1970 \| 0.0525 \| - \| - \|
	\| 29.1176 \| 1980 \| 0.0566 \| - \| - \|
	\| 29.2647 \| 1990 \| 0.0562 \| - \| - \|
	\| 29.4118 \| 2000 \| 0.0547 \| 0.0902 \| 0.8140 \|
	\| 29.5588 \| 2010 \| 0.0495 \| - \| - \|
	\| 29.7059 \| 2020 \| 0.0532 \| - \| - \|
	\| 29.8529 \| 2030 \| 0.0553 \| - \| - \|
	\| 30.0 \| 2040 \| 0.0544 \| - \| - \|

	</details>

	### Framework Versions
	- Python: 3.8.18
	- Sentence Transformers: 3.1.1
	- Transformers: 4.45.1
	- PyTorch: 1.13.1+cu117
	- Accelerate: 0.34.2
	- Datasets: 3.0.0
	- Tokenizers: 0.20.0

	## 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",
	}
	```

	#### TripletLoss
	```bibtex
	@misc{hermans2017defense,
	title={In Defense of the Triplet Loss for Person Re-Identification},
	author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
	year={2017},
	eprint={1703.07737},
	archivePrefix={arXiv},
	primaryClass={cs.CV}
	}
	```

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