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	---
	language: []
	library_name: sentence-transformers
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:362208
	- loss:ContrastiveLoss
	base_model: vinai/phobert-base-v2
	datasets: []
	widget:
	- source_sentence: vùng khí_hậu nóng ẩm ghi_nhận sự tồn_tại của Virus SARS-CoV -2
	.
	sentences:
	- vùng khí_hậu nóng ẩm ghi_nhận sự tồn_tại của Virus SARS-CoV -2 .
	- Chế_độ ăn_uống nghỉ_ngơi như_thế_nào là hợp_lý ?
	- Và bé đang có tình_trạng bị đọng cặn nước_tiểu ở đầu bộ sinh_dục ( bé trai ) .
	- source_sentence: Khoan xương và chèn vào hai bên hai bong_bóng được gọi là đệm xương
	.
	sentences:
	- Khoan xương và chèn vào hai bên hai bong_bóng được gọi là đệm xương .
	- 3 hôm_nay bé đi phân chủ_yếu là nhầy có bọt rồi nước lẫn hoa_cà_hoa_cải , thi_thoảng
	phân có màu xanh đậm .
	- Sau khi chẩn_đoán , bác_sĩ khuyên anh Khánh nên sớm điều_trị kịp_thời để tránh
	nhiều biến_chứng .
	- source_sentence: Những phụ_nữ có tiền_sử bệnh như trên chính là những đối_tượng
	nguy_cơ của tình_trạng tắc ống dẫn trứng .
	sentences:
	- 'Dùng các thiết_bị hỗ_trợ quá_trình di_chuyển đồng_thời giúp cải_thiện chức_năng
	của các khớp như :'
	- Những phụ_nữ có tiền_sử bệnh như trên chính là những đối_tượng nguy_cơ của tình_trạng
	tắc ống dẫn trứng .
	- Trong các nguyên_nhân sau đây , đâu là các nguyên_nhân khách_quan , không đến
	từ mẹ và thai_nhi ?
	- source_sentence: Bé nhà con nay được 1 tháng 23 ngày .
	sentences:
	- 'Vú phải : - bất đối_xứng ở vùng dưới ( kích_thước :'
	- Thưa bác_sĩ tôi 18 tuổi , bị sưng chân răng , nhức tai , nhức đầu với có kêu tiếng
	trong quai_hàm .
	- Bé nhà con nay được 1 tháng 23 ngày .
	- source_sentence: Tuy_nhiên , nếu bệnh không tự lành và vẫn tiếp_tục chảy_máu , cần
	phải sử_dụng các liệu_pháp cầm máu để bù lại lượng máu đã mất .
	sentences:
	- Nguyễn_Thị_Thanh_Tuyền ( 1995 ) .
	- Bệnh_viện Bệnh Nhiệt_đới Trung_ương cơ_sở Kim_Chung là bệnh_viện khám_chữa bệnh
	đa_khoa phục_vụ cho người_dân trong cả nước .
	- 'Một_số yếu_tố làm tăng nguy_cơ mắc bệnh như : Yếu_tố nội_tiết : bệnh thường gặp
	ở phụ_nữ chậm có kinh và sớm mãn_kinh .'
	pipeline_tag: sentence-similarity
	---

	# SentenceTransformer based on vinai/phobert-base-v2

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2). 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: [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) <!-- at revision 2b51e367d92093c9688112098510e6a58bab67cd -->
	- Maximum Sequence Length: 256 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': 256, 'do_lower_case': False}) with Transformer model: RobertaModel
	(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("sentence_transformers_model_id")
	# Run inference
	sentences = [
	'Tuy_nhiên , nếu bệnh không tự lành và vẫn tiếp_tục chảy_máu , cần phải sử_dụng các liệu_pháp cầm máu để bù lại lượng máu đã mất .',
	'Một_số yếu_tố làm tăng nguy_cơ mắc bệnh như : Yếu_tố nội_tiết : bệnh thường gặp ở phụ_nữ chậm có kinh và sớm mãn_kinh .',
	'Nguyễn_Thị_Thanh_Tuyền ( 1995 ) .',
	]
	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]
	```

	<!--
	### 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.
	-->

	<!--
	## 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 Dataset

	#### Unnamed Dataset


	* Size: 362,208 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 \| float \|
	\| details \| <ul><li>min: 3 tokens</li><li>mean: 22.64 tokens</li><li>max: 104 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 23.25 tokens</li><li>max: 222 tokens</li></ul> \| <ul><li>min: 0.1</li><li>mean: 0.82</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \| label \|
	\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------\|:--------------------------------\|
	\| <code>Hiệu_lực của vaccine AstraZeneca ra sao ?</code> \| <code>Hiệu_lực của vaccine AstraZeneca ra sao ?</code> \| <code>1.0</code> \|
	\| <code>Gần đây , tôi có quen một bạn gái , mỗi lần ngồi gần nhau có cử_chỉ thân_mật thì tôi gần như không kìm chế được có_thể nói là giống như hiện_tượng xuất_tinh sớm .</code> \| <code>Chụp CT scanner sọ não : là hình_ảnh tốt nhất để đánh_giá tổn_thương não vì có_thể hiển_thị mô não hoặc xuất_huyết não hoặc nhũn_não .</code> \| <code>0.6540138125419617</code> \|
	\| <code>Sốt siêu_vi sau quan_hệ tình_dục không an_toàn có phải đã nhiễm HIV không ?</code> \| <code>Sốt siêu_vi sau quan_hệ tình_dục không an_toàn có phải đã nhiễm HIV không ?</code> \| <code>1.0</code> \|
	* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
	```json
	{
	"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
	"margin": 0.5,
	"size_average": true
	}
	```

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

	- `per_device_train_batch_size`: 16
	- `per_device_eval_batch_size`: 16
	- `num_train_epochs`: 4
	- `multi_dataset_batch_sampler`: round_robin

	#### All Hyperparameters
	<details><summary>Click to expand</summary>

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `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`: 4
	- `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
	- `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}
	- `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
	- `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_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: round_robin

	</details>

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

	\| Epoch \| Step \| Training Loss \|
	\|:------:\|:-----:\|:-------------:\|
	\| 0.0221 \| 500 \| 0.0168 \|
	\| 0.0442 \| 1000 \| 0.0139 \|
	\| 0.0663 \| 1500 \| 0.0142 \|
	\| 0.0883 \| 2000 \| 0.0139 \|
	\| 0.1104 \| 2500 \| 0.0137 \|
	\| 0.1325 \| 3000 \| 0.0139 \|
	\| 0.1546 \| 3500 \| 0.0137 \|
	\| 0.1767 \| 4000 \| 0.0139 \|
	\| 0.1988 \| 4500 \| 0.0136 \|
	\| 0.2209 \| 5000 \| 0.0135 \|
	\| 0.2430 \| 5500 \| 0.0137 \|
	\| 0.2650 \| 6000 \| 0.0138 \|
	\| 0.2871 \| 6500 \| 0.0136 \|
	\| 0.3092 \| 7000 \| 0.0137 \|
	\| 0.3313 \| 7500 \| 0.0138 \|
	\| 0.3534 \| 8000 \| 0.0135 \|
	\| 0.3755 \| 8500 \| 0.0138 \|
	\| 0.3976 \| 9000 \| 0.0138 \|
	\| 0.4196 \| 9500 \| 0.0141 \|
	\| 0.4417 \| 10000 \| 0.0139 \|
	\| 0.4638 \| 10500 \| 0.0139 \|
	\| 0.4859 \| 11000 \| 0.0138 \|
	\| 0.5080 \| 11500 \| 0.0141 \|
	\| 0.5301 \| 12000 \| 0.0138 \|
	\| 0.5522 \| 12500 \| 0.0138 \|
	\| 0.5743 \| 13000 \| 0.0138 \|
	\| 0.5963 \| 13500 \| 0.0138 \|
	\| 0.6184 \| 14000 \| 0.0136 \|
	\| 0.6405 \| 14500 \| 0.0139 \|
	\| 0.6626 \| 15000 \| 0.0151 \|
	\| 0.6847 \| 15500 \| 0.019 \|
	\| 0.7068 \| 16000 \| 0.0184 \|
	\| 0.7289 \| 16500 \| 0.018 \|
	\| 0.7509 \| 17000 \| 0.0163 \|
	\| 0.7730 \| 17500 \| 0.0164 \|
	\| 0.7951 \| 18000 \| 0.0158 \|
	\| 0.8172 \| 18500 \| 0.0155 \|
	\| 0.8393 \| 19000 \| 0.0151 \|
	\| 0.8614 \| 19500 \| 0.0151 \|
	\| 0.8835 \| 20000 \| 0.0152 \|
	\| 0.9056 \| 20500 \| 0.0152 \|
	\| 0.9276 \| 21000 \| 0.0151 \|
	\| 0.9497 \| 21500 \| 0.0148 \|
	\| 0.9718 \| 22000 \| 0.015 \|
	\| 0.9939 \| 22500 \| 0.0147 \|
	\| 1.0160 \| 23000 \| 0.0149 \|
	\| 1.0381 \| 23500 \| 0.0151 \|
	\| 1.0602 \| 24000 \| 0.015 \|
	\| 1.0823 \| 24500 \| 0.0148 \|
	\| 1.1043 \| 25000 \| 0.0147 \|
	\| 1.1264 \| 25500 \| 0.0149 \|
	\| 1.1485 \| 26000 \| 0.0147 \|
	\| 1.1706 \| 26500 \| 0.015 \|
	\| 1.1927 \| 27000 \| 0.0146 \|
	\| 1.2148 \| 27500 \| 0.0145 \|
	\| 1.2369 \| 28000 \| 0.0147 \|
	\| 1.2589 \| 28500 \| 0.0149 \|
	\| 1.2810 \| 29000 \| 0.0147 \|
	\| 1.3031 \| 29500 \| 0.0144 \|
	\| 1.3252 \| 30000 \| 0.0147 \|
	\| 1.3473 \| 30500 \| 0.0147 \|
	\| 1.3694 \| 31000 \| 0.0145 \|
	\| 1.3915 \| 31500 \| 0.0149 \|
	\| 1.4136 \| 32000 \| 0.0147 \|
	\| 1.4356 \| 32500 \| 0.0148 \|
	\| 1.4577 \| 33000 \| 0.0148 \|
	\| 1.4798 \| 33500 \| 0.0145 \|
	\| 1.5019 \| 34000 \| 0.0149 \|
	\| 1.5240 \| 34500 \| 0.0147 \|
	\| 1.5461 \| 35000 \| 0.0146 \|
	\| 1.5682 \| 35500 \| 0.0144 \|
	\| 1.5902 \| 36000 \| 0.0146 \|
	\| 1.6123 \| 36500 \| 0.0143 \|
	\| 1.6344 \| 37000 \| 0.0145 \|
	\| 1.6565 \| 37500 \| 0.0145 \|
	\| 1.6786 \| 38000 \| 0.0146 \|
	\| 1.7007 \| 38500 \| 0.0143 \|
	\| 1.7228 \| 39000 \| 0.0149 \|
	\| 1.7449 \| 39500 \| 0.0143 \|
	\| 1.7669 \| 40000 \| 0.0146 \|
	\| 1.7890 \| 40500 \| 0.0146 \|
	\| 1.8111 \| 41000 \| 0.0146 \|
	\| 1.8332 \| 41500 \| 0.0142 \|
	\| 1.8553 \| 42000 \| 0.0144 \|
	\| 1.8774 \| 42500 \| 0.0146 \|
	\| 1.8995 \| 43000 \| 0.0147 \|
	\| 1.9215 \| 43500 \| 0.0144 \|
	\| 1.9436 \| 44000 \| 0.0145 \|
	\| 1.9657 \| 44500 \| 0.0143 \|
	\| 1.9878 \| 45000 \| 0.0146 \|
	\| 2.0099 \| 45500 \| 0.0143 \|
	\| 2.0320 \| 46000 \| 0.0147 \|
	\| 2.0541 \| 46500 \| 0.0146 \|
	\| 2.0762 \| 47000 \| 0.0144 \|
	\| 2.0982 \| 47500 \| 0.0144 \|
	\| 2.1203 \| 48000 \| 0.0144 \|
	\| 2.1424 \| 48500 \| 0.0145 \|
	\| 2.1645 \| 49000 \| 0.0144 \|
	\| 2.1866 \| 49500 \| 0.0144 \|
	\| 2.2087 \| 50000 \| 0.0141 \|
	\| 2.2308 \| 50500 \| 0.0142 \|
	\| 2.2528 \| 51000 \| 0.0145 \|
	\| 2.2749 \| 51500 \| 0.0143 \|
	\| 2.2970 \| 52000 \| 0.0141 \|
	\| 2.3191 \| 52500 \| 0.0144 \|
	\| 2.3412 \| 53000 \| 0.0143 \|
	\| 2.3633 \| 53500 \| 0.0144 \|
	\| 2.3854 \| 54000 \| 0.0144 \|
	\| 2.4075 \| 54500 \| 0.0144 \|
	\| 2.4295 \| 55000 \| 0.0145 \|
	\| 2.4516 \| 55500 \| 0.0145 \|
	\| 2.4737 \| 56000 \| 0.0144 \|
	\| 2.4958 \| 56500 \| 0.0147 \|
	\| 2.5179 \| 57000 \| 0.0145 \|
	\| 2.5400 \| 57500 \| 0.0144 \|
	\| 2.5621 \| 58000 \| 0.0143 \|
	\| 2.5842 \| 58500 \| 0.0144 \|
	\| 2.6062 \| 59000 \| 0.0143 \|
	\| 2.6283 \| 59500 \| 0.0142 \|
	\| 2.6504 \| 60000 \| 0.0143 \|
	\| 2.6725 \| 60500 \| 0.0143 \|
	\| 2.6946 \| 61000 \| 0.0143 \|
	\| 2.7167 \| 61500 \| 0.0144 \|
	\| 2.7388 \| 62000 \| 0.0143 \|
	\| 2.7608 \| 62500 \| 0.0143 \|
	\| 2.7829 \| 63000 \| 0.0146 \|
	\| 2.8050 \| 63500 \| 0.0144 \|
	\| 2.8271 \| 64000 \| 0.0141 \|
	\| 2.8492 \| 64500 \| 0.0142 \|
	\| 2.8713 \| 65000 \| 0.0143 \|
	\| 2.8934 \| 65500 \| 0.0146 \|
	\| 2.9155 \| 66000 \| 0.0143 \|
	\| 2.9375 \| 66500 \| 0.0143 \|
	\| 2.9596 \| 67000 \| 0.0141 \|
	\| 2.9817 \| 67500 \| 0.0144 \|
	\| 3.0038 \| 68000 \| 0.0143 \|
	\| 3.0259 \| 68500 \| 0.0145 \|
	\| 3.0480 \| 69000 \| 0.0142 \|
	\| 3.0701 \| 69500 \| 0.0145 \|
	\| 3.0921 \| 70000 \| 0.0142 \|
	\| 3.1142 \| 70500 \| 0.0143 \|
	\| 3.1363 \| 71000 \| 0.0142 \|
	\| 3.1584 \| 71500 \| 0.0143 \|
	\| 3.1805 \| 72000 \| 0.0143 \|
	\| 3.2026 \| 72500 \| 0.014 \|
	\| 3.2247 \| 73000 \| 0.0141 \|
	\| 3.2468 \| 73500 \| 0.0142 \|
	\| 3.2688 \| 74000 \| 0.0143 \|
	\| 3.2909 \| 74500 \| 0.0141 \|
	\| 3.3130 \| 75000 \| 0.0141 \|
	\| 3.3351 \| 75500 \| 0.0143 \|
	\| 3.3572 \| 76000 \| 0.0141 \|
	\| 3.3793 \| 76500 \| 0.0143 \|
	\| 3.4014 \| 77000 \| 0.0143 \|
	\| 3.4234 \| 77500 \| 0.0146 \|
	\| 3.4455 \| 78000 \| 0.0144 \|
	\| 3.4676 \| 78500 \| 0.0143 \|
	\| 3.4897 \| 79000 \| 0.0144 \|
	\| 3.5118 \| 79500 \| 0.0145 \|
	\| 3.5339 \| 80000 \| 0.0142 \|
	\| 3.5560 \| 80500 \| 0.0144 \|
	\| 3.5781 \| 81000 \| 0.0143 \|
	\| 3.6001 \| 81500 \| 0.0142 \|
	\| 3.6222 \| 82000 \| 0.0142 \|
	\| 3.6443 \| 82500 \| 0.0142 \|
	\| 3.6664 \| 83000 \| 0.014 \|
	\| 3.6885 \| 83500 \| 0.0144 \|
	\| 3.7106 \| 84000 \| 0.0141 \|
	\| 3.7327 \| 84500 \| 0.0143 \|
	\| 3.7547 \| 85000 \| 0.014 \|
	\| 3.7768 \| 85500 \| 0.0146 \|
	\| 3.7989 \| 86000 \| 0.0143 \|
	\| 3.8210 \| 86500 \| 0.0142 \|
	\| 3.8431 \| 87000 \| 0.0139 \|
	\| 3.8652 \| 87500 \| 0.0143 \|
	\| 3.8873 \| 88000 \| 0.0144 \|
	\| 3.9094 \| 88500 \| 0.0143 \|
	\| 3.9314 \| 89000 \| 0.0142 \|
	\| 3.9535 \| 89500 \| 0.0142 \|
	\| 3.9756 \| 90000 \| 0.0142 \|

	</details>

	### Framework Versions
	- Python: 3.10.13
	- Sentence Transformers: 3.1.0.dev0
	- Transformers: 4.39.3
	- PyTorch: 2.1.2
	- Accelerate: 0.29.3
	- Datasets: 2.18.0
	- Tokenizers: 0.15.2

	## 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",
	}
	```

	#### ContrastiveLoss
	```bibtex
	@inproceedings{hadsell2006dimensionality,
	author={Hadsell, R. and Chopra, S. and LeCun, Y.},
	booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
	title={Dimensionality Reduction by Learning an Invariant Mapping},
	year={2006},
	volume={2},
	number={},
	pages={1735-1742},
	doi={10.1109/CVPR.2006.100}
	}
	```

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