multilingual-e5-small-aligned / README.md

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	---
	base_model: intfloat/multilingual-e5-small
	library_name: sentence-transformers
	pipeline_tag: sentence-similarity
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:867042
	- loss:MultipleNegativesRankingLoss
	widget:
	- source_sentence: An air strike.
	sentences:
	- מר פרקינסון היה מזועזע אם היה יודע איך מר פוקס מתנהג.
	- 'Sonia: Jangan berkata begitu.'
	- En luftattack.
	- source_sentence: The European Parliament has recently called for a guarantee that
	40 % of the 10 % target will come from sources that do not compete with food production.
	sentences:
	- L' ordre du jour appelle l' examen du projet définitif d' ordre du jour tel qu'
	il a été établi par la Conférence des présidents, le jeudi 13 janvier, conformément
	à l' article 110 du règlement.
	- می توانم با تمام وجود به این باور داشته باشم؟ می توانم در این باره چنین خشمگین
	باشم؟"
	- Europaparlamentet ba nylig om en garanti for at 40 % av de 10 % kommer fra kilder
	som ikke konkurrerer med matvareproduksjon.
	- source_sentence: In effect, this adds to the length of the workday and to its tensions.
	sentences:
	- Musimy wysłuchać opinii zainteresowanych stron, które rozwiązanie jest najatrakcyjniejsze
	dla spółek.
	- Вам надо держать себя в руках.
	- درحقیقت ،‏ یہ دن‌بھر کے کام اور اس سے وابستہ دباؤ میں اضافہ کرتا ہے ۔‏
	- source_sentence: A few HIV positive mothers NOT in their first pregnancy (one was
	in her ninth).
	sentences:
	- Beberapa ibu mengidap HIV positif TIDAK di kehamilan pertama mereka (salah satunya
	bahkan di kehamilan kesembilan).
	- Taigi, manau, kad taip ir pristatysiu jus – kaip pasakorę".
	- הוא איפשר ראייה לשני מיליון אנשים ללא תשלום.
	- source_sentence: What do they think it is that prevents the products of human ingenuity
	from being themselves, fruits of the tree of life, and hence, in some sense, obeying
	evolutionary rules?
	sentences:
	- 'Կարծում եք ի՞նչն է խանգարում, որ մարդկային հնարամտության արդյունքները իրենք էլ
	լինեն կյանքի ծառի պտուղներ և այդպիսով ինչ-որ իմաստով ենթարկվեն էվոլուցիայի կանոններին:'
	- Ja mēs varētu aktivēt šūnas, mēs varētu redzēt, kādus spēkus tās var atbrīvot,
	ko tās var ierosināt un ko stiprināt. Ja mēs tās varētu izslēgt,
	- (Smiech) No dobre, idem do Ameriky.
	---

	# SentenceTransformer based on intfloat/multilingual-e5-small

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small). It maps sentences & paragraphs to a 384-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: [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) <!-- at revision fd1525a9fd15316a2d503bf26ab031a61d056e98 -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 384 dimensions
	- 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': 384, '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})
	(2): Normalize()
	)
	```

	## 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("agentlans/multilingual-e5-small-aligned")
	# Run inference
	sentences = [
	'What do they think it is that prevents the products of human ingenuity from being themselves, fruits of the tree of life, and hence, in some sense, obeying evolutionary rules?',
	'Կարծում եք ի՞նչն է խանգարում, որ մարդկային հնարամտության արդյունքները իրենք էլ լինեն կյանքի ծառի պտուղներ և այդպիսով ինչ-որ իմաստով ենթարկվեն էվոլուցիայի կանոններին:',
	'(Smiech) No dobre, idem do Ameriky.',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 384]

	# 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: 867,042 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: 21.83 tokens</li><li>max: 177 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 24.92 tokens</li><li>max: 229 tokens</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \|
	\|:------------------------------------------------------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>I like English best of all subjects.</code> \| <code>Tykkään englannista eniten kaikista aineista.</code> \|
	\| <code>We shall offer negotiations. Quite right.</code> \| <code>- Oferecer-nos-emos para negociar.</code> \|
	\| <code>It was soon learned that Zelaya had been taken to Costa Rica, where he continued to call himself as the legal head of state.</code> \| <code>Al snel werd bekend dat Zelaya naar Costa Rica was overgebracht, waar hij zich nog steeds het officiële staatshoofd noemde.</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

	- `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
	- `torch_empty_cache_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
	- `include_for_metrics`: []
	- `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
	- `average_tokens_across_devices`: False
	- `prompts`: None
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: round_robin

	</details>

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

	\| Epoch \| Step \| Training Loss \|
	\|:------:\|:------:\|:-------------:\|
	\| 0.0046 \| 500 \| 0.0378 \|
	\| 0.0092 \| 1000 \| 0.0047 \|
	\| 0.0138 \| 1500 \| 0.006 \|
	\| 0.0185 \| 2000 \| 0.0045 \|
	\| 0.0231 \| 2500 \| 0.0027 \|
	\| 0.0277 \| 3000 \| 0.005 \|
	\| 0.0323 \| 3500 \| 0.0045 \|
	\| 0.0369 \| 4000 \| 0.005 \|
	\| 0.0415 \| 4500 \| 0.0066 \|
	\| 0.0461 \| 5000 \| 0.0029 \|
	\| 0.0507 \| 5500 \| 0.0041 \|
	\| 0.0554 \| 6000 \| 0.0064 \|
	\| 0.0600 \| 6500 \| 0.0044 \|
	\| 0.0646 \| 7000 \| 0.0039 \|
	\| 0.0692 \| 7500 \| 0.0025 \|
	\| 0.0738 \| 8000 \| 0.0026 \|
	\| 0.0784 \| 8500 \| 0.0036 \|
	\| 0.0830 \| 9000 \| 0.0027 \|
	\| 0.0877 \| 9500 \| 0.0015 \|
	\| 0.0923 \| 10000 \| 0.003 \|
	\| 0.0969 \| 10500 \| 0.0013 \|
	\| 0.1015 \| 11000 \| 0.002 \|
	\| 0.1061 \| 11500 \| 0.0038 \|
	\| 0.1107 \| 12000 \| 0.0017 \|
	\| 0.1153 \| 12500 \| 0.0029 \|
	\| 0.1199 \| 13000 \| 0.0032 \|
	\| 0.1246 \| 13500 \| 0.0036 \|
	\| 0.1292 \| 14000 \| 0.004 \|
	\| 0.1338 \| 14500 \| 0.0036 \|
	\| 0.1384 \| 15000 \| 0.0025 \|
	\| 0.1430 \| 15500 \| 0.0022 \|
	\| 0.1476 \| 16000 \| 0.0017 \|
	\| 0.1522 \| 16500 \| 0.0019 \|
	\| 0.1569 \| 17000 \| 0.0022 \|
	\| 0.1615 \| 17500 \| 0.0028 \|
	\| 0.1661 \| 18000 \| 0.0033 \|
	\| 0.1707 \| 18500 \| 0.0025 \|
	\| 0.1753 \| 19000 \| 0.0014 \|
	\| 0.1799 \| 19500 \| 0.0033 \|
	\| 0.1845 \| 20000 \| 0.0023 \|
	\| 0.1891 \| 20500 \| 0.0023 \|
	\| 0.1938 \| 21000 \| 0.0009 \|
	\| 0.1984 \| 21500 \| 0.0043 \|
	\| 0.2030 \| 22000 \| 0.0021 \|
	\| 0.2076 \| 22500 \| 0.0025 \|
	\| 0.2122 \| 23000 \| 0.0017 \|
	\| 0.2168 \| 23500 \| 0.0024 \|
	\| 0.2214 \| 24000 \| 0.0021 \|
	\| 0.2261 \| 24500 \| 0.0023 \|
	\| 0.2307 \| 25000 \| 0.0014 \|
	\| 0.2353 \| 25500 \| 0.0027 \|
	\| 0.2399 \| 26000 \| 0.0025 \|
	\| 0.2445 \| 26500 \| 0.0022 \|
	\| 0.2491 \| 27000 \| 0.0022 \|
	\| 0.2537 \| 27500 \| 0.0024 \|
	\| 0.2583 \| 28000 \| 0.0035 \|
	\| 0.2630 \| 28500 \| 0.0032 \|
	\| 0.2676 \| 29000 \| 0.0048 \|
	\| 0.2722 \| 29500 \| 0.0008 \|
	\| 0.2768 \| 30000 \| 0.0027 \|
	\| 0.2814 \| 30500 \| 0.004 \|
	\| 0.2860 \| 31000 \| 0.0013 \|
	\| 0.2906 \| 31500 \| 0.002 \|
	\| 0.2953 \| 32000 \| 0.0016 \|
	\| 0.2999 \| 32500 \| 0.0027 \|
	\| 0.3045 \| 33000 \| 0.0014 \|
	\| 0.3091 \| 33500 \| 0.0022 \|
	\| 0.3137 \| 34000 \| 0.0017 \|
	\| 0.3183 \| 34500 \| 0.0022 \|
	\| 0.3229 \| 35000 \| 0.0026 \|
	\| 0.3275 \| 35500 \| 0.003 \|
	\| 0.3322 \| 36000 \| 0.0022 \|
	\| 0.3368 \| 36500 \| 0.0022 \|
	\| 0.3414 \| 37000 \| 0.0018 \|
	\| 0.3460 \| 37500 \| 0.0028 \|
	\| 0.3506 \| 38000 \| 0.0018 \|
	\| 0.3552 \| 38500 \| 0.0037 \|
	\| 0.3598 \| 39000 \| 0.003 \|
	\| 0.3645 \| 39500 \| 0.002 \|
	\| 0.3691 \| 40000 \| 0.001 \|
	\| 0.3737 \| 40500 \| 0.0015 \|
	\| 0.3783 \| 41000 \| 0.0023 \|
	\| 0.3829 \| 41500 \| 0.0017 \|
	\| 0.3875 \| 42000 \| 0.0034 \|
	\| 0.3921 \| 42500 \| 0.0016 \|
	\| 0.3967 \| 43000 \| 0.0019 \|
	\| 0.4014 \| 43500 \| 0.0015 \|
	\| 0.4060 \| 44000 \| 0.0026 \|
	\| 0.4106 \| 44500 \| 0.0012 \|
	\| 0.4152 \| 45000 \| 0.0014 \|
	\| 0.4198 \| 45500 \| 0.0027 \|
	\| 0.4244 \| 46000 \| 0.0016 \|
	\| 0.4290 \| 46500 \| 0.0027 \|
	\| 0.4337 \| 47000 \| 0.0033 \|
	\| 0.4383 \| 47500 \| 0.0023 \|
	\| 0.4429 \| 48000 \| 0.0024 \|
	\| 0.4475 \| 48500 \| 0.0019 \|
	\| 0.4521 \| 49000 \| 0.0017 \|
	\| 0.4567 \| 49500 \| 0.004 \|
	\| 0.4613 \| 50000 \| 0.0036 \|
	\| 0.4659 \| 50500 \| 0.001 \|
	\| 0.4706 \| 51000 \| 0.0016 \|
	\| 0.4752 \| 51500 \| 0.0024 \|
	\| 0.4798 \| 52000 \| 0.0009 \|
	\| 0.4844 \| 52500 \| 0.0011 \|
	\| 0.4890 \| 53000 \| 0.0018 \|
	\| 0.4936 \| 53500 \| 0.0012 \|
	\| 0.4982 \| 54000 \| 0.0012 \|
	\| 0.5029 \| 54500 \| 0.0014 \|
	\| 0.5075 \| 55000 \| 0.0025 \|
	\| 0.5121 \| 55500 \| 0.0016 \|
	\| 0.5167 \| 56000 \| 0.0015 \|
	\| 0.5213 \| 56500 \| 0.002 \|
	\| 0.5259 \| 57000 \| 0.0008 \|
	\| 0.5305 \| 57500 \| 0.0017 \|
	\| 0.5351 \| 58000 \| 0.0015 \|
	\| 0.5398 \| 58500 \| 0.0009 \|
	\| 0.5444 \| 59000 \| 0.0019 \|
	\| 0.5490 \| 59500 \| 0.0014 \|
	\| 0.5536 \| 60000 \| 0.0028 \|
	\| 0.5582 \| 60500 \| 0.0014 \|
	\| 0.5628 \| 61000 \| 0.0032 \|
	\| 0.5674 \| 61500 \| 0.0013 \|
	\| 0.5721 \| 62000 \| 0.002 \|
	\| 0.5767 \| 62500 \| 0.0018 \|
	\| 0.5813 \| 63000 \| 0.0015 \|
	\| 0.5859 \| 63500 \| 0.0008 \|
	\| 0.5905 \| 64000 \| 0.0021 \|
	\| 0.5951 \| 64500 \| 0.0008 \|
	\| 0.5997 \| 65000 \| 0.002 \|
	\| 0.6043 \| 65500 \| 0.0023 \|
	\| 0.6090 \| 66000 \| 0.0022 \|
	\| 0.6136 \| 66500 \| 0.0013 \|
	\| 0.6182 \| 67000 \| 0.0011 \|
	\| 0.6228 \| 67500 \| 0.0014 \|
	\| 0.6274 \| 68000 \| 0.0027 \|
	\| 0.6320 \| 68500 \| 0.002 \|
	\| 0.6366 \| 69000 \| 0.0013 \|
	\| 0.6413 \| 69500 \| 0.0026 \|
	\| 0.6459 \| 70000 \| 0.0014 \|
	\| 0.6505 \| 70500 \| 0.0017 \|
	\| 0.6551 \| 71000 \| 0.0023 \|
	\| 0.6597 \| 71500 \| 0.0025 \|
	\| 0.6643 \| 72000 \| 0.0013 \|
	\| 0.6689 \| 72500 \| 0.0008 \|
	\| 0.6735 \| 73000 \| 0.0017 \|
	\| 0.6782 \| 73500 \| 0.0022 \|
	\| 0.6828 \| 74000 \| 0.0021 \|
	\| 0.6874 \| 74500 \| 0.0008 \|
	\| 0.6920 \| 75000 \| 0.0007 \|
	\| 0.6966 \| 75500 \| 0.0038 \|
	\| 0.7012 \| 76000 \| 0.0011 \|
	\| 0.7058 \| 76500 \| 0.0016 \|
	\| 0.7105 \| 77000 \| 0.0013 \|
	\| 0.7151 \| 77500 \| 0.0042 \|
	\| 0.7197 \| 78000 \| 0.0009 \|
	\| 0.7243 \| 78500 \| 0.0004 \|
	\| 0.7289 \| 79000 \| 0.0006 \|
	\| 0.7335 \| 79500 \| 0.0007 \|
	\| 0.7381 \| 80000 \| 0.0014 \|
	\| 0.7428 \| 80500 \| 0.002 \|
	\| 0.7474 \| 81000 \| 0.0017 \|
	\| 0.7520 \| 81500 \| 0.0014 \|
	\| 0.7566 \| 82000 \| 0.0015 \|
	\| 0.7612 \| 82500 \| 0.0013 \|
	\| 0.7658 \| 83000 \| 0.001 \|
	\| 0.7704 \| 83500 \| 0.0019 \|
	\| 0.7750 \| 84000 \| 0.0009 \|
	\| 0.7797 \| 84500 \| 0.0021 \|
	\| 0.7843 \| 85000 \| 0.0015 \|
	\| 0.7889 \| 85500 \| 0.001 \|
	\| 0.7935 \| 86000 \| 0.0008 \|
	\| 0.7981 \| 86500 \| 0.0039 \|
	\| 0.8027 \| 87000 \| 0.0018 \|
	\| 0.8073 \| 87500 \| 0.0009 \|
	\| 0.8120 \| 88000 \| 0.0018 \|
	\| 0.8166 \| 88500 \| 0.0008 \|
	\| 0.8212 \| 89000 \| 0.0007 \|
	\| 0.8258 \| 89500 \| 0.0009 \|
	\| 0.8304 \| 90000 \| 0.002 \|
	\| 0.8350 \| 90500 \| 0.001 \|
	\| 0.8396 \| 91000 \| 0.0007 \|
	\| 0.8442 \| 91500 \| 0.0008 \|
	\| 0.8489 \| 92000 \| 0.0021 \|
	\| 0.8535 \| 92500 \| 0.0013 \|
	\| 0.8581 \| 93000 \| 0.0009 \|
	\| 0.8627 \| 93500 \| 0.002 \|
	\| 0.8673 \| 94000 \| 0.0012 \|
	\| 0.8719 \| 94500 \| 0.0034 \|
	\| 0.8765 \| 95000 \| 0.0027 \|
	\| 0.8812 \| 95500 \| 0.0006 \|
	\| 0.8858 \| 96000 \| 0.002 \|
	\| 0.8904 \| 96500 \| 0.0005 \|
	\| 0.8950 \| 97000 \| 0.0009 \|
	\| 0.8996 \| 97500 \| 0.0007 \|
	\| 0.9042 \| 98000 \| 0.0015 \|
	\| 0.9088 \| 98500 \| 0.0006 \|
	\| 0.9134 \| 99000 \| 0.0004 \|
	\| 0.9181 \| 99500 \| 0.0006 \|
	\| 0.9227 \| 100000 \| 0.0031 \|
	\| 0.9273 \| 100500 \| 0.0013 \|
	\| 0.9319 \| 101000 \| 0.0024 \|
	\| 0.9365 \| 101500 \| 0.0006 \|
	\| 0.9411 \| 102000 \| 0.0017 \|
	\| 0.9457 \| 102500 \| 0.0007 \|
	\| 0.9504 \| 103000 \| 0.0012 \|
	\| 0.9550 \| 103500 \| 0.0011 \|
	\| 0.9596 \| 104000 \| 0.0007 \|
	\| 0.9642 \| 104500 \| 0.0004 \|
	\| 0.9688 \| 105000 \| 0.0021 \|
	\| 0.9734 \| 105500 \| 0.0027 \|
	\| 0.9780 \| 106000 \| 0.0016 \|
	\| 0.9826 \| 106500 \| 0.0022 \|
	\| 0.9873 \| 107000 \| 0.0017 \|
	\| 0.9919 \| 107500 \| 0.0009 \|
	\| 0.9965 \| 108000 \| 0.0008 \|

	</details>

	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.3.0
	- Transformers: 4.46.3
	- PyTorch: 2.5.1+cu124
	- Accelerate: 1.1.1
	- Datasets: 3.1.0
	- Tokenizers: 0.20.3

	## 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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