Add new SentenceTransformer model.

d1789b7 verified 7 months ago

19.4 kB

	---
	base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
	datasets: []
	language: []
	library_name: sentence-transformers
	pipeline_tag: sentence-similarity
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:64000
	- loss:DenoisingAutoEncoderLoss
	widget:
	- source_sentence: 𑀟चन𑀙𑀢𑀟 𑀞च𑀪च𑀠च 𑀫𑁣प𑁣 𑀞न𑀠च 𑀞𑁣𑀱च ब𑀢𑀪𑀠च𑀯
	sentences:
	- ' णच ब𑀢𑀪𑀠च पच𑀪𑁦 𑀣च 𑀠च𑀫च𑀢𑀲𑀢णच𑀪𑀳च 𑀣च झच𑀟𑁦𑀟𑀳च ञचणच𑀦 𑀞च𑀠च𑀪 णच𑀣𑀣च 𑀠च𑀫च𑀢𑀲𑀢𑀟𑀳च णच ढच𑀪
	𑀢णचल𑀢𑀯'
	- ' 𑀣च𑀟बच𑀟𑁦 𑀣च 𑀟चन𑀙𑀢𑀟 𑀠𑁣पच𑀪𑀦 पच𑀟च 𑀢णच 𑀤च𑀠च ढचढढच 𑀞𑁣 𑀞च𑀪च𑀠च 𑀢𑀣च𑀟 च𑀞च 𑀞𑀱चपच𑀟पच 𑀣च
	𑀠𑁣पच𑀪 𑀣चन𑀞च𑀪 𑀫𑁣प𑁣 𑀣च 𑀳नख𑀦 𑀞न𑀠च णच 𑀲𑀢 𑀟च 𑀞𑁣𑀱च ब𑀢𑀪𑀠च𑀯'
	- पच𑀪𑁦𑀠𑀢 णच ढनबच 𑀱च झन𑀟ब𑀢णच𑀪 झ𑀱चलल𑁣𑀟 झच𑀲च पच ञचल𑀢ढ𑀢𑀟 झच𑀳च𑀪 𑀢𑀪च𑀟 च बच𑀳च𑀪 पन𑀪𑀞𑀢णणच
	𑀞न𑀠च णच त𑀢 𑀱च झन𑀟ब𑀢णच𑀪 𑀞𑀱चललचण𑁦 थ𑀯
	- source_sentence: णच𑀟च बचढच 𑀣च लन𑀪च 𑀣च 𑀣च पच 𑀲𑀢 𑀣च
	sentences:
	- 𑀘𑁣𑀫𑀟 𑀠𑀢त𑀫च𑁦ल 𑁣ब𑀢𑀣𑀢 𑀝च𑀟 𑀫च𑀢𑀲𑁦𑀳𑀫𑀢 𑀪च𑀟च𑀪 𑀗 बच 𑀱चपच𑀟 𑀣𑀢𑀳च𑀠ढच𑀦 𑀭थ𑀖थ𑀮𑀯
	- ' 𑀱च𑀟𑀟च𑀟 णच𑀟च पच𑀢𑀠च𑀞च 𑀱च झ𑀱च𑀪च𑀪𑀪न𑀟 𑀫𑀪 𑀳न त𑀢 बचढच 𑀣च लन𑀪च 𑀣च 𑀣न𑀞 ढनञचञञ𑁦𑀟 चणणन𑀞च𑀟𑀳न
	𑀣च 𑀠च𑀳न 𑀟𑁦𑀠च पच 𑀫च𑀟णच𑀪 𑀣च पच 𑀲𑀢 𑀳चन𑀪𑀢 𑀣च 𑀳चनझ𑀢 𑀲𑀢ण𑁦 𑀣च 𑀣च𑀯'
	- ' च 𑀞च𑀪𑀞च𑀳𑀫𑀢𑀟 𑀣𑁣𑀞च𑀪𑀦 𑀠च𑀘चल𑀢𑀳च𑀪 लचनण𑁣ण𑀢𑀟 𑀢𑀟𑀣𑀢णच 𑀢पच त𑁦 ढचढढच𑀪 𑀫न𑀞न𑀠च𑀪 𑀞नलच 𑀣च 𑀫च𑀪𑀞𑁣𑀞𑀢𑀟
	𑀳𑀫च𑀪𑀢𑀙च च 𑀢𑀟𑀣𑀢णच 𑀣च 𑀞न𑀠च पचढढचपच𑀪 𑀣च ढ𑀢𑀟 𑀣𑁣𑀞च 𑀣च 𑀞𑀢णचण𑁦 𑀞च𑀙𑀢𑀣𑁣𑀘𑀢𑀟 𑀞𑀱च𑀪च𑀪𑀪न पच
	𑀫च𑀟णच𑀪 𑀞𑀱च𑀪च𑀪𑀪न𑀟 लचनणच च 𑀞च𑀳च𑀪𑀯'
	- source_sentence: 𑀣नढच ढढत𑀕 𑀠च𑀠च𑀪 चलचप𑁣न𑀠𑀢
	sentences:
	- 𑀣नढच 𑀞न𑀠च 𑀣𑁦𑀟𑀞ष𑀣𑁦𑀟𑀞𑀠च𑀟च𑀤च𑀪पच ढढत𑀕 𑀠च𑀠च𑀪 𑀞च𑀳𑀳𑁦ण चलचप𑁣न𑀠𑀢 𑀯
	- ' च𑀟 𑀲च𑀪च 𑀳च𑀠च𑀪𑀱च 𑀞न𑀠च 𑀣चबच ढचणच च𑀟 𑀲च𑀣च𑀣च चणणन𑀞च𑀟 बच 𑀳चन𑀪च𑀟 𑀢णचलच𑀢 𑀟च 𑀟च𑀘𑁦𑀪𑀢णच
	𑀠च𑀳न णच𑀪च𑀯'
	- ' 𑀫च𑁥च𑀞च च𑀤चढपच𑀪𑀱च णच𑀟च 𑀣च 𑀱च𑀫चलच 𑀠न𑀳च𑀠𑀠च𑀟 च त𑀢𑀞𑀢𑀟 चणणन𑀞च𑀟 णचझ𑀢 𑀣च पच𑀱चबच𑀪𑀯'
	- source_sentence: च𑀟
	sentences:
	- 𑀠नपन𑀱च च 𑀪च𑀟च𑀪 र बच 𑀱चपच𑀟 𑀠चणन𑀟 ठ𑀧𑀧ठ𑀦 च𑀞न 𑀟च त𑀢𑀞𑀢𑀟 𑀲च𑀳𑀢𑀟𑀘𑁣𑀘𑀢 𑀬𑀧 𑀣च 𑀞𑁦 त𑀢𑀞𑀢𑀟 𑀱च𑀟𑀢
	𑀘𑀢𑀪ब𑀢𑀟 𑀣च णच ण𑀢 𑀫चप𑀳च𑀪𑀢𑀟 𑀠𑀢𑀟पन𑀟च 𑀞चञच𑀟 ढचणच𑀟 पच𑀳𑀫𑀢𑀟𑀳च च 𑀞च𑀟𑁣𑀯
	- ' च𑀟 ण𑀢 𑀢𑀠च𑀟𑀢𑀟 𑀳𑀯'
	- ' 𑀲च𑀫च𑀣 णच 𑀞च𑀠𑀠चलच 𑀞च𑀞च𑀪 ठ𑀧𑀭ठट𑀭𑀰 𑀣च 𑀞𑀱चललचण𑁦 𑀭𑀧 𑀠च𑀳न ढच𑀟 𑀳𑀫च𑀙च𑀱च च 𑀱च𑀳च𑀟𑀟𑀢 ठ𑁢
	च 𑀣न𑀞 बच𑀳च𑀯'
	- source_sentence: ब𑀫𑁣𑀳प 𑀢𑀢 𑀳𑀫𑀢𑀟𑁦 𑀠च𑀲𑀢 𑀠च𑀫𑀢𑀠𑀠च𑀟त𑀢𑀦 पच𑀢𑀠च𑀞𑁣𑀟 𑀣च 𑀲च𑀳चलनललन𑀞च णच𑀟च ढच
	𑀠च𑀤चन𑀟च त𑀢𑀞𑀢𑀟 𑀫च𑀟𑀞चल𑀢 णचण𑀢𑀟
	sentences:
	- च𑀠𑀢𑀟पचतत𑀢णच च त𑀢𑀞𑀢𑀟 ब𑀫𑁣𑀳प 𑀳𑁦𑀪𑀢𑁦𑀳 𑀢𑀢 𑀳𑀫𑀢𑀟𑁦 𑀠च𑀲𑀢 𑀠च𑀫𑀢𑀠𑀠च𑀟त𑀢𑀦 पच𑀪𑁦 𑀣च ञ𑀢𑀠ढ𑀢𑀟 𑀢𑀟बच𑀟पचपपन𑀟
	प𑀳च𑀪𑀢𑀟 पच𑀢𑀠च𑀞𑁣𑀟 𑀣𑀢𑀪𑁦ढच 𑀣च 𑀲च𑀳चलनललन𑀞च 𑀟च च𑀠𑀢𑀟त𑀢𑀦 णच𑀟च ढच 𑀠च𑀤चन𑀟च त𑀢𑀞𑀢𑀟 𑀞𑀱च𑀟त𑀢णच𑀪
	𑀫च𑀟𑀞चल𑀢 णचण𑀢𑀟 पच𑀲𑀢णच𑀪𑀳न𑀯
	- प𑁣ध𑀳ण ध𑀫𑀢𑀪𑀢 𑀝च𑀟 𑀫च𑀢𑀲𑁦 𑀳𑀫𑀢 च 𑀪च𑀟च𑀪 𑀭𑀭 बच 𑀱चपच𑀟 चबन𑀳पच 𑀭थ𑀗𑀧𑀮 ञच𑀟 𑀱च𑀳च𑀟 ढच𑀣𑀠𑀢𑀟प𑁣𑀟
	ञच𑀟 𑀤च𑀠ढ𑀢च 𑀟𑁦𑀯
	- पचबबच𑀲च𑀣𑀢 𑀠चप𑀳नबन𑀟𑀢𑀟 𑀠नपच𑀟𑁦 𑀟𑁦 च 𑀳च𑀳𑀫𑁦𑀟 च𑀪ल𑀢प 𑀣च𑀞𑁦 णच𑀟𑀞𑀢𑀟 चबच𑀣𑁦𑀤 च च𑀪𑁦𑀱च पच प𑀳च𑀞𑀢णच𑀪
	𑀟𑀢𑀘च𑀪𑀯
	---

	# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2). 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: [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision bf3bf13ab40c3157080a7ab344c831b9ad18b5eb -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 384 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': 384, '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("T-Blue/tsdae_pro_MiniLM_L12_2")
	# Run inference
	sentences = [
	'ब𑀫𑁣𑀳प 𑀢𑀢 𑀳𑀫𑀢𑀟𑁦 𑀠च𑀲𑀢 𑀠च𑀫𑀢𑀠𑀠च𑀟त𑀢𑀦 पच𑀢𑀠च𑀞𑁣𑀟 𑀣च 𑀲च𑀳चलनललन𑀞च णच𑀟च ढच 𑀠च𑀤चन𑀟च त𑀢𑀞𑀢𑀟 𑀫च𑀟𑀞चल𑀢 णचण𑀢𑀟',
	'च𑀠𑀢𑀟पचतत𑀢णच च त𑀢𑀞𑀢𑀟 ब𑀫𑁣𑀳प 𑀳𑁦𑀪𑀢𑁦𑀳 𑀢𑀢 𑀳𑀫𑀢𑀟𑁦 𑀠च𑀲𑀢 𑀠च𑀫𑀢𑀠𑀠च𑀟त𑀢𑀦 पच𑀪𑁦 𑀣च ञ𑀢𑀠ढ𑀢𑀟 𑀢𑀟बच𑀟पचपपन𑀟 प𑀳च𑀪𑀢𑀟 पच𑀢𑀠च𑀞𑁣𑀟 𑀣𑀢𑀪𑁦ढच 𑀣च 𑀲च𑀳चलनललन𑀞च 𑀟च च𑀠𑀢𑀟त𑀢𑀦 णच𑀟च ढच 𑀠च𑀤चन𑀟च त𑀢𑀞𑀢𑀟 𑀞𑀱च𑀟त𑀢णच𑀪 𑀫च𑀟𑀞चल𑀢 णचण𑀢𑀟 पच𑀲𑀢णच𑀪𑀳न𑀯',
	'प𑁣ध𑀳ण ध𑀫𑀢𑀪𑀢 𑀝च𑀟 𑀫च𑀢𑀲𑁦 𑀳𑀫𑀢 च 𑀪च𑀟च𑀪 𑀭𑀭 बच 𑀱चपच𑀟 चबन𑀳पच 𑀭थ𑀗𑀧𑀮 ञच𑀟 𑀱च𑀳च𑀟 ढच𑀣𑀠𑀢𑀟प𑁣𑀟 ञच𑀟 𑀤च𑀠ढ𑀢च 𑀟𑁦𑀯',
	]
	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: 64,000 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: 4 tokens</li><li>mean: 37.72 tokens</li><li>max: 292 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 90.07 tokens</li><li>max: 512 tokens</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \|
	\|:---------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>𑀞न𑀣न ढ𑀢𑀪𑀟𑀢𑀟𑀦𑀞न𑀳च प𑁦𑀞न𑀟</code> \| <code>प𑁦𑀞न𑀟 पचबच णच𑀟च 𑀞न𑀣न 𑀣च ढ𑀢𑀪𑀟𑀢𑀟𑀦𑀞न𑀳च 𑀣च प𑁦𑀞न𑀟 पचत𑀫𑁣बच𑀯</code> \|
	\| <code>च त𑀢ढ𑀢ण𑁣ण𑀢𑀟 𑀳च𑀣च𑀪𑀱च𑀪 𑀳न झच𑀪च 𑀠चप𑀳चण𑀢𑀟</code> \| <code>चढ𑁣𑀞च𑀢𑀞च𑀠च𑀪 च णच𑀱च𑀟त𑀢𑀟 त𑀢ढ𑀢ण𑁣ण𑀢𑀟 𑀳च𑀣च𑀪𑀱च𑀪 𑀘च𑀠च𑀙च𑀦 𑀠च𑀳न च𑀠𑀲च𑀟𑀢 𑀤च 𑀳न 𑀢णच झच𑀪च 𑀠नपच𑀟𑁦 च 𑀠चप𑀳चण𑀢𑀟 चढ𑁣𑀞च𑀟𑀳न𑀯</code> \|
	\| <code>𑀣च बन𑀣न𑀠𑀠च𑀱च 𑀘च𑀪𑀢𑀣न𑀟 𑀠न𑀘चललन पच 𑀯</code> \| <code> पच ढच 𑀣च बन𑀣न𑀠𑀠च𑀱च बच 𑀘च𑀪𑀢𑀣न𑀟 च𑀟च𑀪त𑀫𑀢𑀳प 𑀣चढच𑀟ष𑀣चढच𑀟 𑀣च 𑀠न𑀘चललन 𑀠च𑀳न चलचझच 𑀣च झन𑀟ब𑀢णच𑀪 𑀠च𑀙च𑀢𑀞चपच 𑀙णच𑀟त𑀢 पच 𑀘च𑀠न𑀳 𑀯</code> \|
	* Loss: [<code>DenoisingAutoEncoderLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#denoisingautoencoderloss)

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

	- `per_device_train_batch_size`: 16
	- `per_device_eval_batch_size`: 16
	- `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`: 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`: 3
	- `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 \|
	\|:-----:\|:-----:\|:-------------:\|
	\| 0.125 \| 500 \| 2.5392 \|
	\| 0.25 \| 1000 \| 1.4129 \|
	\| 0.375 \| 1500 \| 1.3383 \|
	\| 0.5 \| 2000 \| 1.288 \|
	\| 0.625 \| 2500 \| 1.2627 \|
	\| 0.75 \| 3000 \| 1.239 \|
	\| 0.875 \| 3500 \| 1.2208 \|
	\| 1.0 \| 4000 \| 1.2041 \|
	\| 1.125 \| 4500 \| 1.1743 \|
	\| 1.25 \| 5000 \| 1.1633 \|
	\| 1.375 \| 5500 \| 1.1526 \|
	\| 1.5 \| 6000 \| 1.1375 \|
	\| 1.625 \| 6500 \| 1.1313 \|
	\| 1.75 \| 7000 \| 1.1246 \|
	\| 1.875 \| 7500 \| 1.1162 \|
	\| 2.0 \| 8000 \| 1.1096 \|
	\| 2.125 \| 8500 \| 1.0876 \|
	\| 2.25 \| 9000 \| 1.0839 \|
	\| 2.375 \| 9500 \| 1.0791 \|
	\| 2.5 \| 10000 \| 1.0697 \|
	\| 2.625 \| 10500 \| 1.0671 \|
	\| 2.75 \| 11000 \| 1.0644 \|
	\| 2.875 \| 11500 \| 1.0579 \|
	\| 3.0 \| 12000 \| 1.0528 \|


	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.0.1
	- Transformers: 4.42.4
	- PyTorch: 2.3.1+cu121
	- Accelerate: 0.33.0
	- Datasets: 2.18.0
	- 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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