bge-large-Chatbot-matryoshka / README.md

Add new SentenceTransformer model.

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	---
	base_model: BAAI/bge-large-en-v1.5
	datasets: []
	language:
	- en
	library_name: sentence-transformers
	license: apache-2.0
	metrics:
	- cosine_accuracy@1
	- cosine_accuracy@3
	- cosine_accuracy@5
	- cosine_accuracy@10
	- cosine_precision@1
	- cosine_precision@3
	- cosine_precision@5
	- cosine_precision@10
	- cosine_recall@1
	- cosine_recall@3
	- cosine_recall@5
	- cosine_recall@10
	- cosine_ndcg@10
	- cosine_mrr@10
	- cosine_map@100
	pipeline_tag: sentence-similarity
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:530
	- loss:MatryoshkaLoss
	- loss:MultipleNegativesRankingLoss
	widget:
	- source_sentence: If you receive a BharatPe speaker that you didn't order, please
	contact BharatPe support immediately. They will assist in resolving the issue
	and advise on the next steps.
	sentences:
	- Can I control multiple BharatPe speakers from one app?
	- What to do if the BharatPe speaker's transaction announcements are intermittently
	silent?
	- What should I do if I receive a BharatPe speaker without ordering it?
	- source_sentence: Remote control capabilities depend on the model of the BharatPe
	speaker. Check if your model supports remote control through the BharatPe app
	or a connected device.
	sentences:
	- How do I update my personal details in my Bharatpe account?
	- What are the benefits of the BharatPe speaker?
	- Can I control the BharatPe speaker remotely?
	- source_sentence: If the announcements are not clear, check the speaker's volume
	settings and ensure it's not placed near noisy equipment. If clarity doesn't improve,
	the speaker may need servicing.
	sentences:
	- What to do if my BharatPe speaker is not syncing with the transaction history
	in the app?
	- What should I do if the speaker is not announcing payments clearly?
	- The speaker doesn't produce any sound, what can be done?
	- source_sentence: If the speaker is causing interference, try relocating it or other
	devices to reduce the interference. Ensure there's a reasonable distance between
	the speaker and other wireless equipment.
	sentences:
	- Can I use my Bharatpe device for international transactions?
	- How do I know if my BharatPe speaker is under warranty?
	- What should I do if the BharatPe speaker is causing interference with other wireless
	devices?
	- source_sentence: I can understand and respond in multiple Indian regional languages.
	Feel free to communicate with me in the language you're most comfortable with.
	sentences:
	- How can I check if the BharatPe speaker is receiving a network signal?
	- Bharti, can you provide tips for effective online communication?
	- Bharti, what languages can you understand and respond to?
	model-index:
	- name: BGE large Chatbot Matryoshka
	results:
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: dim 768
	type: dim_768
	metrics:
	- type: cosine_accuracy@1
	value: 0.8837209302325582
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.9534883720930233
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.9534883720930233
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.9534883720930233
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.8837209302325582
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.3178294573643411
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.19069767441860463
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.09534883720930232
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.8837209302325582
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.9534883720930233
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.9534883720930233
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.9534883720930233
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.9246944071428586
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.9147286821705425
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.9186317558410582
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: dim 512
	type: dim_512
	metrics:
	- type: cosine_accuracy@1
	value: 0.8837209302325582
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.9534883720930233
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.9534883720930233
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.9534883720930233
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.8837209302325582
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.3178294573643411
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.19069767441860463
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.09534883720930232
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.8837209302325582
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.9534883720930233
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.9534883720930233
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.9534883720930233
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.9246944071428586
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.9147286821705425
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.9186317558410582
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: dim 256
	type: dim_256
	metrics:
	- type: cosine_accuracy@1
	value: 0.8837209302325582
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.9302325581395349
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.9534883720930233
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.9534883720930233
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.8837209302325582
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.31007751937984496
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.19069767441860463
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.09534883720930232
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.8837209302325582
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.9302325581395349
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.9534883720930233
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.9534883720930233
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.9220630770785455
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.9116279069767442
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.9147848047984846
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: dim 128
	type: dim_128
	metrics:
	- type: cosine_accuracy@1
	value: 0.9069767441860465
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.9302325581395349
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.9302325581395349
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.9534883720930233
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.9069767441860465
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.31007751937984496
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.18604651162790697
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.09534883720930232
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.9069767441860465
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.9302325581395349
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.9302325581395349
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.9534883720930233
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.9299334172251043
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.9224806201550388
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.92549351912877
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: dim 64
	type: dim_64
	metrics:
	- type: cosine_accuracy@1
	value: 0.8604651162790697
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.9534883720930233
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.9767441860465116
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.9767441860465116
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.8604651162790697
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.3178294573643411
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.1953488372093023
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.09767441860465115
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.8604651162790697
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.9534883720930233
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.9767441860465116
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.9767441860465116
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.9261271120648318
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.9089147286821706
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.9089147286821704
	name: Cosine Map@100
	---

	# BGE large Chatbot Matryoshka

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5). 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: [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) <!-- at revision d4aa6901d3a41ba39fb536a557fa166f842b0e09 -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 1024 tokens
	- Similarity Function: Cosine Similarity
	<!-- - Training Dataset: Unknown -->
	- Language: en
	- License: apache-2.0

	### 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': True}) 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})
	(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("MANMEET75/bge-large-Chatbot-matryoshka")
	# Run inference
	sentences = [
	"I can understand and respond in multiple Indian regional languages. Feel free to communicate with me in the language you're most comfortable with.",
	'Bharti, what languages can you understand and respond to?',
	'Bharti, can you provide tips for effective online communication?',
	]
	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

	#### Information Retrieval
	* Dataset: `dim_768`
	* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| cosine_accuracy@1 \| 0.8837 \|
	\| cosine_accuracy@3 \| 0.9535 \|
	\| cosine_accuracy@5 \| 0.9535 \|
	\| cosine_accuracy@10 \| 0.9535 \|
	\| cosine_precision@1 \| 0.8837 \|
	\| cosine_precision@3 \| 0.3178 \|
	\| cosine_precision@5 \| 0.1907 \|
	\| cosine_precision@10 \| 0.0953 \|
	\| cosine_recall@1 \| 0.8837 \|
	\| cosine_recall@3 \| 0.9535 \|
	\| cosine_recall@5 \| 0.9535 \|
	\| cosine_recall@10 \| 0.9535 \|
	\| cosine_ndcg@10 \| 0.9247 \|
	\| cosine_mrr@10 \| 0.9147 \|
	\| cosine_map@100 \| 0.9186 \|

	#### Information Retrieval
	* Dataset: `dim_512`
	* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| cosine_accuracy@1 \| 0.8837 \|
	\| cosine_accuracy@3 \| 0.9535 \|
	\| cosine_accuracy@5 \| 0.9535 \|
	\| cosine_accuracy@10 \| 0.9535 \|
	\| cosine_precision@1 \| 0.8837 \|
	\| cosine_precision@3 \| 0.3178 \|
	\| cosine_precision@5 \| 0.1907 \|
	\| cosine_precision@10 \| 0.0953 \|
	\| cosine_recall@1 \| 0.8837 \|
	\| cosine_recall@3 \| 0.9535 \|
	\| cosine_recall@5 \| 0.9535 \|
	\| cosine_recall@10 \| 0.9535 \|
	\| cosine_ndcg@10 \| 0.9247 \|
	\| cosine_mrr@10 \| 0.9147 \|
	\| cosine_map@100 \| 0.9186 \|

	#### Information Retrieval
	* Dataset: `dim_256`
	* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| cosine_accuracy@1 \| 0.8837 \|
	\| cosine_accuracy@3 \| 0.9302 \|
	\| cosine_accuracy@5 \| 0.9535 \|
	\| cosine_accuracy@10 \| 0.9535 \|
	\| cosine_precision@1 \| 0.8837 \|
	\| cosine_precision@3 \| 0.3101 \|
	\| cosine_precision@5 \| 0.1907 \|
	\| cosine_precision@10 \| 0.0953 \|
	\| cosine_recall@1 \| 0.8837 \|
	\| cosine_recall@3 \| 0.9302 \|
	\| cosine_recall@5 \| 0.9535 \|
	\| cosine_recall@10 \| 0.9535 \|
	\| cosine_ndcg@10 \| 0.9221 \|
	\| cosine_mrr@10 \| 0.9116 \|
	\| cosine_map@100 \| 0.9148 \|

	#### Information Retrieval
	* Dataset: `dim_128`
	* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| cosine_accuracy@1 \| 0.907 \|
	\| cosine_accuracy@3 \| 0.9302 \|
	\| cosine_accuracy@5 \| 0.9302 \|
	\| cosine_accuracy@10 \| 0.9535 \|
	\| cosine_precision@1 \| 0.907 \|
	\| cosine_precision@3 \| 0.3101 \|
	\| cosine_precision@5 \| 0.186 \|
	\| cosine_precision@10 \| 0.0953 \|
	\| cosine_recall@1 \| 0.907 \|
	\| cosine_recall@3 \| 0.9302 \|
	\| cosine_recall@5 \| 0.9302 \|
	\| cosine_recall@10 \| 0.9535 \|
	\| cosine_ndcg@10 \| 0.9299 \|
	\| cosine_mrr@10 \| 0.9225 \|
	\| cosine_map@100 \| 0.9255 \|

	#### Information Retrieval
	* Dataset: `dim_64`
	* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| cosine_accuracy@1 \| 0.8605 \|
	\| cosine_accuracy@3 \| 0.9535 \|
	\| cosine_accuracy@5 \| 0.9767 \|
	\| cosine_accuracy@10 \| 0.9767 \|
	\| cosine_precision@1 \| 0.8605 \|
	\| cosine_precision@3 \| 0.3178 \|
	\| cosine_precision@5 \| 0.1953 \|
	\| cosine_precision@10 \| 0.0977 \|
	\| cosine_recall@1 \| 0.8605 \|
	\| cosine_recall@3 \| 0.9535 \|
	\| cosine_recall@5 \| 0.9767 \|
	\| cosine_recall@10 \| 0.9767 \|
	\| cosine_ndcg@10 \| 0.9261 \|
	\| cosine_mrr@10 \| 0.9089 \|
	\| cosine_map@100 \| 0.9089 \|

	<!--
	## 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: 530 training samples
	* Columns: <code>positive</code> and <code>anchor</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| positive \| anchor \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 11 tokens</li><li>mean: 35.33 tokens</li><li>max: 99 tokens</li></ul> \| <ul><li>min: 7 tokens</li><li>mean: 17.3 tokens</li><li>max: 29 tokens</li></ul> \|
	* Samples:
	\| positive \| anchor \|
	\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------\|
	\| <code>BharatPe Speaker comes with the following benefits: - Helps you avoid payment fraud - Lightweight & Easy installation process - Compatible with SIM & GPRS connectivity - Comes with a battery, no hassle of constant charging - Available in 10 Languages - Cashback Offers - Free replacement To Know more and place an order, tap below http://bharatpe.in/speaker.</code> \| <code>What are the benefits of the BharatPe speaker?</code> \|
	\| <code>BharatPe Speaker comes with the following benefits: - Helps you avoid payment fraud - Lightweight & Easy installation process - Compatible with SIM & GPRS connectivity - Comes with a battery, no hassle of constant charging - Available in 10 Languages - Cashback Offers - Free replacement To Know more and place an order, tap below http://bharatpe.in/speaker.</code> \| <code>What advantages does the BharatPe speaker offer?</code> \|
	\| <code>BharatPe Speaker comes with the following benefits: - Helps you avoid payment fraud - Lightweight & Easy installation process - Compatible with SIM & GPRS connectivity - Comes with a battery, no hassle of constant charging - Available in 10 Languages - Cashback Offers - Free replacement To Know more and place an order, tap below http://bharatpe.in/speaker.</code> \| <code>Can you outline the benefits of using the BharatPe speaker?</code> \|
	* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
	```json
	{
	"loss": "MultipleNegativesRankingLoss",
	"matryoshka_dims": [
	768,
	512,
	256,
	128,
	64
	],
	"matryoshka_weights": [
	1,
	1,
	1,
	1,
	1
	],
	"n_dims_per_step": -1
	}
	```

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

	- `eval_strategy`: epoch
	- `per_device_train_batch_size`: 32
	- `per_device_eval_batch_size`: 16
	- `gradient_accumulation_steps`: 16
	- `learning_rate`: 2e-05
	- `num_train_epochs`: 10
	- `lr_scheduler_type`: cosine
	- `warmup_ratio`: 0.1
	- `tf32`: False
	- `load_best_model_at_end`: True
	- `optim`: adamw_torch_fused
	- `batch_sampler`: no_duplicates

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

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: epoch
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 32
	- `per_device_eval_batch_size`: 16
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 16
	- `eval_accumulation_steps`: None
	- `learning_rate`: 2e-05
	- `weight_decay`: 0.0
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `max_grad_norm`: 1.0
	- `num_train_epochs`: 10
	- `max_steps`: -1
	- `lr_scheduler_type`: cosine
	- `lr_scheduler_kwargs`: {}
	- `warmup_ratio`: 0.1
	- `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`: False
	- `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`: True
	- `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_fused
	- `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`: no_duplicates
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| dim_128_cosine_map@100 \| dim_256_cosine_map@100 \| dim_512_cosine_map@100 \| dim_64_cosine_map@100 \| dim_768_cosine_map@100 \|
	\|:----------:\|:-----:\|:-------------:\|:----------------------:\|:----------------------:\|:----------------------:\|:---------------------:\|:----------------------:\|
	\| 0.9412 \| 1 \| - \| 0.7980 \| 0.8251 \| 0.8141 \| 0.7124 \| 0.8260 \|
	\| 1.8824 \| 2 \| - \| 0.8624 \| 0.8619 \| 0.8691 \| 0.7637 \| 0.8557 \|
	\| 2.8235 \| 3 \| - \| 0.8763 \| 0.8792 \| 0.8770 \| 0.8588 \| 0.8832 \|
	\| 3.7647 \| 4 \| - \| 0.9007 \| 0.9014 \| 0.9115 \| 0.8820 \| 0.9130 \|
	\| 4.7059 \| 5 \| - \| 0.9014 \| 0.9146 \| 0.9186 \| 0.9053 \| 0.9185 \|
	\| 5.6471 \| 6 \| - \| 0.9134 \| 0.9146 \| 0.9186 \| 0.9205 \| 0.9183 \|
	\| 6.5882 \| 7 \| - \| 0.9255 \| 0.9146 \| 0.9186 \| 0.9089 \| 0.9185 \|
	\| 7.5294 \| 8 \| - \| 0.9255 \| 0.9147 \| 0.9186 \| 0.9089 \| 0.9185 \|
	\| 8.4706 \| 9 \| - \| 0.9255 \| 0.9147 \| 0.9186 \| 0.9089 \| 0.9186 \|
	\| 9.4118 \| 10 \| 2.0337 \| 0.9255 \| 0.9148 \| 0.9186 \| 0.9089 \| 0.9186 \|

	* The bold row denotes the saved checkpoint.

	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.0.1
	- Transformers: 4.41.2
	- PyTorch: 2.1.2+cu121
	- Accelerate: 0.32.1
	- 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",
	}
	```

	#### MatryoshkaLoss
	```bibtex
	@misc{kusupati2024matryoshka,
	title={Matryoshka Representation Learning},
	author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
	year={2024},
	eprint={2205.13147},
	archivePrefix={arXiv},
	primaryClass={cs.LG}
	}
	```

	#### 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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