Update Hugging Face org references: llm-semantic-router → vllm-sr

f60baa4 verified 27 days ago

17 kB

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:606
- loss:MultipleNegativesRankingLoss
base_model: vllm-sr/mmbert-embed-32k-2d-matryoshka
widget:
- source_sentence: What might be contained in Item 8 of a financial document?
  sentences:
  - 'What amount was authorized for future share repurchases by the company as of
    October 31, 2023?
    Answer: $1.0 billion'
  - 'What information is contained in Item 8 of a financial document?
    Answer: Item 8 contains the Financial Statements and Supplementary Data.'
  - 'What might be contained in Item 8 of a financial document?
    Answer: Financial Statements and Supplementary Data'
- source_sentence: In IBM's 2023 Annual Report, which pages detail the Financial Statements
    and Supplementary Data?
  sentences:
  - 'How are the pages of IBM''s financial statements and supplementary data described
    in terms of location in the 2023 Annual Report?
    Answer: They are incorporated by reference.'
  - 'In IBM''s 2023 Annual Report, which pages detail the Financial Statements and
    Supplementary Data?
    Answer: Pages 44 through 121'
  - 'What does Item 8 of the financial document pertain to?
    Answer: It pertains to Financial Statements and Supplementary Data.'
- source_sentence: How much free cash flow did Delta Air Lines generate in 2023?
  sentences:
  - 'What is the title of Item 8 in the document?
    Answer: Financial Statements and Supplementary Data'
  - 'How does Free Cash Flow differ from net cash provided by operating activities?
    Answer: Free Cash Flow is calculated by subtracting purchases of property and
    equipment from net cash provided by operating activities.'
  - 'How much free cash flow did Delta Air Lines generate in 2023?
    Answer: $2,003 million'
- source_sentence: How much did the AUS for equity increase from 2022 to 2023?
  sentences:
  - 'What caused the effective tax rate for 2022 to be impacted?
    Answer: The effective tax rate for 2022 was impacted by net discrete tax benefits
    of $130, primarily related to excess tax benefits from stock compensation.'
  - 'How did foreign exchange rate fluctuations impact the company''s consolidated
    revenues and income before income taxes for the fiscal years ended May 31, 2023,
    2022, and 2021?
    Answer: Foreign exchange rate fluctuations had a negative impact on the company''s
    consolidated revenues by approximately $2,859 million in 2023, $295 million in
    2022, and positively by $893 million in 2021. Similarly, for income before income
    taxes, the impact was a detriment of approximately $824 million in 2023, $87 million
    in 2022, and a benefit of $260 million in 2021.'
  - 'How much did the AUS for equity increase from 2022 to 2023?
    Answer: $95 billion'
- source_sentence: What is included in Item 8 of the document?
  sentences:
  - 'How has Google Search evolved since its inception?
    Answer: Google Search has evolved from a basic web page search tool into a dynamic,
    multimodal experience, expanding into mobile web search, introducing Universal
    Search, and new search modalities like voice and visual search.'
  - 'What is the content of Item 8 in the document?
    Answer: Item 8 of the document includes Financial Statements and Supplementary
    Data.'
  - 'What is included in Item 8 of the document?
    Answer: Financial Statements and Supplementary Data'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on vllm-sr/mmbert-embed-32k-2d-matryoshka

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [vllm-sr/mmbert-embed-32k-2d-matryoshka](https://huggingface.co/vllm-sr/mmbert-embed-32k-2d-matryoshka). 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:** [vllm-sr/mmbert-embed-32k-2d-matryoshka](https://huggingface.co/vllm-sr/mmbert-embed-32k-2d-matryoshka) <!-- at revision 38ba9e6c90ad4f7631d7402c5bc2c0f953277aae -->
- **Maximum Sequence Length:** 32768 tokens
- **Output Dimensionality:** 768 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/huggingface/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': 32768, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
  (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 = [
    'What is included in Item 8 of the document?',
    'What is included in Item 8 of the document?\n\nAnswer: Financial Statements and Supplementary Data',
    'What is the content of Item 8 in the document?\n\nAnswer: Item 8 of the document includes Financial Statements and Supplementary Data.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0078, 0.9297, 0.5508],
#         [0.9297, 0.9922, 0.6680],
#         [0.5508, 0.6680, 1.0000]], dtype=torch.bfloat16)
```

<!--
### 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: 606 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
* Approximate statistics based on the first 606 samples:
  |         | sentence_0                                                                         | sentence_1                                                                          | sentence_2                                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                              | string                                                                              |
  | details | <ul><li>min: 10 tokens</li><li>mean: 22.46 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 52.15 tokens</li><li>max: 229 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 49.13 tokens</li><li>max: 120 tokens</li></ul> |
* Samples:
  | sentence_0                                                                                                                     | sentence_1                                                                                                                                                                                                                                                                                                                        | sentence_2                                                                                                                                                                                                                                                                |
  |:-------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>What led to the increase in operating income margin for the Dollar Tree segment in 2022?</code>                          | <code>What led to the increase in operating income margin for the Dollar Tree segment in 2022?<br><br>Answer: The increase in operating income margin for the Dollar Tree segment in 2022 was primarily due to the gross profit margin increase and a decrease in the selling, general and administrative expense rate.</code>    | <code>What was the increase in the gross profit margin for the fiscal year 2022 compared to the previous year?<br><br>Answer: The increase in the gross profit margin for the fiscal year 2022 compared to the previous year was 2.1%.</code>                             |
  | <code>How much net cash was provided by operating activities in 2022?</code>                                                   | <code>How much net cash was provided by operating activities in 2022?<br><br>Answer: $4.5 billion</code>                                                                                                                                                                                                                          | <code>What were the main components contributing to the net cash provided by operating activities in 2023?<br><br>Answer: Operating income, depreciation and amortization, and various adjustments in operating assets and liabilities were the main contributors.</code> |
  | <code>How was the stock-based compensation expense of $254 million accounted for in the company's financial statements?</code> | <code>How was the stock-based compensation expense of $254 million accounted for in the company's financial statements?<br><br>Answer: The stock-based compensation expense of $254 million was recorded in the line item selling, general, and administrative expenses in the company's consolidated statement of income.</code> | <code>How much did the cost of revenue increase in stock-based compensation expense from fiscal year 2021 to 2023?<br><br>Answer: $2 million</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",
      "gather_across_devices": false
  }
  ```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `num_train_epochs`: 2
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `per_device_eval_batch_size`: 8
- `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`: 2
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: None
- `warmup_ratio`: None
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `enable_jit_checkpoint`: False
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `use_cpu`: False
- `seed`: 42
- `data_seed`: None
- `bf16`: False
- `fp16`: False
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: -1
- `ddp_backend`: None
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `group_by_length`: False
- `length_column_name`: length
- `project`: huggingface
- `trackio_space_id`: trackio
- `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
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `auto_find_batch_size`: False
- `full_determinism`: False
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_num_input_tokens_seen`: no
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: True
- `use_cache`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
- `router_mapping`: {}
- `learning_rate_mapping`: {}

</details>

### Framework Versions
- Python: 3.12.10
- Sentence Transformers: 5.2.2
- Transformers: 5.0.0
- PyTorch: 2.7.0+cu128
- Accelerate: 1.12.0
- Datasets: 4.5.0
- Tokenizers: 0.22.2

## 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",
}
```

#### 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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## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

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## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- dense
	- generated_from_trainer
	- dataset_size:606
	- loss:MultipleNegativesRankingLoss
	base_model: vllm-sr/mmbert-embed-32k-2d-matryoshka
	widget:
	- source_sentence: What might be contained in Item 8 of a financial document?
	sentences:
	- 'What amount was authorized for future share repurchases by the company as of
	October 31, 2023?


	Answer: $1.0 billion'
	- 'What information is contained in Item 8 of a financial document?


	Answer: Item 8 contains the Financial Statements and Supplementary Data.'
	- 'What might be contained in Item 8 of a financial document?


	Answer: Financial Statements and Supplementary Data'
	- source_sentence: In IBM's 2023 Annual Report, which pages detail the Financial Statements
	and Supplementary Data?
	sentences:
	- 'How are the pages of IBM''s financial statements and supplementary data described
	in terms of location in the 2023 Annual Report?


	Answer: They are incorporated by reference.'
	- 'In IBM''s 2023 Annual Report, which pages detail the Financial Statements and
	Supplementary Data?


	Answer: Pages 44 through 121'
	- 'What does Item 8 of the financial document pertain to?


	Answer: It pertains to Financial Statements and Supplementary Data.'
	- source_sentence: How much free cash flow did Delta Air Lines generate in 2023?
	sentences:
	- 'What is the title of Item 8 in the document?


	Answer: Financial Statements and Supplementary Data'
	- 'How does Free Cash Flow differ from net cash provided by operating activities?


	Answer: Free Cash Flow is calculated by subtracting purchases of property and
	equipment from net cash provided by operating activities.'
	- 'How much free cash flow did Delta Air Lines generate in 2023?


	Answer: $2,003 million'
	- source_sentence: How much did the AUS for equity increase from 2022 to 2023?
	sentences:
	- 'What caused the effective tax rate for 2022 to be impacted?


	Answer: The effective tax rate for 2022 was impacted by net discrete tax benefits
	of $130, primarily related to excess tax benefits from stock compensation.'
	- 'How did foreign exchange rate fluctuations impact the company''s consolidated
	revenues and income before income taxes for the fiscal years ended May 31, 2023,
	2022, and 2021?


	Answer: Foreign exchange rate fluctuations had a negative impact on the company''s
	consolidated revenues by approximately $2,859 million in 2023, $295 million in
	2022, and positively by $893 million in 2021. Similarly, for income before income
	taxes, the impact was a detriment of approximately $824 million in 2023, $87 million
	in 2022, and a benefit of $260 million in 2021.'
	- 'How much did the AUS for equity increase from 2022 to 2023?


	Answer: $95 billion'
	- source_sentence: What is included in Item 8 of the document?
	sentences:
	- 'How has Google Search evolved since its inception?


	Answer: Google Search has evolved from a basic web page search tool into a dynamic,
	multimodal experience, expanding into mobile web search, introducing Universal
	Search, and new search modalities like voice and visual search.'
	- 'What is the content of Item 8 in the document?


	Answer: Item 8 of the document includes Financial Statements and Supplementary
	Data.'
	- 'What is included in Item 8 of the document?


	Answer: Financial Statements and Supplementary Data'
	pipeline_tag: sentence-similarity
	library_name: sentence-transformers
	---

	# SentenceTransformer based on vllm-sr/mmbert-embed-32k-2d-matryoshka

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [vllm-sr/mmbert-embed-32k-2d-matryoshka](https://huggingface.co/vllm-sr/mmbert-embed-32k-2d-matryoshka). 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: [vllm-sr/mmbert-embed-32k-2d-matryoshka](https://huggingface.co/vllm-sr/mmbert-embed-32k-2d-matryoshka) <!-- at revision 38ba9e6c90ad4f7631d7402c5bc2c0f953277aae -->
	- Maximum Sequence Length: 32768 tokens
	- Output Dimensionality: 768 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/huggingface/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': 32768, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
	(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 = [
	'What is included in Item 8 of the document?',
	'What is included in Item 8 of the document?\n\nAnswer: Financial Statements and Supplementary Data',
	'What is the content of Item 8 in the document?\n\nAnswer: Item 8 of the document includes Financial Statements and Supplementary Data.',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 768]

	# Get the similarity scores for the embeddings
	similarities = model.similarity(embeddings, embeddings)
	print(similarities)
	# tensor([[1.0078, 0.9297, 0.5508],
	# [0.9297, 0.9922, 0.6680],
	# [0.5508, 0.6680, 1.0000]], dtype=torch.bfloat16)
	```

	<!--
	### 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: 606 training samples
	* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
	* Approximate statistics based on the first 606 samples:
	\| \| sentence_0 \| sentence_1 \| sentence_2 \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| details \| <ul><li>min: 10 tokens</li><li>mean: 22.46 tokens</li><li>max: 55 tokens</li></ul> \| <ul><li>min: 18 tokens</li><li>mean: 52.15 tokens</li><li>max: 229 tokens</li></ul> \| <ul><li>min: 18 tokens</li><li>mean: 49.13 tokens</li><li>max: 120 tokens</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \| sentence_2 \|
	\|:-------------------------------------------------------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>What led to the increase in operating income margin for the Dollar Tree segment in 2022?</code> \| <code>What led to the increase in operating income margin for the Dollar Tree segment in 2022?<br><br>Answer: The increase in operating income margin for the Dollar Tree segment in 2022 was primarily due to the gross profit margin increase and a decrease in the selling, general and administrative expense rate.</code> \| <code>What was the increase in the gross profit margin for the fiscal year 2022 compared to the previous year?<br><br>Answer: The increase in the gross profit margin for the fiscal year 2022 compared to the previous year was 2.1%.</code> \|
	\| <code>How much net cash was provided by operating activities in 2022?</code> \| <code>How much net cash was provided by operating activities in 2022?<br><br>Answer: $4.5 billion</code> \| <code>What were the main components contributing to the net cash provided by operating activities in 2023?<br><br>Answer: Operating income, depreciation and amortization, and various adjustments in operating assets and liabilities were the main contributors.</code> \|
	\| <code>How was the stock-based compensation expense of $254 million accounted for in the company's financial statements?</code> \| <code>How was the stock-based compensation expense of $254 million accounted for in the company's financial statements?<br><br>Answer: The stock-based compensation expense of $254 million was recorded in the line item selling, general, and administrative expenses in the company's consolidated statement of income.</code> \| <code>How much did the cost of revenue increase in stock-based compensation expense from fiscal year 2021 to 2023?<br><br>Answer: $2 million</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",
	"gather_across_devices": false
	}
	```

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

	- `num_train_epochs`: 2
	- `multi_dataset_batch_sampler`: round_robin

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

	- `do_predict`: False
	- `eval_strategy`: no
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 8
	- `per_device_eval_batch_size`: 8
	- `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`: 2
	- `max_steps`: -1
	- `lr_scheduler_type`: linear
	- `lr_scheduler_kwargs`: None
	- `warmup_ratio`: None
	- `warmup_steps`: 0
	- `log_level`: passive
	- `log_level_replica`: warning
	- `log_on_each_node`: True
	- `logging_nan_inf_filter`: True
	- `enable_jit_checkpoint`: False
	- `save_on_each_node`: False
	- `save_only_model`: False
	- `restore_callback_states_from_checkpoint`: False
	- `use_cpu`: False
	- `seed`: 42
	- `data_seed`: None
	- `bf16`: False
	- `fp16`: False
	- `bf16_full_eval`: False
	- `fp16_full_eval`: False
	- `tf32`: None
	- `local_rank`: -1
	- `ddp_backend`: None
	- `debug`: []
	- `dataloader_drop_last`: False
	- `dataloader_num_workers`: 0
	- `dataloader_prefetch_factor`: None
	- `disable_tqdm`: False
	- `remove_unused_columns`: True
	- `label_names`: None
	- `load_best_model_at_end`: False
	- `ignore_data_skip`: False
	- `fsdp`: []
	- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
	- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
	- `parallelism_config`: None
	- `deepspeed`: None
	- `label_smoothing_factor`: 0.0
	- `optim`: adamw_torch
	- `optim_args`: None
	- `group_by_length`: False
	- `length_column_name`: length
	- `project`: huggingface
	- `trackio_space_id`: trackio
	- `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
	- `push_to_hub`: False
	- `resume_from_checkpoint`: None
	- `hub_model_id`: None
	- `hub_strategy`: every_save
	- `hub_private_repo`: None
	- `hub_always_push`: False
	- `hub_revision`: None
	- `gradient_checkpointing`: False
	- `gradient_checkpointing_kwargs`: None
	- `include_for_metrics`: []
	- `eval_do_concat_batches`: True
	- `auto_find_batch_size`: False
	- `full_determinism`: False
	- `ddp_timeout`: 1800
	- `torch_compile`: False
	- `torch_compile_backend`: None
	- `torch_compile_mode`: None
	- `include_num_input_tokens_seen`: no
	- `neftune_noise_alpha`: None
	- `optim_target_modules`: None
	- `batch_eval_metrics`: False
	- `eval_on_start`: False
	- `use_liger_kernel`: False
	- `liger_kernel_config`: None
	- `eval_use_gather_object`: False
	- `average_tokens_across_devices`: True
	- `use_cache`: False
	- `prompts`: None
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: round_robin
	- `router_mapping`: {}
	- `learning_rate_mapping`: {}

	</details>

	### Framework Versions
	- Python: 3.12.10
	- Sentence Transformers: 5.2.2
	- Transformers: 5.0.0
	- PyTorch: 2.7.0+cu128
	- Accelerate: 1.12.0
	- Datasets: 4.5.0
	- Tokenizers: 0.22.2

	## 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",
	}
	```

	#### 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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