---
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)
- **Maximum Sequence Length:** 32768 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
### 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)
```
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 606 training samples
* Columns: sentence_0, sentence_1, and sentence_2
* Approximate statistics based on the first 606 samples:
| | sentence_0 | sentence_1 | sentence_2 |
|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string | string |
| details | - min: 10 tokens
- mean: 22.46 tokens
- max: 55 tokens
| - min: 18 tokens
- mean: 52.15 tokens
- max: 229 tokens
| - min: 18 tokens
- mean: 49.13 tokens
- max: 120 tokens
|
* Samples:
| sentence_0 | sentence_1 | sentence_2 |
|:-------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| What led to the increase in operating income margin for the Dollar Tree segment in 2022? | What led to the increase in operating income margin for the Dollar Tree segment in 2022?
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. | What was the increase in the gross profit margin for the fiscal year 2022 compared to the previous year?
Answer: The increase in the gross profit margin for the fiscal year 2022 compared to the previous year was 2.1%. |
| How much net cash was provided by operating activities in 2022? | How much net cash was provided by operating activities in 2022?
Answer: $4.5 billion | What were the main components contributing to the net cash provided by operating activities in 2023?
Answer: Operating income, depreciation and amortization, and various adjustments in operating assets and liabilities were the main contributors. |
| How was the stock-based compensation expense of $254 million accounted for in the company's financial statements? | How was the stock-based compensation expense of $254 million accounted for in the company's financial statements?
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. | How much did the cost of revenue increase in stock-based compensation expense from fiscal year 2021 to 2023?
Answer: $2 million |
* Loss: [MultipleNegativesRankingLoss](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
Click to expand
- `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`: {}