sentence-transformers onnx safetensors xlm-roberta sentence-similarity feature-extraction generated_from_trainer dataset_size:6300 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss en arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:BAAI/bge-m3 base_model:quantized:BAAI/bge-m3 license:apache-2.0 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: BAAI/bge-m3
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:6300
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: The consolidated financial statements and accompanying notes listed
    in Part IV, Item 15(a)(1) of this Annual Report on Form 10-K.
  sentences:
  - How much total space does an average The Home Depot store encompass including
    its garden area?
  - What section of the Annual Report on Form 10-K contains the consolidated financial
    statements and accompanying notes?
  - What types of competitive factors does Garmin believe are important in its markets?
- source_sentence: Item 3. Legal Proceedings, which covers litigation and regulatory
    matters, refers to Note 12 – Commitments and Contingencies for more detailed information
    within the Consolidated Financial Statements.
  sentences:
  - What pages contain the Financial Statements and Supplementary Data in IBM’s 2023
    Annual Report to Stockholders?
  - In which note can further details on Legal Proceedings be found within the Consolidated
    Financial Statements?
  - What is the title of Item 8 in the document?
- source_sentence: Net Revenues for the Entertainment segment were $659.3 million
    in 2023.
  sentences:
  - What were the net revenues for the Entertainment segment in 2023?
  - How much net cash was provided by operating activities in 2023?
  - What was the net income reported for the fiscal year ending in August 2023?
- source_sentence: 'The capital allocation program focuses on three objectives: (1)
    grow our business at an average target ROIC-adjusted rate of 20% or greater; (2)
    maintain a strong investment-grade balance sheet, including a target average automotive
    cash balance of $18.0 billion; and (3) after the first two objectives are met,
    return available cash to shareholders.'
  sentences:
  - Why is ICE Mortgage Technology subject to the examination by the Federal Financial
    Institutions Examination Council (FFIEC) and its member agencies?
  - What type of regulations do U.S. automobiles need to comply with under the National
    Highway Traffic Safety Administration?
  - What are the three objectives of the capital allocation program referenced?
- source_sentence: As of January 28, 2024 the net carrying value of our inventories
    was $1.3 billion, which included provisions for obsolete and damaged inventory
    of $139.7 million.
  sentences:
  - What is the status of the company's inventory as of January 28, 2024, in terms
    of its valuation and provisions for obsolescence?
  - What is the relationship between the ESG goals and the long-term growth strategy?
  - What were the financial impacts of Ford's investments in Rivian and Argo in the
    year 2022?
model-index:
- name: BGE-M3 Financial Matryoshka
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 1024
      type: dim_1024
    metrics:
    - type: cosine_accuracy@1
      value: 0.7171428571428572
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8314285714285714
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.87
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9142857142857143
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7171428571428572
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27714285714285714
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.174
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09142857142857141
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7171428571428572
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8314285714285714
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.87
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9142857142857143
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8152097277196483
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7835873015873015
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7867088346410263
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.7128571428571429
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8342857142857143
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8657142857142858
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.91
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7128571428571429
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2780952380952381
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17314285714285713
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09099999999999998
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7128571428571429
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8342857142857143
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8657142857142858
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.91
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8122143155463835
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7808730158730155
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7843065190190194
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.7114285714285714
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8357142857142857
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8642857142857143
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.91
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7114285714285714
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2785714285714286
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17285714285714285
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09099999999999998
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7114285714285714
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8357142857142857
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8642857142857143
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.91
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8109635546819154
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7792959183673466
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.782703758965192
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 384
      type: dim_384
    metrics:
    - type: cosine_accuracy@1
      value: 0.7142857142857143
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8328571428571429
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8628571428571429
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9128571428571428
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7142857142857143
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2776190476190476
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17257142857142854
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09128571428571428
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7142857142857143
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8328571428571429
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8628571428571429
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9128571428571428
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8125530857386527
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7806292517006799
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7837508100457361
      name: Cosine Map@100
---

# BGE-M3 Financial Matryoshka

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3). 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-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision babcf60cae0a1f438d7ade582983d4ba462303c2 -->
- **Maximum Sequence Length:** 8192 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': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (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("haophancs/bge-m3-financial-matryoshka")
# Run inference
sentences = [
    'As of January 28, 2024 the net carrying value of our inventories was $1.3 billion, which included provisions for obsolete and damaged inventory of $139.7 million.',
    "What is the status of the company's inventory as of January 28, 2024, in terms of its valuation and provisions for obsolescence?",
    'What is the relationship between the ESG goals and the long-term growth strategy?',
]
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>
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### Out-of-Scope Use

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## Evaluation

### Metrics

#### Information Retrieval
* Dataset: `dim_1024`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.7171     |
| cosine_accuracy@3   | 0.8314     |
| cosine_accuracy@5   | 0.87       |
| cosine_accuracy@10  | 0.9143     |
| cosine_precision@1  | 0.7171     |
| cosine_precision@3  | 0.2771     |
| cosine_precision@5  | 0.174      |
| cosine_precision@10 | 0.0914     |
| cosine_recall@1     | 0.7171     |
| cosine_recall@3     | 0.8314     |
| cosine_recall@5     | 0.87       |
| cosine_recall@10    | 0.9143     |
| cosine_ndcg@10      | 0.8152     |
| cosine_mrr@10       | 0.7836     |
| **cosine_map@100**  | **0.7867** |

#### 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   | 0.7129     |
| cosine_accuracy@3   | 0.8343     |
| cosine_accuracy@5   | 0.8657     |
| cosine_accuracy@10  | 0.91       |
| cosine_precision@1  | 0.7129     |
| cosine_precision@3  | 0.2781     |
| cosine_precision@5  | 0.1731     |
| cosine_precision@10 | 0.091      |
| cosine_recall@1     | 0.7129     |
| cosine_recall@3     | 0.8343     |
| cosine_recall@5     | 0.8657     |
| cosine_recall@10    | 0.91       |
| cosine_ndcg@10      | 0.8122     |
| cosine_mrr@10       | 0.7809     |
| **cosine_map@100**  | **0.7843** |

#### 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   | 0.7114     |
| cosine_accuracy@3   | 0.8357     |
| cosine_accuracy@5   | 0.8643     |
| cosine_accuracy@10  | 0.91       |
| cosine_precision@1  | 0.7114     |
| cosine_precision@3  | 0.2786     |
| cosine_precision@5  | 0.1729     |
| cosine_precision@10 | 0.091      |
| cosine_recall@1     | 0.7114     |
| cosine_recall@3     | 0.8357     |
| cosine_recall@5     | 0.8643     |
| cosine_recall@10    | 0.91       |
| cosine_ndcg@10      | 0.811      |
| cosine_mrr@10       | 0.7793     |
| **cosine_map@100**  | **0.7827** |

#### Information Retrieval
* Dataset: `dim_384`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.7143     |
| cosine_accuracy@3   | 0.8329     |
| cosine_accuracy@5   | 0.8629     |
| cosine_accuracy@10  | 0.9129     |
| cosine_precision@1  | 0.7143     |
| cosine_precision@3  | 0.2776     |
| cosine_precision@5  | 0.1726     |
| cosine_precision@10 | 0.0913     |
| cosine_recall@1     | 0.7143     |
| cosine_recall@3     | 0.8329     |
| cosine_recall@5     | 0.8629     |
| cosine_recall@10    | 0.9129     |
| cosine_ndcg@10      | 0.8126     |
| cosine_mrr@10       | 0.7806     |
| **cosine_map@100**  | **0.7838** |

<!--
## 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.*
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### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 6,300 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: 51.97 tokens</li><li>max: 1146 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 21.63 tokens</li><li>max: 47 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                     | anchor                                                                                                                             |
  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------|
  | <code>From fiscal year 2022 to 2023, the cost of revenue as a percentage of total net revenue decreased by 3 percent.</code>                                                                 | <code>What was the percentage change in cost of revenue as a percentage of total net revenue from fiscal year 2022 to 2023?</code> |
  | <code> •Operating income increased $321 million, or 2%, to $18.1 billion versus year ago due to the increase in net sales, partially offset by a modest decrease in operating margin.</code> | <code>What factors contributed to the increase in operating income for Procter & Gamble in 2023?</code>                            |
  | <code>market specific brands including 'Aurrera,' 'Lider,' and 'PhonePe.'</code>                                                                                                             | <code>What specific brands does Walmart International market?</code>                                                               |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          1024,
          768,
          512,
          384
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

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

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 4
- `per_device_eval_batch_size`: 2
- `gradient_accumulation_steps`: 2
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `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`: 4
- `per_device_eval_batch_size`: 2
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 2
- `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`: 4
- `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`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `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
<details><summary>Click to expand</summary>

| Epoch      | Step     | Training Loss | dim_1024_cosine_map@100 | dim_384_cosine_map@100 | dim_512_cosine_map@100 | dim_768_cosine_map@100 |
|:----------:|:--------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|
| 0.0127     | 10       | 0.2059        | -                       | -                      | -                      | -                      |
| 0.0254     | 20       | 0.2612        | -                       | -                      | -                      | -                      |
| 0.0381     | 30       | 0.0873        | -                       | -                      | -                      | -                      |
| 0.0508     | 40       | 0.1352        | -                       | -                      | -                      | -                      |
| 0.0635     | 50       | 0.156         | -                       | -                      | -                      | -                      |
| 0.0762     | 60       | 0.0407        | -                       | -                      | -                      | -                      |
| 0.0889     | 70       | 0.09          | -                       | -                      | -                      | -                      |
| 0.1016     | 80       | 0.027         | -                       | -                      | -                      | -                      |
| 0.1143     | 90       | 0.0978        | -                       | -                      | -                      | -                      |
| 0.1270     | 100      | 0.0105        | -                       | -                      | -                      | -                      |
| 0.1397     | 110      | 0.0402        | -                       | -                      | -                      | -                      |
| 0.1524     | 120      | 0.0745        | -                       | -                      | -                      | -                      |
| 0.1651     | 130      | 0.0655        | -                       | -                      | -                      | -                      |
| 0.1778     | 140      | 0.0075        | -                       | -                      | -                      | -                      |
| 0.1905     | 150      | 0.0141        | -                       | -                      | -                      | -                      |
| 0.2032     | 160      | 0.0615        | -                       | -                      | -                      | -                      |
| 0.2159     | 170      | 0.0029        | -                       | -                      | -                      | -                      |
| 0.2286     | 180      | 0.0269        | -                       | -                      | -                      | -                      |
| 0.2413     | 190      | 0.0724        | -                       | -                      | -                      | -                      |
| 0.2540     | 200      | 0.0218        | -                       | -                      | -                      | -                      |
| 0.2667     | 210      | 0.0027        | -                       | -                      | -                      | -                      |
| 0.2794     | 220      | 0.007         | -                       | -                      | -                      | -                      |
| 0.2921     | 230      | 0.0814        | -                       | -                      | -                      | -                      |
| 0.3048     | 240      | 0.0326        | -                       | -                      | -                      | -                      |
| 0.3175     | 250      | 0.0061        | -                       | -                      | -                      | -                      |
| 0.3302     | 260      | 0.0471        | -                       | -                      | -                      | -                      |
| 0.3429     | 270      | 0.0115        | -                       | -                      | -                      | -                      |
| 0.3556     | 280      | 0.0021        | -                       | -                      | -                      | -                      |
| 0.3683     | 290      | 0.0975        | -                       | -                      | -                      | -                      |
| 0.3810     | 300      | 0.0572        | -                       | -                      | -                      | -                      |
| 0.3937     | 310      | 0.0125        | -                       | -                      | -                      | -                      |
| 0.4063     | 320      | 0.04          | -                       | -                      | -                      | -                      |
| 0.4190     | 330      | 0.0023        | -                       | -                      | -                      | -                      |
| 0.4317     | 340      | 0.0121        | -                       | -                      | -                      | -                      |
| 0.4444     | 350      | 0.0116        | -                       | -                      | -                      | -                      |
| 0.4571     | 360      | 0.0059        | -                       | -                      | -                      | -                      |
| 0.4698     | 370      | 0.0217        | -                       | -                      | -                      | -                      |
| 0.4825     | 380      | 0.0294        | -                       | -                      | -                      | -                      |
| 0.4952     | 390      | 0.1102        | -                       | -                      | -                      | -                      |
| 0.5079     | 400      | 0.0103        | -                       | -                      | -                      | -                      |
| 0.5206     | 410      | 0.0023        | -                       | -                      | -                      | -                      |
| 0.5333     | 420      | 0.0157        | -                       | -                      | -                      | -                      |
| 0.5460     | 430      | 0.0805        | -                       | -                      | -                      | -                      |
| 0.5587     | 440      | 0.0168        | -                       | -                      | -                      | -                      |
| 0.5714     | 450      | 0.1279        | -                       | -                      | -                      | -                      |
| 0.5841     | 460      | 0.2012        | -                       | -                      | -                      | -                      |
| 0.5968     | 470      | 0.0436        | -                       | -                      | -                      | -                      |
| 0.6095     | 480      | 0.0204        | -                       | -                      | -                      | -                      |
| 0.6222     | 490      | 0.0097        | -                       | -                      | -                      | -                      |
| 0.6349     | 500      | 0.0013        | -                       | -                      | -                      | -                      |
| 0.6476     | 510      | 0.0042        | -                       | -                      | -                      | -                      |
| 0.6603     | 520      | 0.0034        | -                       | -                      | -                      | -                      |
| 0.6730     | 530      | 0.0226        | -                       | -                      | -                      | -                      |
| 0.6857     | 540      | 0.0267        | -                       | -                      | -                      | -                      |
| 0.6984     | 550      | 0.0007        | -                       | -                      | -                      | -                      |
| 0.7111     | 560      | 0.0766        | -                       | -                      | -                      | -                      |
| 0.7238     | 570      | 0.2174        | -                       | -                      | -                      | -                      |
| 0.7365     | 580      | 0.0089        | -                       | -                      | -                      | -                      |
| 0.7492     | 590      | 0.0794        | -                       | -                      | -                      | -                      |
| 0.7619     | 600      | 0.0031        | -                       | -                      | -                      | -                      |
| 0.7746     | 610      | 0.0499        | -                       | -                      | -                      | -                      |
| 0.7873     | 620      | 0.0105        | -                       | -                      | -                      | -                      |
| 0.8        | 630      | 0.0097        | -                       | -                      | -                      | -                      |
| 0.8127     | 640      | 0.0028        | -                       | -                      | -                      | -                      |
| 0.8254     | 650      | 0.0029        | -                       | -                      | -                      | -                      |
| 0.8381     | 660      | 0.1811        | -                       | -                      | -                      | -                      |
| 0.8508     | 670      | 0.064         | -                       | -                      | -                      | -                      |
| 0.8635     | 680      | 0.0139        | -                       | -                      | -                      | -                      |
| 0.8762     | 690      | 0.055         | -                       | -                      | -                      | -                      |
| 0.8889     | 700      | 0.0013        | -                       | -                      | -                      | -                      |
| 0.9016     | 710      | 0.0402        | -                       | -                      | -                      | -                      |
| 0.9143     | 720      | 0.0824        | -                       | -                      | -                      | -                      |
| 0.9270     | 730      | 0.03          | -                       | -                      | -                      | -                      |
| 0.9397     | 740      | 0.0337        | -                       | -                      | -                      | -                      |
| 0.9524     | 750      | 0.1192        | -                       | -                      | -                      | -                      |
| 0.9651     | 760      | 0.0039        | -                       | -                      | -                      | -                      |
| 0.9778     | 770      | 0.004         | -                       | -                      | -                      | -                      |
| 0.9905     | 780      | 0.1413        | -                       | -                      | -                      | -                      |
| 0.9994     | 787      | -             | 0.7851                  | 0.7794                 | 0.7822                 | 0.7863                 |
| 1.0032     | 790      | 0.019         | -                       | -                      | -                      | -                      |
| 1.0159     | 800      | 0.0587        | -                       | -                      | -                      | -                      |
| 1.0286     | 810      | 0.0186        | -                       | -                      | -                      | -                      |
| 1.0413     | 820      | 0.0018        | -                       | -                      | -                      | -                      |
| 1.0540     | 830      | 0.0631        | -                       | -                      | -                      | -                      |
| 1.0667     | 840      | 0.0127        | -                       | -                      | -                      | -                      |
| 1.0794     | 850      | 0.0037        | -                       | -                      | -                      | -                      |
| 1.0921     | 860      | 0.0029        | -                       | -                      | -                      | -                      |
| 1.1048     | 870      | 0.1437        | -                       | -                      | -                      | -                      |
| 1.1175     | 880      | 0.0015        | -                       | -                      | -                      | -                      |
| 1.1302     | 890      | 0.0024        | -                       | -                      | -                      | -                      |
| 1.1429     | 900      | 0.0133        | -                       | -                      | -                      | -                      |
| 1.1556     | 910      | 0.0245        | -                       | -                      | -                      | -                      |
| 1.1683     | 920      | 0.0017        | -                       | -                      | -                      | -                      |
| 1.1810     | 930      | 0.0007        | -                       | -                      | -                      | -                      |
| 1.1937     | 940      | 0.002         | -                       | -                      | -                      | -                      |
| 1.2063     | 950      | 0.0044        | -                       | -                      | -                      | -                      |
| 1.2190     | 960      | 0.0009        | -                       | -                      | -                      | -                      |
| 1.2317     | 970      | 0.01          | -                       | -                      | -                      | -                      |
| 1.2444     | 980      | 0.0026        | -                       | -                      | -                      | -                      |
| 1.2571     | 990      | 0.0017        | -                       | -                      | -                      | -                      |
| 1.2698     | 1000     | 0.0014        | -                       | -                      | -                      | -                      |
| 1.2825     | 1010     | 0.0009        | -                       | -                      | -                      | -                      |
| 1.2952     | 1020     | 0.0829        | -                       | -                      | -                      | -                      |
| 1.3079     | 1030     | 0.0011        | -                       | -                      | -                      | -                      |
| 1.3206     | 1040     | 0.012         | -                       | -                      | -                      | -                      |
| 1.3333     | 1050     | 0.0019        | -                       | -                      | -                      | -                      |
| 1.3460     | 1060     | 0.0007        | -                       | -                      | -                      | -                      |
| 1.3587     | 1070     | 0.0141        | -                       | -                      | -                      | -                      |
| 1.3714     | 1080     | 0.0003        | -                       | -                      | -                      | -                      |
| 1.3841     | 1090     | 0.001         | -                       | -                      | -                      | -                      |
| 1.3968     | 1100     | 0.0005        | -                       | -                      | -                      | -                      |
| 1.4095     | 1110     | 0.0031        | -                       | -                      | -                      | -                      |
| 1.4222     | 1120     | 0.0004        | -                       | -                      | -                      | -                      |
| 1.4349     | 1130     | 0.0054        | -                       | -                      | -                      | -                      |
| 1.4476     | 1140     | 0.0003        | -                       | -                      | -                      | -                      |
| 1.4603     | 1150     | 0.0007        | -                       | -                      | -                      | -                      |
| 1.4730     | 1160     | 0.0009        | -                       | -                      | -                      | -                      |
| 1.4857     | 1170     | 0.001         | -                       | -                      | -                      | -                      |
| 1.4984     | 1180     | 0.0006        | -                       | -                      | -                      | -                      |
| 1.5111     | 1190     | 0.0046        | -                       | -                      | -                      | -                      |
| 1.5238     | 1200     | 0.0003        | -                       | -                      | -                      | -                      |
| 1.5365     | 1210     | 0.0002        | -                       | -                      | -                      | -                      |
| 1.5492     | 1220     | 0.004         | -                       | -                      | -                      | -                      |
| 1.5619     | 1230     | 0.0017        | -                       | -                      | -                      | -                      |
| 1.5746     | 1240     | 0.0003        | -                       | -                      | -                      | -                      |
| 1.5873     | 1250     | 0.0027        | -                       | -                      | -                      | -                      |
| 1.6        | 1260     | 0.1134        | -                       | -                      | -                      | -                      |
| 1.6127     | 1270     | 0.0007        | -                       | -                      | -                      | -                      |
| 1.6254     | 1280     | 0.0005        | -                       | -                      | -                      | -                      |
| 1.6381     | 1290     | 0.0008        | -                       | -                      | -                      | -                      |
| 1.6508     | 1300     | 0.0001        | -                       | -                      | -                      | -                      |
| 1.6635     | 1310     | 0.0023        | -                       | -                      | -                      | -                      |
| 1.6762     | 1320     | 0.0005        | -                       | -                      | -                      | -                      |
| 1.6889     | 1330     | 0.0004        | -                       | -                      | -                      | -                      |
| 1.7016     | 1340     | 0.0003        | -                       | -                      | -                      | -                      |
| 1.7143     | 1350     | 0.0347        | -                       | -                      | -                      | -                      |
| 1.7270     | 1360     | 0.0339        | -                       | -                      | -                      | -                      |
| 1.7397     | 1370     | 0.0003        | -                       | -                      | -                      | -                      |
| 1.7524     | 1380     | 0.0005        | -                       | -                      | -                      | -                      |
| 1.7651     | 1390     | 0.0002        | -                       | -                      | -                      | -                      |
| 1.7778     | 1400     | 0.0031        | -                       | -                      | -                      | -                      |
| 1.7905     | 1410     | 0.0002        | -                       | -                      | -                      | -                      |
| 1.8032     | 1420     | 0.0012        | -                       | -                      | -                      | -                      |
| 1.8159     | 1430     | 0.0002        | -                       | -                      | -                      | -                      |
| 1.8286     | 1440     | 0.0002        | -                       | -                      | -                      | -                      |
| 1.8413     | 1450     | 0.0004        | -                       | -                      | -                      | -                      |
| 1.8540     | 1460     | 0.011         | -                       | -                      | -                      | -                      |
| 1.8667     | 1470     | 0.0824        | -                       | -                      | -                      | -                      |
| 1.8794     | 1480     | 0.0003        | -                       | -                      | -                      | -                      |
| 1.8921     | 1490     | 0.0004        | -                       | -                      | -                      | -                      |
| 1.9048     | 1500     | 0.0006        | -                       | -                      | -                      | -                      |
| 1.9175     | 1510     | 0.015         | -                       | -                      | -                      | -                      |
| 1.9302     | 1520     | 0.0004        | -                       | -                      | -                      | -                      |
| 1.9429     | 1530     | 0.0004        | -                       | -                      | -                      | -                      |
| 1.9556     | 1540     | 0.0011        | -                       | -                      | -                      | -                      |
| 1.9683     | 1550     | 0.0003        | -                       | -                      | -                      | -                      |
| 1.9810     | 1560     | 0.0006        | -                       | -                      | -                      | -                      |
| 1.9937     | 1570     | 0.0042        | -                       | -                      | -                      | -                      |
| 2.0        | 1575     | -             | 0.7862                  | 0.7855                 | 0.7852                 | 0.7878                 |
| 2.0063     | 1580     | 0.0005        | -                       | -                      | -                      | -                      |
| 2.0190     | 1590     | 0.002         | -                       | -                      | -                      | -                      |
| 2.0317     | 1600     | 0.0013        | -                       | -                      | -                      | -                      |
| 2.0444     | 1610     | 0.0002        | -                       | -                      | -                      | -                      |
| 2.0571     | 1620     | 0.0035        | -                       | -                      | -                      | -                      |
| 2.0698     | 1630     | 0.0004        | -                       | -                      | -                      | -                      |
| 2.0825     | 1640     | 0.0002        | -                       | -                      | -                      | -                      |
| 2.0952     | 1650     | 0.0032        | -                       | -                      | -                      | -                      |
| 2.1079     | 1660     | 0.0916        | -                       | -                      | -                      | -                      |
| 2.1206     | 1670     | 0.0002        | -                       | -                      | -                      | -                      |
| 2.1333     | 1680     | 0.0006        | -                       | -                      | -                      | -                      |
| 2.1460     | 1690     | 0.0002        | -                       | -                      | -                      | -                      |
| 2.1587     | 1700     | 0.0003        | -                       | -                      | -                      | -                      |
| 2.1714     | 1710     | 0.0001        | -                       | -                      | -                      | -                      |
| 2.1841     | 1720     | 0.0001        | -                       | -                      | -                      | -                      |
| 2.1968     | 1730     | 0.0004        | -                       | -                      | -                      | -                      |
| 2.2095     | 1740     | 0.0004        | -                       | -                      | -                      | -                      |
| 2.2222     | 1750     | 0.0001        | -                       | -                      | -                      | -                      |
| 2.2349     | 1760     | 0.0002        | -                       | -                      | -                      | -                      |
| 2.2476     | 1770     | 0.0007        | -                       | -                      | -                      | -                      |
| 2.2603     | 1780     | 0.0001        | -                       | -                      | -                      | -                      |
| 2.2730     | 1790     | 0.0002        | -                       | -                      | -                      | -                      |
| 2.2857     | 1800     | 0.0004        | -                       | -                      | -                      | -                      |
| 2.2984     | 1810     | 0.0711        | -                       | -                      | -                      | -                      |
| 2.3111     | 1820     | 0.0001        | -                       | -                      | -                      | -                      |
| 2.3238     | 1830     | 0.0005        | -                       | -                      | -                      | -                      |
| 2.3365     | 1840     | 0.0004        | -                       | -                      | -                      | -                      |
| 2.3492     | 1850     | 0.0001        | -                       | -                      | -                      | -                      |
| 2.3619     | 1860     | 0.0005        | -                       | -                      | -                      | -                      |
| 2.3746     | 1870     | 0.0003        | -                       | -                      | -                      | -                      |
| 2.3873     | 1880     | 0.0001        | -                       | -                      | -                      | -                      |
| 2.4        | 1890     | 0.0002        | -                       | -                      | -                      | -                      |
| 2.4127     | 1900     | 0.0001        | -                       | -                      | -                      | -                      |
| 2.4254     | 1910     | 0.0002        | -                       | -                      | -                      | -                      |
| 2.4381     | 1920     | 0.0002        | -                       | -                      | -                      | -                      |
| 2.4508     | 1930     | 0.0002        | -                       | -                      | -                      | -                      |
| 2.4635     | 1940     | 0.0004        | -                       | -                      | -                      | -                      |
| 2.4762     | 1950     | 0.0001        | -                       | -                      | -                      | -                      |
| 2.4889     | 1960     | 0.0002        | -                       | -                      | -                      | -                      |
| 2.5016     | 1970     | 0.0002        | -                       | -                      | -                      | -                      |
| 2.5143     | 1980     | 0.0001        | -                       | -                      | -                      | -                      |
| 2.5270     | 1990     | 0.0001        | -                       | -                      | -                      | -                      |
| 2.5397     | 2000     | 0.0002        | -                       | -                      | -                      | -                      |
| 2.5524     | 2010     | 0.0023        | -                       | -                      | -                      | -                      |
| 2.5651     | 2020     | 0.0002        | -                       | -                      | -                      | -                      |
| 2.5778     | 2030     | 0.0001        | -                       | -                      | -                      | -                      |
| 2.5905     | 2040     | 0.0003        | -                       | -                      | -                      | -                      |
| 2.6032     | 2050     | 0.0003        | -                       | -                      | -                      | -                      |
| 2.6159     | 2060     | 0.0002        | -                       | -                      | -                      | -                      |
| 2.6286     | 2070     | 0.0001        | -                       | -                      | -                      | -                      |
| 2.6413     | 2080     | 0.0           | -                       | -                      | -                      | -                      |
| 2.6540     | 2090     | 0.0001        | -                       | -                      | -                      | -                      |
| 2.6667     | 2100     | 0.0001        | -                       | -                      | -                      | -                      |
| 2.6794     | 2110     | 0.0001        | -                       | -                      | -                      | -                      |
| 2.6921     | 2120     | 0.0001        | -                       | -                      | -                      | -                      |
| 2.7048     | 2130     | 0.0001        | -                       | -                      | -                      | -                      |
| 2.7175     | 2140     | 0.0048        | -                       | -                      | -                      | -                      |
| 2.7302     | 2150     | 0.0005        | -                       | -                      | -                      | -                      |
| 2.7429     | 2160     | 0.0001        | -                       | -                      | -                      | -                      |
| 2.7556     | 2170     | 0.0001        | -                       | -                      | -                      | -                      |
| 2.7683     | 2180     | 0.0001        | -                       | -                      | -                      | -                      |
| 2.7810     | 2190     | 0.0001        | -                       | -                      | -                      | -                      |
| 2.7937     | 2200     | 0.0001        | -                       | -                      | -                      | -                      |
| 2.8063     | 2210     | 0.0001        | -                       | -                      | -                      | -                      |
| 2.8190     | 2220     | 0.0001        | -                       | -                      | -                      | -                      |
| 2.8317     | 2230     | 0.0002        | -                       | -                      | -                      | -                      |
| 2.8444     | 2240     | 0.0036        | -                       | -                      | -                      | -                      |
| 2.8571     | 2250     | 0.0001        | -                       | -                      | -                      | -                      |
| 2.8698     | 2260     | 0.0368        | -                       | -                      | -                      | -                      |
| 2.8825     | 2270     | 0.0003        | -                       | -                      | -                      | -                      |
| 2.8952     | 2280     | 0.0002        | -                       | -                      | -                      | -                      |
| 2.9079     | 2290     | 0.0001        | -                       | -                      | -                      | -                      |
| 2.9206     | 2300     | 0.0005        | -                       | -                      | -                      | -                      |
| 2.9333     | 2310     | 0.0001        | -                       | -                      | -                      | -                      |
| 2.9460     | 2320     | 0.0001        | -                       | -                      | -                      | -                      |
| 2.9587     | 2330     | 0.0003        | -                       | -                      | -                      | -                      |
| 2.9714     | 2340     | 0.0001        | -                       | -                      | -                      | -                      |
| 2.9841     | 2350     | 0.0001        | -                       | -                      | -                      | -                      |
| 2.9968     | 2360     | 0.0002        | -                       | -                      | -                      | -                      |
| **2.9994** | **2362** | **-**         | **0.7864**              | **0.7805**             | **0.7838**             | **0.7852**             |
| 3.0095     | 2370     | 0.0025        | -                       | -                      | -                      | -                      |
| 3.0222     | 2380     | 0.0002        | -                       | -                      | -                      | -                      |
| 3.0349     | 2390     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.0476     | 2400     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.0603     | 2410     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.0730     | 2420     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.0857     | 2430     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.0984     | 2440     | 0.0002        | -                       | -                      | -                      | -                      |
| 3.1111     | 2450     | 0.0116        | -                       | -                      | -                      | -                      |
| 3.1238     | 2460     | 0.0002        | -                       | -                      | -                      | -                      |
| 3.1365     | 2470     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.1492     | 2480     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.1619     | 2490     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.1746     | 2500     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.1873     | 2510     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.2        | 2520     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.2127     | 2530     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.2254     | 2540     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.2381     | 2550     | 0.0002        | -                       | -                      | -                      | -                      |
| 3.2508     | 2560     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.2635     | 2570     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.2762     | 2580     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.2889     | 2590     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.3016     | 2600     | 0.063         | -                       | -                      | -                      | -                      |
| 3.3143     | 2610     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.3270     | 2620     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.3397     | 2630     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.3524     | 2640     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.3651     | 2650     | 0.0002        | -                       | -                      | -                      | -                      |
| 3.3778     | 2660     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.3905     | 2670     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.4032     | 2680     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.4159     | 2690     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.4286     | 2700     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.4413     | 2710     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.4540     | 2720     | 0.0002        | -                       | -                      | -                      | -                      |
| 3.4667     | 2730     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.4794     | 2740     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.4921     | 2750     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.5048     | 2760     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.5175     | 2770     | 0.0002        | -                       | -                      | -                      | -                      |
| 3.5302     | 2780     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.5429     | 2790     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.5556     | 2800     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.5683     | 2810     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.5810     | 2820     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.5937     | 2830     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.6063     | 2840     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.6190     | 2850     | 0.0           | -                       | -                      | -                      | -                      |
| 3.6317     | 2860     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.6444     | 2870     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.6571     | 2880     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.6698     | 2890     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.6825     | 2900     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.6952     | 2910     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.7079     | 2920     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.7206     | 2930     | 0.0003        | -                       | -                      | -                      | -                      |
| 3.7333     | 2940     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.7460     | 2950     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.7587     | 2960     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.7714     | 2970     | 0.0002        | -                       | -                      | -                      | -                      |
| 3.7841     | 2980     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.7968     | 2990     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.8095     | 3000     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.8222     | 3010     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.8349     | 3020     | 0.0002        | -                       | -                      | -                      | -                      |
| 3.8476     | 3030     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.8603     | 3040     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.8730     | 3050     | 0.0214        | -                       | -                      | -                      | -                      |
| 3.8857     | 3060     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.8984     | 3070     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.9111     | 3080     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.9238     | 3090     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.9365     | 3100     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.9492     | 3110     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.9619     | 3120     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.9746     | 3130     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.9873     | 3140     | 0.0001        | -                       | -                      | -                      | -                      |
| 3.9975     | 3148     | -             | 0.7867                  | 0.7838                 | 0.7827                 | 0.7843                 |

* The bold row denotes the saved checkpoint.
</details>

### Framework Versions
- Python: 3.12.2
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.2.0+cu121
- Accelerate: 0.31.0
- 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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