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ModernBERT-base-nli-v3

Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
feature-extraction
Generated from Trainer
dataset_size:557850
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results
Inference Endpoints
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ModernBERT-base-nli-v3 / README.md
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---
language:
- en
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:557850
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: estrogen/ModernBERT-base-sbert-initialized
widget:
- source_sentence: A construction worker is standing on a crane placing a large arm
on top of a stature in progress.
sentences:
- A man is playing with his camera.
- A person standing
- Nobody is standing
- source_sentence: A boy in red slides down an inflatable ride.
sentences:
- a baby smiling
- A boy is playing on an inflatable ride.
- A boy pierces a knife through an inflatable ride.
- source_sentence: A man in a black shirt is playing a guitar.
sentences:
- A group of women are selling their wares
- The man is wearing black.
- The man is wearing a blue shirt.
- source_sentence: A man with a large power drill standing next to his daughter with
a vacuum cleaner hose.
sentences:
- A man holding a drill stands next to a girl holding a vacuum hose.
- Kids ride an amusement ride.
- The man and girl are painting the walls.
- source_sentence: A middle-aged man works under the engine of a train on rail tracks.
sentences:
- A guy is working on a train.
- Two young asian men are squatting.
- A guy is driving to work.
datasets:
- sentence-transformers/all-nli
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on estrogen/ModernBERT-base-sbert-initialized
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev
type: sts-dev
metrics:
- type: pearson_cosine
value: 0.8601586939371598
name: Pearson Cosine
- type: spearman_cosine
value: 0.8650559283517015
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test
type: sts-test
metrics:
- type: pearson_cosine
value: 0.8483904083763342
name: Pearson Cosine
- type: spearman_cosine
value: 0.8504558364206114
name: Spearman Cosine
---
# SentenceTransformer based on estrogen/ModernBERT-base-sbert-initialized
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [estrogen/ModernBERT-base-sbert-initialized](https://huggingface.co/estrogen/ModernBERT-base-sbert-initialized) on the [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. 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:** [estrogen/ModernBERT-base-sbert-initialized](https://huggingface.co/estrogen/ModernBERT-base-sbert-initialized) <!-- at revision d80f2f10df59065d673fa4d9ef890aae3cbf4b68 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
- **Language:** en
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: 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("estrogen/ModernBERT-base-nli-v3")
# Run inference
sentences = [
'A middle-aged man works under the engine of a train on rail tracks.',
'A guy is working on a train.',
'A guy is driving to work.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Semantic Similarity
* Datasets: `sts-dev` and `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | sts-dev | sts-test |
|:--------------------|:-----------|:-----------|
| pearson_cosine | 0.8602 | 0.8484 |
| **spearman_cosine** | **0.8651** | **0.8505** |
<!--
## 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
#### all-nli
* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
* Size: 557,850 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 7 tokens</li><li>mean: 10.46 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.91 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.49 tokens</li><li>max: 51 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------|
| <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code> | <code>A person is at a diner, ordering an omelette.</code> |
| <code>Children smiling and waving at camera</code> | <code>There are children present</code> | <code>The kids are frowning</code> |
| <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Evaluation Dataset
#### all-nli
* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
* Size: 6,584 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 18.25 tokens</li><li>max: 69 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 9.88 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.48 tokens</li><li>max: 29 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------|
| <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code> | <code>The men are fighting outside a deli.</code> |
| <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>Two kids in numbered jerseys wash their hands.</code> | <code>Two kids in jackets walk to school.</code> |
| <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> | <code>A man selling donuts to a customer.</code> | <code>A woman drinks her coffee in a small cafe.</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 1024
- `per_device_eval_batch_size`: 1024
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `bf16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 1024
- `per_device_eval_batch_size`: 1024
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `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`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `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`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | Validation Loss | sts-dev_spearman_cosine | sts-test_spearman_cosine |
|:------:|:----:|:-------------:|:---------------:|:-----------------------:|:------------------------:|
| 0 | 0 | - | - | 0.5576 | - |
| 0.0018 | 1 | 36.2556 | - | - | - |
| 0.0037 | 2 | 36.6329 | - | - | - |
| 0.0055 | 3 | 36.9705 | - | - | - |
| 0.0073 | 4 | 36.9173 | - | - | - |
| 0.0092 | 5 | 36.8254 | - | - | - |
| 0.0110 | 6 | 36.7313 | - | - | - |
| 0.0128 | 7 | 36.5865 | - | - | - |
| 0.0147 | 8 | 36.1709 | - | - | - |
| 0.0165 | 9 | 36.0519 | - | - | - |
| 0.0183 | 10 | 35.712 | - | - | - |
| 0.0202 | 11 | 35.4072 | - | - | - |
| 0.0220 | 12 | 35.0623 | - | - | - |
| 0.0239 | 13 | 34.6996 | - | - | - |
| 0.0257 | 14 | 34.2426 | - | - | - |
| 0.0275 | 15 | 33.6913 | - | - | - |
| 0.0294 | 16 | 33.2808 | - | - | - |
| 0.0312 | 17 | 32.5487 | - | - | - |
| 0.0330 | 18 | 31.6451 | - | - | - |
| 0.0349 | 19 | 30.7017 | - | - | - |
| 0.0367 | 20 | 29.8238 | - | - | - |
| 0.0385 | 21 | 28.7414 | - | - | - |
| 0.0404 | 22 | 27.316 | - | - | - |
| 0.0422 | 23 | 26.1119 | - | - | - |
| 0.0440 | 24 | 24.7211 | - | - | - |
| 0.0459 | 25 | 24.0007 | - | - | - |
| 0.0477 | 26 | 22.706 | - | - | - |
| 0.0495 | 27 | 21.7943 | - | - | - |
| 0.0514 | 28 | 21.5753 | - | - | - |
| 0.0532 | 29 | 20.9671 | - | - | - |
| 0.0550 | 30 | 20.5548 | - | - | - |
| 0.0569 | 31 | 20.263 | - | - | - |
| 0.0587 | 32 | 19.8474 | - | - | - |
| 0.0606 | 33 | 18.846 | - | - | - |
| 0.0624 | 34 | 18.5923 | - | - | - |
| 0.0642 | 35 | 17.8432 | - | - | - |
| 0.0661 | 36 | 17.6267 | - | - | - |
| 0.0679 | 37 | 17.1291 | - | - | - |
| 0.0697 | 38 | 16.6147 | - | - | - |
| 0.0716 | 39 | 16.1403 | - | - | - |
| 0.0734 | 40 | 16.5382 | - | - | - |
| 0.0752 | 41 | 15.7209 | - | - | - |
| 0.0771 | 42 | 15.565 | - | - | - |
| 0.0789 | 43 | 15.2099 | - | - | - |
| 0.0807 | 44 | 15.2644 | - | - | - |
| 0.0826 | 45 | 14.8458 | - | - | - |
| 0.0844 | 46 | 15.2214 | - | - | - |
| 0.0862 | 47 | 15.194 | - | - | - |
| 0.0881 | 48 | 15.53 | - | - | - |
| 0.0899 | 49 | 14.893 | - | - | - |
| 0.0917 | 50 | 14.4146 | - | - | - |
| 0.0936 | 51 | 14.4308 | - | - | - |
| 0.0954 | 52 | 13.8239 | - | - | - |
| 0.0972 | 53 | 13.9299 | - | - | - |
| 0.0991 | 54 | 14.6545 | - | - | - |
| 0.1009 | 55 | 14.3374 | - | - | - |
| 0.1028 | 56 | 14.5065 | - | - | - |
| 0.1046 | 57 | 13.8447 | - | - | - |
| 0.1064 | 58 | 14.179 | - | - | - |
| 0.1083 | 59 | 13.8866 | - | - | - |
| 0.1101 | 60 | 13.4879 | - | - | - |
| 0.1119 | 61 | 13.6273 | - | - | - |
| 0.1138 | 62 | 13.891 | - | - | - |
| 0.1156 | 63 | 13.6066 | - | - | - |
| 0.1174 | 64 | 13.4999 | - | - | - |
| 0.1193 | 65 | 13.9862 | - | - | - |
| 0.1211 | 66 | 13.4257 | - | - | - |
| 0.1229 | 67 | 13.9192 | - | - | - |
| 0.1248 | 68 | 13.5504 | - | - | - |
| 0.1266 | 69 | 13.3689 | - | - | - |
| 0.1284 | 70 | 13.4802 | - | - | - |
| 0.1303 | 71 | 13.0249 | - | - | - |
| 0.1321 | 72 | 13.2021 | - | - | - |
| 0.1339 | 73 | 13.1101 | - | - | - |
| 0.1358 | 74 | 13.0868 | - | - | - |
| 0.1376 | 75 | 12.8536 | - | - | - |
| 0.1394 | 76 | 12.9317 | - | - | - |
| 0.1413 | 77 | 12.6403 | - | - | - |
| 0.1431 | 78 | 12.9776 | - | - | - |
| 0.1450 | 79 | 13.1359 | - | - | - |
| 0.1468 | 80 | 13.0558 | - | - | - |
| 0.1486 | 81 | 13.0849 | - | - | - |
| 0.1505 | 82 | 12.6719 | - | - | - |
| 0.1523 | 83 | 12.5796 | - | - | - |
| 0.1541 | 84 | 12.472 | - | - | - |
| 0.1560 | 85 | 12.4221 | - | - | - |
| 0.1578 | 86 | 12.0878 | - | - | - |
| 0.1596 | 87 | 12.6923 | - | - | - |
| 0.1615 | 88 | 12.4428 | - | - | - |
| 0.1633 | 89 | 12.2897 | - | - | - |
| 0.1651 | 90 | 12.4254 | - | - | - |
| 0.1670 | 91 | 12.3808 | - | - | - |
| 0.1688 | 92 | 12.5224 | - | - | - |
| 0.1706 | 93 | 12.48 | - | - | - |
| 0.1725 | 94 | 11.8793 | - | - | - |
| 0.1743 | 95 | 11.8582 | - | - | - |
| 0.1761 | 96 | 12.5362 | - | - | - |
| 0.1780 | 97 | 12.3912 | - | - | - |
| 0.1798 | 98 | 12.7162 | - | - | - |
| 0.1817 | 99 | 12.4455 | - | - | - |
| 0.1835 | 100 | 12.4815 | 8.5398 | 0.8199 | - |
| 0.1853 | 101 | 12.1586 | - | - | - |
| 0.1872 | 102 | 11.8041 | - | - | - |
| 0.1890 | 103 | 11.6278 | - | - | - |
| 0.1908 | 104 | 11.8511 | - | - | - |
| 0.1927 | 105 | 11.762 | - | - | - |
| 0.1945 | 106 | 11.568 | - | - | - |
| 0.1963 | 107 | 11.8152 | - | - | - |
| 0.1982 | 108 | 11.9005 | - | - | - |
| 0.2 | 109 | 11.9282 | - | - | - |
| 0.2018 | 110 | 11.8451 | - | - | - |
| 0.2037 | 111 | 12.1208 | - | - | - |
| 0.2055 | 112 | 11.6718 | - | - | - |
| 0.2073 | 113 | 11.0296 | - | - | - |
| 0.2092 | 114 | 11.4185 | - | - | - |
| 0.2110 | 115 | 11.337 | - | - | - |
| 0.2128 | 116 | 10.9242 | - | - | - |
| 0.2147 | 117 | 11.0482 | - | - | - |
| 0.2165 | 118 | 11.3196 | - | - | - |
| 0.2183 | 119 | 11.1849 | - | - | - |
| 0.2202 | 120 | 10.9769 | - | - | - |
| 0.2220 | 121 | 10.5047 | - | - | - |
| 0.2239 | 122 | 11.1094 | - | - | - |
| 0.2257 | 123 | 11.2565 | - | - | - |
| 0.2275 | 124 | 11.1569 | - | - | - |
| 0.2294 | 125 | 11.5391 | - | - | - |
| 0.2312 | 126 | 10.8941 | - | - | - |
| 0.2330 | 127 | 10.8196 | - | - | - |
| 0.2349 | 128 | 11.0836 | - | - | - |
| 0.2367 | 129 | 11.4241 | - | - | - |
| 0.2385 | 130 | 11.4976 | - | - | - |
| 0.2404 | 131 | 10.938 | - | - | - |
| 0.2422 | 132 | 11.5283 | - | - | - |
| 0.2440 | 133 | 11.4238 | - | - | - |
| 0.2459 | 134 | 11.3364 | - | - | - |
| 0.2477 | 135 | 11.225 | - | - | - |
| 0.2495 | 136 | 11.0415 | - | - | - |
| 0.2514 | 137 | 10.8503 | - | - | - |
| 0.2532 | 138 | 10.9302 | - | - | - |
| 0.2550 | 139 | 10.5476 | - | - | - |
| 0.2569 | 140 | 10.8422 | - | - | - |
| 0.2587 | 141 | 10.4239 | - | - | - |
| 0.2606 | 142 | 10.5155 | - | - | - |
| 0.2624 | 143 | 10.589 | - | - | - |
| 0.2642 | 144 | 10.6116 | - | - | - |
| 0.2661 | 145 | 10.7158 | - | - | - |
| 0.2679 | 146 | 10.6952 | - | - | - |
| 0.2697 | 147 | 10.3678 | - | - | - |
| 0.2716 | 148 | 11.159 | - | - | - |
| 0.2734 | 149 | 11.3336 | - | - | - |
| 0.2752 | 150 | 10.7669 | - | - | - |
| 0.2771 | 151 | 10.5946 | - | - | - |
| 0.2789 | 152 | 10.9448 | - | - | - |
| 0.2807 | 153 | 10.7132 | - | - | - |
| 0.2826 | 154 | 10.5812 | - | - | - |
| 0.2844 | 155 | 10.7827 | - | - | - |
| 0.2862 | 156 | 10.7807 | - | - | - |
| 0.2881 | 157 | 10.7351 | - | - | - |
| 0.2899 | 158 | 10.7904 | - | - | - |
| 0.2917 | 159 | 10.5921 | - | - | - |
| 0.2936 | 160 | 10.2996 | - | - | - |
| 0.2954 | 161 | 10.2353 | - | - | - |
| 0.2972 | 162 | 10.2108 | - | - | - |
| 0.2991 | 163 | 10.089 | - | - | - |
| 0.3009 | 164 | 10.1736 | - | - | - |
| 0.3028 | 165 | 10.2599 | - | - | - |
| 0.3046 | 166 | 10.4347 | - | - | - |
| 0.3064 | 167 | 10.9999 | - | - | - |
| 0.3083 | 168 | 11.1655 | - | - | - |
| 0.3101 | 169 | 10.8125 | - | - | - |
| 0.3119 | 170 | 10.5497 | - | - | - |
| 0.3138 | 171 | 10.6918 | - | - | - |
| 0.3156 | 172 | 10.4792 | - | - | - |
| 0.3174 | 173 | 10.6018 | - | - | - |
| 0.3193 | 174 | 10.2092 | - | - | - |
| 0.3211 | 175 | 10.5625 | - | - | - |
| 0.3229 | 176 | 10.3539 | - | - | - |
| 0.3248 | 177 | 9.5403 | - | - | - |
| 0.3266 | 178 | 10.2351 | - | - | - |
| 0.3284 | 179 | 10.1557 | - | - | - |
| 0.3303 | 180 | 10.0721 | - | - | - |
| 0.3321 | 181 | 9.721 | - | - | - |
| 0.3339 | 182 | 9.7519 | - | - | - |
| 0.3358 | 183 | 9.7737 | - | - | - |
| 0.3376 | 184 | 9.5207 | - | - | - |
| 0.3394 | 185 | 9.6557 | - | - | - |
| 0.3413 | 186 | 9.7205 | - | - | - |
| 0.3431 | 187 | 9.9902 | - | - | - |
| 0.3450 | 188 | 10.1699 | - | - | - |
| 0.3468 | 189 | 10.5102 | - | - | - |
| 0.3486 | 190 | 10.2026 | - | - | - |
| 0.3505 | 191 | 10.1148 | - | - | - |
| 0.3523 | 192 | 9.5341 | - | - | - |
| 0.3541 | 193 | 9.5213 | - | - | - |
| 0.3560 | 194 | 9.7469 | - | - | - |
| 0.3578 | 195 | 10.1795 | - | - | - |
| 0.3596 | 196 | 10.3835 | - | - | - |
| 0.3615 | 197 | 10.7346 | - | - | - |
| 0.3633 | 198 | 9.9378 | - | - | - |
| 0.3651 | 199 | 9.7758 | - | - | - |
| 0.3670 | 200 | 10.3206 | 7.0991 | 0.8294 | - |
| 0.3688 | 201 | 9.7032 | - | - | - |
| 0.3706 | 202 | 9.8851 | - | - | - |
| 0.3725 | 203 | 9.9285 | - | - | - |
| 0.3743 | 204 | 10.0227 | - | - | - |
| 0.3761 | 205 | 9.8062 | - | - | - |
| 0.3780 | 206 | 9.9988 | - | - | - |
| 0.3798 | 207 | 10.0256 | - | - | - |
| 0.3817 | 208 | 9.8837 | - | - | - |
| 0.3835 | 209 | 10.0787 | - | - | - |
| 0.3853 | 210 | 9.5776 | - | - | - |
| 0.3872 | 211 | 9.6239 | - | - | - |
| 0.3890 | 212 | 9.717 | - | - | - |
| 0.3908 | 213 | 10.1639 | - | - | - |
| 0.3927 | 214 | 9.4994 | - | - | - |
| 0.3945 | 215 | 9.6895 | - | - | - |
| 0.3963 | 216 | 9.4938 | - | - | - |
| 0.3982 | 217 | 9.3008 | - | - | - |
| 0.4 | 218 | 9.6183 | - | - | - |
| 0.4018 | 219 | 9.3632 | - | - | - |
| 0.4037 | 220 | 9.3575 | - | - | - |
| 0.4055 | 221 | 9.4888 | - | - | - |
| 0.4073 | 222 | 9.337 | - | - | - |
| 0.4092 | 223 | 9.9598 | - | - | - |
| 0.4110 | 224 | 9.345 | - | - | - |
| 0.4128 | 225 | 9.2595 | - | - | - |
| 0.4147 | 226 | 9.3508 | - | - | - |
| 0.4165 | 227 | 9.8293 | - | - | - |
| 0.4183 | 228 | 9.8365 | - | - | - |
| 0.4202 | 229 | 9.6528 | - | - | - |
| 0.4220 | 230 | 9.9696 | - | - | - |
| 0.4239 | 231 | 10.113 | - | - | - |
| 0.4257 | 232 | 9.9706 | - | - | - |
| 0.4275 | 233 | 9.577 | - | - | - |
| 0.4294 | 234 | 9.7624 | - | - | - |
| 0.4312 | 235 | 9.5083 | - | - | - |
| 0.4330 | 236 | 9.5067 | - | - | - |
| 0.4349 | 237 | 9.1004 | - | - | - |
| 0.4367 | 238 | 8.914 | - | - | - |
| 0.4385 | 239 | 9.6852 | - | - | - |
| 0.4404 | 240 | 9.573 | - | - | - |
| 0.4422 | 241 | 9.8598 | - | - | - |
| 0.4440 | 242 | 10.1793 | - | - | - |
| 0.4459 | 243 | 10.2789 | - | - | - |
| 0.4477 | 244 | 9.9536 | - | - | - |
| 0.4495 | 245 | 9.3878 | - | - | - |
| 0.4514 | 246 | 9.6734 | - | - | - |
| 0.4532 | 247 | 9.3747 | - | - | - |
| 0.4550 | 248 | 8.8334 | - | - | - |
| 0.4569 | 249 | 9.7495 | - | - | - |
| 0.4587 | 250 | 8.8468 | - | - | - |
| 0.4606 | 251 | 9.3828 | - | - | - |
| 0.4624 | 252 | 9.1118 | - | - | - |
| 0.4642 | 253 | 9.3682 | - | - | - |
| 0.4661 | 254 | 9.3647 | - | - | - |
| 0.4679 | 255 | 9.8533 | - | - | - |
| 0.4697 | 256 | 9.2787 | - | - | - |
| 0.4716 | 257 | 8.9831 | - | - | - |
| 0.4734 | 258 | 9.0524 | - | - | - |
| 0.4752 | 259 | 9.5378 | - | - | - |
| 0.4771 | 260 | 9.4227 | - | - | - |
| 0.4789 | 261 | 9.3545 | - | - | - |
| 0.4807 | 262 | 8.8428 | - | - | - |
| 0.4826 | 263 | 9.1284 | - | - | - |
| 0.4844 | 264 | 8.7769 | - | - | - |
| 0.4862 | 265 | 9.0381 | - | - | - |
| 0.4881 | 266 | 9.0261 | - | - | - |
| 0.4899 | 267 | 8.811 | - | - | - |
| 0.4917 | 268 | 9.0848 | - | - | - |
| 0.4936 | 269 | 9.0951 | - | - | - |
| 0.4954 | 270 | 9.0682 | - | - | - |
| 0.4972 | 271 | 9.0418 | - | - | - |
| 0.4991 | 272 | 9.7316 | - | - | - |
| 0.5009 | 273 | 9.263 | - | - | - |
| 0.5028 | 274 | 9.624 | - | - | - |
| 0.5046 | 275 | 10.0133 | - | - | - |
| 0.5064 | 276 | 9.0789 | - | - | - |
| 0.5083 | 277 | 9.1399 | - | - | - |
| 0.5101 | 278 | 9.3854 | - | - | - |
| 0.5119 | 279 | 8.9982 | - | - | - |
| 0.5138 | 280 | 9.1342 | - | - | - |
| 0.5156 | 281 | 9.0517 | - | - | - |
| 0.5174 | 282 | 9.5637 | - | - | - |
| 0.5193 | 283 | 9.5213 | - | - | - |
| 0.5211 | 284 | 9.9231 | - | - | - |
| 0.5229 | 285 | 10.3441 | - | - | - |
| 0.5248 | 286 | 9.6162 | - | - | - |
| 0.5266 | 287 | 9.4794 | - | - | - |
| 0.5284 | 288 | 9.2728 | - | - | - |
| 0.5303 | 289 | 9.411 | - | - | - |
| 0.5321 | 290 | 9.5806 | - | - | - |
| 0.5339 | 291 | 9.4193 | - | - | - |
| 0.5358 | 292 | 9.3528 | - | - | - |
| 0.5376 | 293 | 9.7581 | - | - | - |
| 0.5394 | 294 | 9.4407 | - | - | - |
| 0.5413 | 295 | 9.027 | - | - | - |
| 0.5431 | 296 | 9.4272 | - | - | - |
| 0.5450 | 297 | 9.2733 | - | - | - |
| 0.5468 | 298 | 9.3 | - | - | - |
| 0.5486 | 299 | 9.6388 | - | - | - |
| 0.5505 | 300 | 9.0698 | 6.8356 | 0.8273 | - |
| 0.5523 | 301 | 9.4613 | - | - | - |
| 0.5541 | 302 | 9.9061 | - | - | - |
| 0.5560 | 303 | 9.3524 | - | - | - |
| 0.5578 | 304 | 9.1935 | - | - | - |
| 0.5596 | 305 | 9.1243 | - | - | - |
| 0.5615 | 306 | 8.8865 | - | - | - |
| 0.5633 | 307 | 9.4411 | - | - | - |
| 0.5651 | 308 | 9.1322 | - | - | - |
| 0.5670 | 309 | 9.3072 | - | - | - |
| 0.5688 | 310 | 8.4299 | - | - | - |
| 0.5706 | 311 | 8.9471 | - | - | - |
| 0.5725 | 312 | 8.5097 | - | - | - |
| 0.5743 | 313 | 9.1158 | - | - | - |
| 0.5761 | 314 | 9.0221 | - | - | - |
| 0.5780 | 315 | 9.5871 | - | - | - |
| 0.5798 | 316 | 9.3789 | - | - | - |
| 0.5817 | 317 | 9.1566 | - | - | - |
| 0.5835 | 318 | 9.0472 | - | - | - |
| 0.5853 | 319 | 8.947 | - | - | - |
| 0.5872 | 320 | 9.1791 | - | - | - |
| 0.5890 | 321 | 8.8764 | - | - | - |
| 0.5908 | 322 | 8.9794 | - | - | - |
| 0.5927 | 323 | 9.2044 | - | - | - |
| 0.5945 | 324 | 9.0374 | - | - | - |
| 0.5963 | 325 | 9.3389 | - | - | - |
| 0.5982 | 326 | 9.7387 | - | - | - |
| 0.6 | 327 | 9.4248 | - | - | - |
| 0.6018 | 328 | 9.4799 | - | - | - |
| 0.6037 | 329 | 8.9019 | - | - | - |
| 0.6055 | 330 | 9.113 | - | - | - |
| 0.6073 | 331 | 9.3148 | - | - | - |
| 0.6092 | 332 | 8.9871 | - | - | - |
| 0.6110 | 333 | 8.5404 | - | - | - |
| 0.6128 | 334 | 9.1587 | - | - | - |
| 0.6147 | 335 | 8.9698 | - | - | - |
| 0.6165 | 336 | 9.3393 | - | - | - |
| 0.6183 | 337 | 9.4845 | - | - | - |
| 0.6202 | 338 | 9.6075 | - | - | - |
| 0.6220 | 339 | 9.426 | - | - | - |
| 0.6239 | 340 | 9.0633 | - | - | - |
| 0.6257 | 341 | 9.1017 | - | - | - |
| 0.6275 | 342 | 9.2461 | - | - | - |
| 0.6294 | 343 | 9.065 | - | - | - |
| 0.6312 | 344 | 9.4668 | - | - | - |
| 0.6330 | 345 | 9.0267 | - | - | - |
| 0.6349 | 346 | 9.2938 | - | - | - |
| 0.6367 | 347 | 9.391 | - | - | - |
| 0.6385 | 348 | 9.2386 | - | - | - |
| 0.6404 | 349 | 9.5285 | - | - | - |
| 0.6422 | 350 | 9.5958 | - | - | - |
| 0.6440 | 351 | 9.157 | - | - | - |
| 0.6459 | 352 | 9.4166 | - | - | - |
| 0.6477 | 353 | 9.358 | - | - | - |
| 0.6495 | 354 | 9.4497 | - | - | - |
| 0.6514 | 355 | 9.407 | - | - | - |
| 0.6532 | 356 | 9.1505 | - | - | - |
| 0.6550 | 357 | 9.403 | - | - | - |
| 0.6569 | 358 | 9.1949 | - | - | - |
| 0.6587 | 359 | 8.7922 | - | - | - |
| 0.6606 | 360 | 8.883 | - | - | - |
| 0.6624 | 361 | 8.6828 | - | - | - |
| 0.6642 | 362 | 8.5654 | - | - | - |
| 0.6661 | 363 | 8.705 | - | - | - |
| 0.6679 | 364 | 8.8329 | - | - | - |
| 0.6697 | 365 | 9.1604 | - | - | - |
| 0.6716 | 366 | 9.1609 | - | - | - |
| 0.6734 | 367 | 9.4693 | - | - | - |
| 0.6752 | 368 | 9.1431 | - | - | - |
| 0.6771 | 369 | 8.7564 | - | - | - |
| 0.6789 | 370 | 9.1378 | - | - | - |
| 0.6807 | 371 | 8.8472 | - | - | - |
| 0.6826 | 372 | 8.9159 | - | - | - |
| 0.6844 | 373 | 8.9551 | - | - | - |
| 0.6862 | 374 | 9.2721 | - | - | - |
| 0.6881 | 375 | 8.7511 | - | - | - |
| 0.6899 | 376 | 9.1683 | - | - | - |
| 0.6917 | 377 | 8.8438 | - | - | - |
| 0.6936 | 378 | 8.6151 | - | - | - |
| 0.6954 | 379 | 8.7015 | - | - | - |
| 0.6972 | 380 | 7.6009 | - | - | - |
| 0.6991 | 381 | 7.3242 | - | - | - |
| 0.7009 | 382 | 7.4182 | - | - | - |
| 0.7028 | 383 | 7.2576 | - | - | - |
| 0.7046 | 384 | 7.0578 | - | - | - |
| 0.7064 | 385 | 6.0212 | - | - | - |
| 0.7083 | 386 | 5.9868 | - | - | - |
| 0.7101 | 387 | 6.033 | - | - | - |
| 0.7119 | 388 | 5.8085 | - | - | - |
| 0.7138 | 389 | 5.6002 | - | - | - |
| 0.7156 | 390 | 5.439 | - | - | - |
| 0.7174 | 391 | 5.1661 | - | - | - |
| 0.7193 | 392 | 5.1261 | - | - | - |
| 0.7211 | 393 | 5.5393 | - | - | - |
| 0.7229 | 394 | 4.8909 | - | - | - |
| 0.7248 | 395 | 5.2803 | - | - | - |
| 0.7266 | 396 | 5.1639 | - | - | - |
| 0.7284 | 397 | 4.7125 | - | - | - |
| 0.7303 | 398 | 4.842 | - | - | - |
| 0.7321 | 399 | 5.0971 | - | - | - |
| 0.7339 | 400 | 4.5101 | 5.0650 | 0.8590 | - |
| 0.7358 | 401 | 4.3422 | - | - | - |
| 0.7376 | 402 | 4.719 | - | - | - |
| 0.7394 | 403 | 4.1823 | - | - | - |
| 0.7413 | 404 | 3.7903 | - | - | - |
| 0.7431 | 405 | 3.886 | - | - | - |
| 0.7450 | 406 | 4.1115 | - | - | - |
| 0.7468 | 407 | 3.9201 | - | - | - |
| 0.7486 | 408 | 3.9291 | - | - | - |
| 0.7505 | 409 | 4.0412 | - | - | - |
| 0.7523 | 410 | 3.6614 | - | - | - |
| 0.7541 | 411 | 3.5718 | - | - | - |
| 0.7560 | 412 | 3.6689 | - | - | - |
| 0.7578 | 413 | 3.7457 | - | - | - |
| 0.7596 | 414 | 3.4272 | - | - | - |
| 0.7615 | 415 | 3.5112 | - | - | - |
| 0.7633 | 416 | 3.8348 | - | - | - |
| 0.7651 | 417 | 3.5177 | - | - | - |
| 0.7670 | 418 | 3.3441 | - | - | - |
| 0.7688 | 419 | 3.362 | - | - | - |
| 0.7706 | 420 | 3.4926 | - | - | - |
| 0.7725 | 421 | 3.4722 | - | - | - |
| 0.7743 | 422 | 2.8568 | - | - | - |
| 0.7761 | 423 | 3.3396 | - | - | - |
| 0.7780 | 424 | 2.972 | - | - | - |
| 0.7798 | 425 | 3.6889 | - | - | - |
| 0.7817 | 426 | 3.5154 | - | - | - |
| 0.7835 | 427 | 3.4098 | - | - | - |
| 0.7853 | 428 | 3.4569 | - | - | - |
| 0.7872 | 429 | 3.4916 | - | - | - |
| 0.7890 | 430 | 3.7394 | - | - | - |
| 0.7908 | 431 | 3.332 | - | - | - |
| 0.7927 | 432 | 3.3767 | - | - | - |
| 0.7945 | 433 | 3.1173 | - | - | - |
| 0.7963 | 434 | 3.2257 | - | - | - |
| 0.7982 | 435 | 3.3629 | - | - | - |
| 0.8 | 436 | 3.1992 | - | - | - |
| 0.8018 | 437 | 3.1252 | - | - | - |
| 0.8037 | 438 | 3.5155 | - | - | - |
| 0.8055 | 439 | 3.2583 | - | - | - |
| 0.8073 | 440 | 2.9001 | - | - | - |
| 0.8092 | 441 | 3.1542 | - | - | - |
| 0.8110 | 442 | 3.0473 | - | - | - |
| 0.8128 | 443 | 3.0446 | - | - | - |
| 0.8147 | 444 | 3.3807 | - | - | - |
| 0.8165 | 445 | 3.1246 | - | - | - |
| 0.8183 | 446 | 3.1922 | - | - | - |
| 0.8202 | 447 | 3.09 | - | - | - |
| 0.8220 | 448 | 3.4341 | - | - | - |
| 0.8239 | 449 | 3.0926 | - | - | - |
| 0.8257 | 450 | 2.9746 | - | - | - |
| 0.8275 | 451 | 3.1014 | - | - | - |
| 0.8294 | 452 | 3.2205 | - | - | - |
| 0.8312 | 453 | 3.1147 | - | - | - |
| 0.8330 | 454 | 2.9682 | - | - | - |
| 0.8349 | 455 | 3.1681 | - | - | - |
| 0.8367 | 456 | 2.9821 | - | - | - |
| 0.8385 | 457 | 2.8484 | - | - | - |
| 0.8404 | 458 | 3.0341 | - | - | - |
| 0.8422 | 459 | 3.0632 | - | - | - |
| 0.8440 | 460 | 3.2026 | - | - | - |
| 0.8459 | 461 | 3.132 | - | - | - |
| 0.8477 | 462 | 3.0209 | - | - | - |
| 0.8495 | 463 | 2.7183 | - | - | - |
| 0.8514 | 464 | 3.0257 | - | - | - |
| 0.8532 | 465 | 3.1462 | - | - | - |
| 0.8550 | 466 | 2.8747 | - | - | - |
| 0.8569 | 467 | 3.0932 | - | - | - |
| 0.8587 | 468 | 3.0097 | - | - | - |
| 0.8606 | 469 | 3.0956 | - | - | - |
| 0.8624 | 470 | 3.019 | - | - | - |
| 0.8642 | 471 | 3.1342 | - | - | - |
| 0.8661 | 472 | 2.688 | - | - | - |
| 0.8679 | 473 | 2.8892 | - | - | - |
| 0.8697 | 474 | 3.1589 | - | - | - |
| 0.8716 | 475 | 2.9274 | - | - | - |
| 0.8734 | 476 | 2.8554 | - | - | - |
| 0.8752 | 477 | 2.694 | - | - | - |
| 0.8771 | 478 | 2.7397 | - | - | - |
| 0.8789 | 479 | 2.6452 | - | - | - |
| 0.8807 | 480 | 3.0158 | - | - | - |
| 0.8826 | 481 | 3.0148 | - | - | - |
| 0.8844 | 482 | 2.5704 | - | - | - |
| 0.8862 | 483 | 2.6755 | - | - | - |
| 0.8881 | 484 | 2.7805 | - | - | - |
| 0.8899 | 485 | 2.8554 | - | - | - |
| 0.8917 | 486 | 2.6966 | - | - | - |
| 0.8936 | 487 | 2.8759 | - | - | - |
| 0.8954 | 488 | 2.8838 | - | - | - |
| 0.8972 | 489 | 2.7885 | - | - | - |
| 0.8991 | 490 | 2.7576 | - | - | - |
| 0.9009 | 491 | 2.9139 | - | - | - |
| 0.9028 | 492 | 2.6583 | - | - | - |
| 0.9046 | 493 | 2.9654 | - | - | - |
| 0.9064 | 494 | 2.551 | - | - | - |
| 0.9083 | 495 | 2.5596 | - | - | - |
| 0.9101 | 496 | 2.9595 | - | - | - |
| 0.9119 | 497 | 2.8677 | - | - | - |
| 0.9138 | 498 | 2.5793 | - | - | - |
| 0.9156 | 499 | 2.5415 | - | - | - |
| 0.9174 | 500 | 2.9738 | 4.8764 | 0.8651 | - |
| 0.9193 | 501 | 2.5838 | - | - | - |
| 0.9211 | 502 | 2.6544 | - | - | - |
| 0.9229 | 503 | 2.7046 | - | - | - |
| 0.9248 | 504 | 2.6339 | - | - | - |
| 0.9266 | 505 | 2.687 | - | - | - |
| 0.9284 | 506 | 2.7863 | - | - | - |
| 0.9303 | 507 | 2.7409 | - | - | - |
| 0.9321 | 508 | 2.656 | - | - | - |
| 0.9339 | 509 | 2.7456 | - | - | - |
| 0.9358 | 510 | 2.6589 | - | - | - |
| 0.9376 | 511 | 2.697 | - | - | - |
| 0.9394 | 512 | 2.6443 | - | - | - |
| 0.9413 | 513 | 2.7357 | - | - | - |
| 0.9431 | 514 | 2.969 | - | - | - |
| 0.9450 | 515 | 2.4175 | - | - | - |
| 0.9468 | 516 | 2.5424 | - | - | - |
| 0.9486 | 517 | 2.4773 | - | - | - |
| 0.9505 | 518 | 2.6269 | - | - | - |
| 0.9523 | 519 | 2.6288 | - | - | - |
| 0.9541 | 520 | 2.9471 | - | - | - |
| 0.9560 | 521 | 2.9775 | - | - | - |
| 0.9578 | 522 | 2.9949 | - | - | - |
| 0.9596 | 523 | 2.7084 | - | - | - |
| 0.9615 | 524 | 2.6431 | - | - | - |
| 0.9633 | 525 | 2.5849 | - | - | - |
| 0.9651 | 526 | 7.353 | - | - | - |
| 0.9670 | 527 | 9.1463 | - | - | - |
| 0.9688 | 528 | 10.9846 | - | - | - |
| 0.9706 | 529 | 10.6362 | - | - | - |
| 0.9725 | 530 | 10.0763 | - | - | - |
| 0.9743 | 531 | 9.7147 | - | - | - |
| 0.9761 | 532 | 9.3911 | - | - | - |
| 0.9780 | 533 | 9.3722 | - | - | - |
| 0.9798 | 534 | 10.794 | - | - | - |
| 0.9817 | 535 | 11.661 | - | - | - |
| 0.9835 | 536 | 11.4706 | - | - | - |
| 0.9853 | 537 | 12.0868 | - | - | - |
| 0.9872 | 538 | 12.0017 | - | - | - |
| 0.9890 | 539 | 11.7965 | - | - | - |
| 0.9908 | 540 | 12.5961 | - | - | - |
| 0.9927 | 541 | 9.6563 | - | - | - |
| 0.9945 | 542 | 11.5097 | - | - | - |
| 0.9963 | 543 | 12.0945 | - | - | - |
| 0.9982 | 544 | 10.7032 | - | - | - |
| 1.0 | 545 | 10.5622 | - | - | 0.8505 |
</details>
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.48.0.dev0
- PyTorch: 2.1.0+cu118
- Accelerate: 1.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## 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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