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MultiLinguSwahili-mxbai-embed-large-v1-nli-matryoshka

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:1115700
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
Inference Endpoints
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MultiLinguSwahili-mxbai-embed-large-v1-nli-matryoshka / README.md
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---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1115700
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: mixedbread-ai/mxbai-embed-large-v1
datasets: []
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: Ndege mwenye mdomo mrefu katikati ya ndege.
sentences:
- Panya anayekimbia juu ya gurudumu.
- Mtu anashindana katika mashindano ya mbio.
- Ndege anayeruka.
- source_sentence: Msichana mchanga mwenye nywele nyeusi anakabili kamera na kushikilia
mfuko wa karatasi wakati amevaa shati la machungwa na mabawa ya kipepeo yenye
rangi nyingi.
sentences:
- Mwanamke mzee anakataa kupigwa picha.
- mtu akila na mvulana mdogo kwenye kijia cha jiji
- Msichana mchanga anakabili kamera.
- source_sentence: Wanawake na watoto wameketi nje katika kivuli wakati kikundi cha
watoto wadogo wameketi ndani katika kivuli.
sentences:
- Mwanamke na watoto na kukaa chini.
- Mwanamke huyo anakimbia.
- Watu wanasafiri kwa baiskeli.
- source_sentence: Mtoto mdogo anaruka mikononi mwa mwanamke aliyevalia suti nyeusi
ya kuogelea akiwa kwenye dimbwi.
sentences:
- Mtoto akiruka mikononi mwa mwanamke aliyevalia suti ya kuogelea kwenye dimbwi.
- Someone is holding oranges and walking
- Mama na binti wakinunua viatu.
- source_sentence: Mwanamume na mwanamke wachanga waliovaa mikoba wanaweka au kuondoa
kitu kutoka kwenye mti mweupe wa zamani, huku watu wengine wamesimama au wameketi
nyuma.
sentences:
- tai huruka
- mwanamume na mwanamke wenye mikoba
- Wanaume wawili wameketi karibu na mwanamke.
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on mixedbread-ai/mxbai-embed-large-v1
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 768
type: sts-test-768
metrics:
- type: pearson_cosine
value: 0.7132706238512434
name: Pearson Cosine
- type: spearman_cosine
value: 0.7051536841043449
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6350557885817543
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6244954371574937
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6378177587771076
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.62660657495158
name: Spearman Euclidean
- type: pearson_dot
value: 0.5703890363847545
name: Pearson Dot
- type: spearman_dot
value: 0.5603263508842454
name: Spearman Dot
- type: pearson_max
value: 0.7132706238512434
name: Pearson Max
- type: spearman_max
value: 0.7051536841043449
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 512
type: sts-test-512
metrics:
- type: pearson_cosine
value: 0.7123126668825692
name: Pearson Cosine
- type: spearman_cosine
value: 0.703609966898051
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6388434483972429
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6281398975795567
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6419247701070586
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6310772735048756
name: Spearman Euclidean
- type: pearson_dot
value: 0.5490282729432092
name: Pearson Dot
- type: spearman_dot
value: 0.5413067160939415
name: Spearman Dot
- type: pearson_max
value: 0.7123126668825692
name: Pearson Max
- type: spearman_max
value: 0.703609966898051
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 256
type: sts-test-256
metrics:
- type: pearson_cosine
value: 0.7077861691807766
name: Pearson Cosine
- type: spearman_cosine
value: 0.7000862774499549
name: Spearman Cosine
- type: pearson_manhattan
value: 0.643288835639384
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6325033715865666
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6460218727916103
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6343987601663327
name: Spearman Euclidean
- type: pearson_dot
value: 0.5115397990320991
name: Pearson Dot
- type: spearman_dot
value: 0.5059807217044437
name: Spearman Dot
- type: pearson_max
value: 0.7077861691807766
name: Pearson Max
- type: spearman_max
value: 0.7000862774499549
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 128
type: sts-test-128
metrics:
- type: pearson_cosine
value: 0.7028807205576924
name: Pearson Cosine
- type: spearman_cosine
value: 0.6967519700533644
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6497250338362586
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6388633921530281
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.650616035583963
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6388752538429412
name: Spearman Euclidean
- type: pearson_dot
value: 0.473211586813894
name: Pearson Dot
- type: spearman_dot
value: 0.468867985238822
name: Spearman Dot
- type: pearson_max
value: 0.7028807205576924
name: Pearson Max
- type: spearman_max
value: 0.6967519700533644
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 64
type: sts-test-64
metrics:
- type: pearson_cosine
value: 0.6904004410097948
name: Pearson Cosine
- type: spearman_cosine
value: 0.684874855155489
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6498424787891348
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6359659710580793
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6513241092538908
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6369881684130174
name: Spearman Euclidean
- type: pearson_dot
value: 0.42134226096367267
name: Pearson Dot
- type: spearman_dot
value: 0.4179675632105097
name: Spearman Dot
- type: pearson_max
value: 0.6904004410097948
name: Pearson Max
- type: spearman_max
value: 0.684874855155489
name: Spearman Max
---
# SentenceTransformer based on mixedbread-ai/mxbai-embed-large-v1
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) on the Mollel/swahili-n_li-triplet-swh-eng dataset. 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:** [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) <!-- at revision 990580e27d329c7408b3741ecff85876e128e203 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- Mollel/swahili-n_li-triplet-swh-eng
<!-- - **Language:** Unknown -->
<!-- - **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': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## 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("sartifyllc/MultiLinguSwahili-mxbai-embed-large-v1-nli-matryoshka")
# Run inference
sentences = [
'Mwanamume na mwanamke wachanga waliovaa mikoba wanaweka au kuondoa kitu kutoka kwenye mti mweupe wa zamani, huku watu wengine wamesimama au wameketi nyuma.',
'mwanamume na mwanamke wenye mikoba',
'tai huruka',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-test-768`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.7133 |
| **spearman_cosine** | **0.7052** |
| pearson_manhattan | 0.6351 |
| spearman_manhattan | 0.6245 |
| pearson_euclidean | 0.6378 |
| spearman_euclidean | 0.6266 |
| pearson_dot | 0.5704 |
| spearman_dot | 0.5603 |
| pearson_max | 0.7133 |
| spearman_max | 0.7052 |
#### Semantic Similarity
* Dataset: `sts-test-512`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.7123 |
| **spearman_cosine** | **0.7036** |
| pearson_manhattan | 0.6388 |
| spearman_manhattan | 0.6281 |
| pearson_euclidean | 0.6419 |
| spearman_euclidean | 0.6311 |
| pearson_dot | 0.549 |
| spearman_dot | 0.5413 |
| pearson_max | 0.7123 |
| spearman_max | 0.7036 |
#### Semantic Similarity
* Dataset: `sts-test-256`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.7078 |
| **spearman_cosine** | **0.7001** |
| pearson_manhattan | 0.6433 |
| spearman_manhattan | 0.6325 |
| pearson_euclidean | 0.646 |
| spearman_euclidean | 0.6344 |
| pearson_dot | 0.5115 |
| spearman_dot | 0.506 |
| pearson_max | 0.7078 |
| spearman_max | 0.7001 |
#### Semantic Similarity
* Dataset: `sts-test-128`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.7029 |
| **spearman_cosine** | **0.6968** |
| pearson_manhattan | 0.6497 |
| spearman_manhattan | 0.6389 |
| pearson_euclidean | 0.6506 |
| spearman_euclidean | 0.6389 |
| pearson_dot | 0.4732 |
| spearman_dot | 0.4689 |
| pearson_max | 0.7029 |
| spearman_max | 0.6968 |
#### Semantic Similarity
* Dataset: `sts-test-64`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.6904 |
| **spearman_cosine** | **0.6849** |
| pearson_manhattan | 0.6498 |
| spearman_manhattan | 0.636 |
| pearson_euclidean | 0.6513 |
| spearman_euclidean | 0.637 |
| pearson_dot | 0.4213 |
| spearman_dot | 0.418 |
| pearson_max | 0.6904 |
| spearman_max | 0.6849 |
<!--
## 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.*
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## Training Details
### Training Dataset
#### Mollel/swahili-n_li-triplet-swh-eng
* Dataset: Mollel/swahili-n_li-triplet-swh-eng
* Size: 1,115,700 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: 15.18 tokens</li><li>max: 80 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 18.53 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 17.8 tokens</li><li>max: 53 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>Mtu aliyepanda farasi anaruka juu ya ndege iliyovunjika.</code> | <code>Mtu yuko nje, juu ya farasi.</code> | <code>Mtu yuko kwenye mkahawa, akiagiza omelette.</code> |
| <code>Children smiling and waving at camera</code> | <code>There are children present</code> | <code>The kids are frowning</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
#### Mollel/swahili-n_li-triplet-swh-eng
* Dataset: Mollel/swahili-n_li-triplet-swh-eng
* Size: 13,168 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: 26.43 tokens</li><li>max: 94 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.37 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.7 tokens</li><li>max: 54 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>Wanawake wawili wanakumbatiana huku wakishikilia vifurushi vya kwenda.</code> | <code>Wanawake wawili wanashikilia vifurushi.</code> | <code>Wanaume hao wanapigana nje ya duka la vyakula vitamu.</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> |
* 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
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `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
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `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`: 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
- `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, '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`: 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_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | sts-test-128_spearman_cosine | sts-test-256_spearman_cosine | sts-test-512_spearman_cosine | sts-test-64_spearman_cosine | sts-test-768_spearman_cosine |
|:------:|:-----:|:-------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|
| 0.0029 | 100 | 9.6293 | - | - | - | - | - |
| 0.0057 | 200 | 8.1059 | - | - | - | - | - |
| 0.0086 | 300 | 8.6054 | - | - | - | - | - |
| 0.0115 | 400 | 6.8896 | - | - | - | - | - |
| 0.0143 | 500 | 6.9096 | - | - | - | - | - |
| 0.0172 | 600 | 6.7797 | - | - | - | - | - |
| 0.0201 | 700 | 6.8013 | - | - | - | - | - |
| 0.0229 | 800 | 7.49 | - | - | - | - | - |
| 0.0258 | 900 | 7.2888 | - | - | - | - | - |
| 0.0287 | 1000 | 7.3862 | - | - | - | - | - |
| 0.0315 | 1100 | 6.8292 | - | - | - | - | - |
| 0.0344 | 1200 | 6.2505 | - | - | - | - | - |
| 0.0373 | 1300 | 4.8736 | - | - | - | - | - |
| 0.0402 | 1400 | 4.7668 | - | - | - | - | - |
| 0.0430 | 1500 | 5.0843 | - | - | - | - | - |
| 0.0459 | 1600 | 3.8507 | - | - | - | - | - |
| 0.0488 | 1700 | 5.1235 | - | - | - | - | - |
| 0.0516 | 1800 | 4.6187 | - | - | - | - | - |
| 0.0545 | 1900 | 3.8704 | - | - | - | - | - |
| 0.0574 | 2000 | 3.3635 | - | - | - | - | - |
| 0.0602 | 2100 | 3.4204 | - | - | - | - | - |
| 0.0631 | 2200 | 3.5258 | - | - | - | - | - |
| 0.0660 | 2300 | 3.6726 | - | - | - | - | - |
| 0.0688 | 2400 | 3.8007 | - | - | - | - | - |
| 0.0717 | 2500 | 3.5593 | - | - | - | - | - |
| 0.0746 | 2600 | 3.3407 | - | - | - | - | - |
| 0.0774 | 2700 | 4.6645 | - | - | - | - | - |
| 0.0803 | 2800 | 4.5431 | - | - | - | - | - |
| 0.0832 | 2900 | 4.0496 | - | - | - | - | - |
| 0.0860 | 3000 | 3.8313 | - | - | - | - | - |
| 0.0889 | 3100 | 3.6324 | - | - | - | - | - |
| 0.0918 | 3200 | 3.3442 | - | - | - | - | - |
| 0.0946 | 3300 | 2.9437 | - | - | - | - | - |
| 0.0975 | 3400 | 2.8352 | - | - | - | - | - |
| 0.1004 | 3500 | 2.8069 | - | - | - | - | - |
| 0.1033 | 3600 | 2.9686 | - | - | - | - | - |
| 0.1061 | 3700 | 2.8355 | - | - | - | - | - |
| 0.1090 | 3800 | 2.9827 | - | - | - | - | - |
| 0.1119 | 3900 | 3.1181 | - | - | - | - | - |
| 0.1147 | 4000 | 4.1636 | - | - | - | - | - |
| 0.1176 | 4100 | 5.4112 | - | - | - | - | - |
| 0.1205 | 4200 | 5.3505 | - | - | - | - | - |
| 0.1233 | 4300 | 3.8779 | - | - | - | - | - |
| 0.1262 | 4400 | 3.7439 | - | - | - | - | - |
| 0.1291 | 4500 | 3.3232 | - | - | - | - | - |
| 0.1319 | 4600 | 3.6257 | - | - | - | - | - |
| 0.1348 | 4700 | 3.8231 | - | - | - | - | - |
| 0.1377 | 4800 | 3.4048 | - | - | - | - | - |
| 0.1405 | 4900 | 3.0996 | - | - | - | - | - |
| 0.1434 | 5000 | 3.386 | - | - | - | - | - |
| 0.1463 | 5100 | 2.8902 | - | - | - | - | - |
| 0.1491 | 5200 | 3.2461 | - | - | - | - | - |
| 0.1520 | 5300 | 2.6888 | - | - | - | - | - |
| 0.1549 | 5400 | 3.2005 | - | - | - | - | - |
| 0.1577 | 5500 | 3.1291 | - | - | - | - | - |
| 0.1606 | 5600 | 2.993 | - | - | - | - | - |
| 0.1635 | 5700 | 3.3405 | - | - | - | - | - |
| 0.1664 | 5800 | 3.3929 | - | - | - | - | - |
| 0.1692 | 5900 | 4.0071 | - | - | - | - | - |
| 0.1721 | 6000 | 3.8775 | - | - | - | - | - |
| 0.1750 | 6100 | 4.0725 | - | - | - | - | - |
| 0.1778 | 6200 | 4.3434 | - | - | - | - | - |
| 0.1807 | 6300 | 4.0734 | - | - | - | - | - |
| 0.1836 | 6400 | 3.805 | - | - | - | - | - |
| 0.1864 | 6500 | 3.9273 | - | - | - | - | - |
| 0.1893 | 6600 | 3.9514 | - | - | - | - | - |
| 0.1922 | 6700 | 3.8316 | - | - | - | - | - |
| 0.1950 | 6800 | 3.2888 | - | - | - | - | - |
| 0.1979 | 6900 | 3.4367 | - | - | - | - | - |
| 0.2008 | 7000 | 3.0205 | - | - | - | - | - |
| 0.2036 | 7100 | 3.404 | - | - | - | - | - |
| 0.2065 | 7200 | 3.225 | - | - | - | - | - |
| 0.2094 | 7300 | 3.8446 | - | - | - | - | - |
| 0.2122 | 7400 | 3.2551 | - | - | - | - | - |
| 0.2151 | 7500 | 3.35 | - | - | - | - | - |
| 0.2180 | 7600 | 3.5524 | - | - | - | - | - |
| 0.2208 | 7700 | 3.7775 | - | - | - | - | - |
| 0.2237 | 7800 | 3.2797 | - | - | - | - | - |
| 0.2266 | 7900 | 3.96 | - | - | - | - | - |
| 0.2294 | 8000 | 3.7124 | - | - | - | - | - |
| 0.2323 | 8100 | 3.2713 | - | - | - | - | - |
| 0.2352 | 8200 | 3.8838 | - | - | - | - | - |
| 0.2381 | 8300 | 3.3932 | - | - | - | - | - |
| 0.2409 | 8400 | 3.3798 | - | - | - | - | - |
| 0.2438 | 8500 | 3.2386 | - | - | - | - | - |
| 0.2467 | 8600 | 3.1264 | - | - | - | - | - |
| 0.2495 | 8700 | 3.9248 | - | - | - | - | - |
| 0.2524 | 8800 | 3.5402 | - | - | - | - | - |
| 0.2553 | 8900 | 3.688 | - | - | - | - | - |
| 0.2581 | 9000 | 4.0903 | - | - | - | - | - |
| 0.2610 | 9100 | 4.4358 | - | - | - | - | - |
| 0.2639 | 9200 | 4.1334 | - | - | - | - | - |
| 0.2667 | 9300 | 3.4894 | - | - | - | - | - |
| 0.2696 | 9400 | 4.0032 | - | - | - | - | - |
| 0.2725 | 9500 | 4.1421 | - | - | - | - | - |
| 0.2753 | 9600 | 3.6995 | - | - | - | - | - |
| 0.2782 | 9700 | 3.8307 | - | - | - | - | - |
| 0.2811 | 9800 | 3.7448 | - | - | - | - | - |
| 0.2839 | 9900 | 3.6962 | - | - | - | - | - |
| 0.2868 | 10000 | 3.3733 | - | - | - | - | - |
| 0.2897 | 10100 | 3.4597 | - | - | - | - | - |
| 0.2925 | 10200 | 3.6834 | - | - | - | - | - |
| 0.2954 | 10300 | 3.7873 | - | - | - | - | - |
| 0.2983 | 10400 | 3.1388 | - | - | - | - | - |
| 0.3012 | 10500 | 3.9492 | - | - | - | - | - |
| 0.3040 | 10600 | 3.5991 | - | - | - | - | - |
| 0.3069 | 10700 | 4.2448 | - | - | - | - | - |
| 0.3098 | 10800 | 3.92 | - | - | - | - | - |
| 0.3126 | 10900 | 3.8442 | - | - | - | - | - |
| 0.3155 | 11000 | 4.3227 | - | - | - | - | - |
| 0.3184 | 11100 | 3.6447 | - | - | - | - | - |
| 0.3212 | 11200 | 3.8106 | - | - | - | - | - |
| 0.3241 | 11300 | 3.3499 | - | - | - | - | - |
| 0.3270 | 11400 | 3.8586 | - | - | - | - | - |
| 0.3298 | 11500 | 3.4284 | - | - | - | - | - |
| 0.3327 | 11600 | 3.2439 | - | - | - | - | - |
| 0.3356 | 11700 | 3.6645 | - | - | - | - | - |
| 0.3384 | 11800 | 3.9315 | - | - | - | - | - |
| 0.3413 | 11900 | 3.6439 | - | - | - | - | - |
| 0.3442 | 12000 | 3.6706 | - | - | - | - | - |
| 0.3470 | 12100 | 3.5084 | - | - | - | - | - |
| 0.3499 | 12200 | 3.9352 | - | - | - | - | - |
| 0.3528 | 12300 | 3.7615 | - | - | - | - | - |
| 0.3556 | 12400 | 3.7642 | - | - | - | - | - |
| 0.3585 | 12500 | 3.8085 | - | - | - | - | - |
| 0.3614 | 12600 | 3.411 | - | - | - | - | - |
| 0.3643 | 12700 | 3.8521 | - | - | - | - | - |
| 0.3671 | 12800 | 3.5473 | - | - | - | - | - |
| 0.3700 | 12900 | 3.5322 | - | - | - | - | - |
| 0.3729 | 13000 | 3.1496 | - | - | - | - | - |
| 0.3757 | 13100 | 3.5285 | - | - | - | - | - |
| 0.3786 | 13200 | 4.4428 | - | - | - | - | - |
| 0.3815 | 13300 | 3.4391 | - | - | - | - | - |
| 0.3843 | 13400 | 3.6457 | - | - | - | - | - |
| 0.3872 | 13500 | 3.2051 | - | - | - | - | - |
| 0.3901 | 13600 | 3.3738 | - | - | - | - | - |
| 0.3929 | 13700 | 3.5465 | - | - | - | - | - |
| 0.3958 | 13800 | 3.5853 | - | - | - | - | - |
| 0.3987 | 13900 | 3.297 | - | - | - | - | - |
| 0.4015 | 14000 | 3.3994 | - | - | - | - | - |
| 0.4044 | 14100 | 3.542 | - | - | - | - | - |
| 0.4073 | 14200 | 3.8516 | - | - | - | - | - |
| 0.4101 | 14300 | 3.6002 | - | - | - | - | - |
| 0.4130 | 14400 | 3.7251 | - | - | - | - | - |
| 0.4159 | 14500 | 3.4421 | - | - | - | - | - |
| 0.4187 | 14600 | 3.365 | - | - | - | - | - |
| 0.4216 | 14700 | 3.5327 | - | - | - | - | - |
| 0.4245 | 14800 | 3.1557 | - | - | - | - | - |
| 0.4274 | 14900 | 3.7096 | - | - | - | - | - |
| 0.4302 | 15000 | 3.9073 | - | - | - | - | - |
| 0.4331 | 15100 | 3.2662 | - | - | - | - | - |
| 0.4360 | 15200 | 3.3979 | - | - | - | - | - |
| 0.4388 | 15300 | 3.1515 | - | - | - | - | - |
| 0.4417 | 15400 | 3.247 | - | - | - | - | - |
| 0.4446 | 15500 | 3.3723 | - | - | - | - | - |
| 0.4474 | 15600 | 3.6837 | - | - | - | - | - |
| 0.4503 | 15700 | 3.4302 | - | - | - | - | - |
| 0.4532 | 15800 | 3.8231 | - | - | - | - | - |
| 0.4560 | 15900 | 3.1679 | - | - | - | - | - |
| 0.4589 | 16000 | 3.2766 | - | - | - | - | - |
| 0.4618 | 16100 | 3.3 | - | - | - | - | - |
| 0.4646 | 16200 | 3.557 | - | - | - | - | - |
| 0.4675 | 16300 | 3.5876 | - | - | - | - | - |
| 0.4704 | 16400 | 3.0928 | - | - | - | - | - |
| 0.4732 | 16500 | 2.9105 | - | - | - | - | - |
| 0.4761 | 16600 | 3.254 | - | - | - | - | - |
| 0.4790 | 16700 | 3.8005 | - | - | - | - | - |
| 0.4818 | 16800 | 3.1539 | - | - | - | - | - |
| 0.4847 | 16900 | 3.0174 | - | - | - | - | - |
| 0.4876 | 17000 | 3.4317 | - | - | - | - | - |
| 0.4904 | 17100 | 3.6292 | - | - | - | - | - |
| 0.4933 | 17200 | 3.7037 | - | - | - | - | - |
| 0.4962 | 17300 | 3.5144 | - | - | - | - | - |
| 0.4991 | 17400 | 3.7012 | - | - | - | - | - |
| 0.5019 | 17500 | 3.2587 | - | - | - | - | - |
| 0.5048 | 17600 | 3.1335 | - | - | - | - | - |
| 0.5077 | 17700 | 3.4027 | - | - | - | - | - |
| 0.5105 | 17800 | 3.6637 | - | - | - | - | - |
| 0.5134 | 17900 | 3.1682 | - | - | - | - | - |
| 0.5163 | 18000 | 3.2303 | - | - | - | - | - |
| 0.5191 | 18100 | 3.2155 | - | - | - | - | - |
| 0.5220 | 18200 | 3.431 | - | - | - | - | - |
| 0.5249 | 18300 | 3.1019 | - | - | - | - | - |
| 0.5277 | 18400 | 3.5245 | - | - | - | - | - |
| 0.5306 | 18500 | 3.1072 | - | - | - | - | - |
| 0.5335 | 18600 | 2.9673 | - | - | - | - | - |
| 0.5363 | 18700 | 3.0401 | - | - | - | - | - |
| 0.5392 | 18800 | 3.0617 | - | - | - | - | - |
| 0.5421 | 18900 | 3.6658 | - | - | - | - | - |
| 0.5449 | 19000 | 3.5137 | - | - | - | - | - |
| 0.5478 | 19100 | 3.5897 | - | - | - | - | - |
| 0.5507 | 19200 | 2.8309 | - | - | - | - | - |
| 0.5535 | 19300 | 3.7047 | - | - | - | - | - |
| 0.5564 | 19400 | 3.3343 | - | - | - | - | - |
| 0.5593 | 19500 | 3.3689 | - | - | - | - | - |
| 0.5622 | 19600 | 3.1783 | - | - | - | - | - |
| 0.5650 | 19700 | 3.6135 | - | - | - | - | - |
| 0.5679 | 19800 | 3.5106 | - | - | - | - | - |
| 0.5708 | 19900 | 3.8416 | - | - | - | - | - |
| 0.5736 | 20000 | 3.1559 | - | - | - | - | - |
| 0.5765 | 20100 | 3.2931 | - | - | - | - | - |
| 0.5794 | 20200 | 3.2411 | - | - | - | - | - |
| 0.5822 | 20300 | 3.5898 | - | - | - | - | - |
| 0.5851 | 20400 | 3.2916 | - | - | - | - | - |
| 0.5880 | 20500 | 3.619 | - | - | - | - | - |
| 0.5908 | 20600 | 3.8023 | - | - | - | - | - |
| 0.5937 | 20700 | 3.1023 | - | - | - | - | - |
| 0.5966 | 20800 | 3.2682 | - | - | - | - | - |
| 0.5994 | 20900 | 2.9783 | - | - | - | - | - |
| 0.6023 | 21000 | 3.1373 | - | - | - | - | - |
| 0.6052 | 21100 | 3.5358 | - | - | - | - | - |
| 0.6080 | 21200 | 3.2374 | - | - | - | - | - |
| 0.6109 | 21300 | 3.6793 | - | - | - | - | - |
| 0.6138 | 21400 | 3.388 | - | - | - | - | - |
| 0.6166 | 21500 | 3.1295 | - | - | - | - | - |
| 0.6195 | 21600 | 3.7971 | - | - | - | - | - |
| 0.6224 | 21700 | 3.4638 | - | - | - | - | - |
| 0.6253 | 21800 | 3.1254 | - | - | - | - | - |
| 0.6281 | 21900 | 3.705 | - | - | - | - | - |
| 0.6310 | 22000 | 2.9319 | - | - | - | - | - |
| 0.6339 | 22100 | 3.6908 | - | - | - | - | - |
| 0.6367 | 22200 | 3.3938 | - | - | - | - | - |
| 0.6396 | 22300 | 3.389 | - | - | - | - | - |
| 0.6425 | 22400 | 2.9946 | - | - | - | - | - |
| 0.6453 | 22500 | 3.9109 | - | - | - | - | - |
| 0.6482 | 22600 | 3.4698 | - | - | - | - | - |
| 0.6511 | 22700 | 3.1229 | - | - | - | - | - |
| 0.6539 | 22800 | 3.3769 | - | - | - | - | - |
| 0.6568 | 22900 | 3.1849 | - | - | - | - | - |
| 0.6597 | 23000 | 3.4464 | - | - | - | - | - |
| 0.6625 | 23100 | 2.9192 | - | - | - | - | - |
| 0.6654 | 23200 | 3.0796 | - | - | - | - | - |
| 0.6683 | 23300 | 3.4603 | - | - | - | - | - |
| 0.6711 | 23400 | 3.6775 | - | - | - | - | - |
| 0.6740 | 23500 | 3.5132 | - | - | - | - | - |
| 0.6769 | 23600 | 3.7764 | - | - | - | - | - |
| 0.6797 | 23700 | 3.0643 | - | - | - | - | - |
| 0.6826 | 23800 | 3.1545 | - | - | - | - | - |
| 0.6855 | 23900 | 2.997 | - | - | - | - | - |
| 0.6883 | 24000 | 3.1385 | - | - | - | - | - |
| 0.6912 | 24100 | 3.3879 | - | - | - | - | - |
| 0.6941 | 24200 | 3.5442 | - | - | - | - | - |
| 0.6970 | 24300 | 3.3687 | - | - | - | - | - |
| 0.6998 | 24400 | 3.4195 | - | - | - | - | - |
| 0.7027 | 24500 | 3.4057 | - | - | - | - | - |
| 0.7056 | 24600 | 3.2503 | - | - | - | - | - |
| 0.7084 | 24700 | 3.3703 | - | - | - | - | - |
| 0.7113 | 24800 | 3.0839 | - | - | - | - | - |
| 0.7142 | 24900 | 3.11 | - | - | - | - | - |
| 0.7170 | 25000 | 3.1105 | - | - | - | - | - |
| 0.7199 | 25100 | 2.8735 | - | - | - | - | - |
| 0.7228 | 25200 | 3.0287 | - | - | - | - | - |
| 0.7256 | 25300 | 3.2992 | - | - | - | - | - |
| 0.7285 | 25400 | 3.2015 | - | - | - | - | - |
| 0.7314 | 25500 | 3.3135 | - | - | - | - | - |
| 0.7342 | 25600 | 3.1618 | - | - | - | - | - |
| 0.7371 | 25700 | 3.5939 | - | - | - | - | - |
| 0.7400 | 25800 | 2.9016 | - | - | - | - | - |
| 0.7428 | 25900 | 3.2528 | - | - | - | - | - |
| 0.7457 | 26000 | 3.5005 | - | - | - | - | - |
| 0.7486 | 26100 | 3.2494 | - | - | - | - | - |
| 0.7514 | 26200 | 2.618 | - | - | - | - | - |
| 0.7543 | 26300 | 4.3413 | - | - | - | - | - |
| 0.7572 | 26400 | 4.0215 | - | - | - | - | - |
| 0.7601 | 26500 | 3.6406 | - | - | - | - | - |
| 0.7629 | 26600 | 3.6815 | - | - | - | - | - |
| 0.7658 | 26700 | 3.6911 | - | - | - | - | - |
| 0.7687 | 26800 | 3.3901 | - | - | - | - | - |
| 0.7715 | 26900 | 3.7262 | - | - | - | - | - |
| 0.7744 | 27000 | 3.3099 | - | - | - | - | - |
| 0.7773 | 27100 | 3.2131 | - | - | - | - | - |
| 0.7801 | 27200 | 3.1818 | - | - | - | - | - |
| 0.7830 | 27300 | 3.3306 | - | - | - | - | - |
| 0.7859 | 27400 | 3.4347 | - | - | - | - | - |
| 0.7887 | 27500 | 3.1169 | - | - | - | - | - |
| 0.7916 | 27600 | 3.2788 | - | - | - | - | - |
| 0.7945 | 27700 | 3.3876 | - | - | - | - | - |
| 0.7973 | 27800 | 3.0329 | - | - | - | - | - |
| 0.8002 | 27900 | 2.9935 | - | - | - | - | - |
| 0.8031 | 28000 | 3.0313 | - | - | - | - | - |
| 0.8059 | 28100 | 3.0293 | - | - | - | - | - |
| 0.8088 | 28200 | 3.0225 | - | - | - | - | - |
| 0.8117 | 28300 | 2.9378 | - | - | - | - | - |
| 0.8145 | 28400 | 2.8588 | - | - | - | - | - |
| 0.8174 | 28500 | 3.0936 | - | - | - | - | - |
| 0.8203 | 28600 | 2.9192 | - | - | - | - | - |
| 0.8232 | 28700 | 3.0259 | - | - | - | - | - |
| 0.8260 | 28800 | 2.76 | - | - | - | - | - |
| 0.8289 | 28900 | 3.0673 | - | - | - | - | - |
| 0.8318 | 29000 | 2.9333 | - | - | - | - | - |
| 0.8346 | 29100 | 2.9847 | - | - | - | - | - |
| 0.8375 | 29200 | 2.9882 | - | - | - | - | - |
| 0.8404 | 29300 | 2.9578 | - | - | - | - | - |
| 0.8432 | 29400 | 2.8535 | - | - | - | - | - |
| 0.8461 | 29500 | 3.012 | - | - | - | - | - |
| 0.8490 | 29600 | 2.6693 | - | - | - | - | - |
| 0.8518 | 29700 | 2.9026 | - | - | - | - | - |
| 0.8547 | 29800 | 2.7965 | - | - | - | - | - |
| 0.8576 | 29900 | 2.8402 | - | - | - | - | - |
| 0.8604 | 30000 | 2.6286 | - | - | - | - | - |
| 0.8633 | 30100 | 2.6588 | - | - | - | - | - |
| 0.8662 | 30200 | 2.6185 | - | - | - | - | - |
| 0.8690 | 30300 | 2.785 | - | - | - | - | - |
| 0.8719 | 30400 | 2.7637 | - | - | - | - | - |
| 0.8748 | 30500 | 2.8271 | - | - | - | - | - |
| 0.8776 | 30600 | 2.6788 | - | - | - | - | - |
| 0.8805 | 30700 | 2.5934 | - | - | - | - | - |
| 0.8834 | 30800 | 2.7782 | - | - | - | - | - |
| 0.8863 | 30900 | 2.7925 | - | - | - | - | - |
| 0.8891 | 31000 | 2.6091 | - | - | - | - | - |
| 0.8920 | 31100 | 2.7123 | - | - | - | - | - |
| 0.8949 | 31200 | 2.6067 | - | - | - | - | - |
| 0.8977 | 31300 | 2.65 | - | - | - | - | - |
| 0.9006 | 31400 | 2.7695 | - | - | - | - | - |
| 0.9035 | 31500 | 2.7075 | - | - | - | - | - |
| 0.9063 | 31600 | 2.5539 | - | - | - | - | - |
| 0.9092 | 31700 | 2.5283 | - | - | - | - | - |
| 0.9121 | 31800 | 2.7156 | - | - | - | - | - |
| 0.9149 | 31900 | 2.4318 | - | - | - | - | - |
| 0.9178 | 32000 | 2.7335 | - | - | - | - | - |
| 0.9207 | 32100 | 2.4435 | - | - | - | - | - |
| 0.9235 | 32200 | 2.6529 | - | - | - | - | - |
| 0.9264 | 32300 | 2.568 | - | - | - | - | - |
| 0.9293 | 32400 | 2.5639 | - | - | - | - | - |
| 0.9321 | 32500 | 2.6727 | - | - | - | - | - |
| 0.9350 | 32600 | 2.5063 | - | - | - | - | - |
| 0.9379 | 32700 | 2.5447 | - | - | - | - | - |
| 0.9407 | 32800 | 2.5767 | - | - | - | - | - |
| 0.9436 | 32900 | 2.5155 | - | - | - | - | - |
| 0.9465 | 33000 | 2.4016 | - | - | - | - | - |
| 0.9493 | 33100 | 2.7624 | - | - | - | - | - |
| 0.9522 | 33200 | 2.5887 | - | - | - | - | - |
| 0.9551 | 33300 | 2.5945 | - | - | - | - | - |
| 0.9580 | 33400 | 2.4295 | - | - | - | - | - |
| 0.9608 | 33500 | 2.6082 | - | - | - | - | - |
| 0.9637 | 33600 | 2.5034 | - | - | - | - | - |
| 0.9666 | 33700 | 2.5149 | - | - | - | - | - |
| 0.9694 | 33800 | 2.5311 | - | - | - | - | - |
| 0.9723 | 33900 | 2.6413 | - | - | - | - | - |
| 0.9752 | 34000 | 2.6304 | - | - | - | - | - |
| 0.9780 | 34100 | 2.5159 | - | - | - | - | - |
| 0.9809 | 34200 | 2.701 | - | - | - | - | - |
| 0.9838 | 34300 | 2.3928 | - | - | - | - | - |
| 0.9866 | 34400 | 2.5428 | - | - | - | - | - |
| 0.9895 | 34500 | 2.4652 | - | - | - | - | - |
| 0.9924 | 34600 | 2.7281 | - | - | - | - | - |
| 0.9952 | 34700 | 2.4693 | - | - | - | - | - |
| 0.9981 | 34800 | 2.4129 | - | - | - | - | - |
| 1.0 | 34866 | - | 0.6968 | 0.7001 | 0.7036 | 0.6849 | 0.7052 |
</details>
### Framework Versions
- Python: 3.11.9
- Sentence Transformers: 3.0.1
- Transformers: 4.40.1
- PyTorch: 2.3.0+cu121
- Accelerate: 0.29.3
- Datasets: 2.19.0
- 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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