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swahili-gte-base-en-v1.5-nli-matryoshka

Sentence Similarity
sentence-transformers
Safetensors
new
feature-extraction
Generated from Trainer
dataset_size:557850
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
custom_code
Eval Results
text-embeddings-inference
Inference Endpoints
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---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:557850
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: Alibaba-NLP/gte-base-en-v1.5
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: Mwanamume aliyepangwa vizuri anasimama kwa mguu mmoja karibu na
pwani safi ya bahari.
sentences:
- mtu anacheka wakati wa kufua nguo
- Mwanamume fulani yuko nje karibu na ufuo wa bahari.
- Mwanamume fulani ameketi kwenye sofa yake.
- source_sentence: Mwanamume mwenye ngozi nyeusi akivuta sigareti karibu na chombo
cha taka cha kijani.
sentences:
- Karibu na chombo cha taka mwanamume huyo alisimama na kuvuta sigareti
- Kitanda ni chafu.
- Alipokuwa kwenye dimbwi la kuogelea mvulana huyo mwenye ugonjwa wa albino alijihadhari
na jua kupita kiasi
- source_sentence: Mwanamume kijana mwenye nywele nyekundu anaketi ukutani akisoma
gazeti huku mwanamke na msichana mchanga wakipita.
sentences:
- Mwanamume aliyevalia shati la bluu amegonga ukuta kando ya barabara na gari la
bluu na gari nyekundu lenye maji nyuma.
- Mwanamume mchanga anatazama gazeti huku wanawake wawili wakipita karibu naye.
- Mwanamume huyo mchanga analala huku Mama akimwongoza binti yake kwenye bustani.
- source_sentence: Wasichana wako nje.
sentences:
- Wasichana wawili wakisafiri kwenye sehemu ya kusisimua.
- Kuna watu watatu wakiongoza gari linaloweza kugeuzwa-geuzwa wakipita watu wengine.
- Wasichana watatu wamesimama pamoja katika chumba, mmoja anasikiliza, mwingine
anaandika ukutani na wa tatu anaongea nao.
- source_sentence: Mwanamume aliyevalia koti la bluu la kuzuia upepo, amelala uso
chini kwenye benchi ya bustani, akiwa na chupa ya pombe iliyofungwa kwenye mojawapo
ya miguu ya benchi.
sentences:
- Mwanamume amelala uso chini kwenye benchi ya bustani.
- Mwanamke anaunganisha uzi katika mipira kando ya rundo la mipira
- Mwanamume fulani anacheza dansi kwenye klabu hiyo akifungua chupa.
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on Alibaba-NLP/gte-base-en-v1.5
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 768
type: sts-test-768
metrics:
- type: pearson_cosine
value: 0.7043347377864616
name: Pearson Cosine
- type: spearman_cosine
value: 0.6964343322647693
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6909108013214409
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6918757829517036
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6929234868177542
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6937500609344119
name: Spearman Euclidean
- type: pearson_dot
value: 0.70124411699517
name: Pearson Dot
- type: spearman_dot
value: 0.6918131755587139
name: Spearman Dot
- type: pearson_max
value: 0.7043347377864616
name: Pearson Max
- type: spearman_max
value: 0.6964343322647693
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 512
type: sts-test-512
metrics:
- type: pearson_cosine
value: 0.7024370656682521
name: Pearson Cosine
- type: spearman_cosine
value: 0.6960997397306026
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6937121372484026
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6942680507505805
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6958879339072266
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6965067811247516
name: Spearman Euclidean
- type: pearson_dot
value: 0.6739585793600888
name: Pearson Dot
- type: spearman_dot
value: 0.6635969331239819
name: Spearman Dot
- type: pearson_max
value: 0.7024370656682521
name: Pearson Max
- type: spearman_max
value: 0.6965067811247516
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 256
type: sts-test-256
metrics:
- type: pearson_cosine
value: 0.6975572102129655
name: Pearson Cosine
- type: spearman_cosine
value: 0.6922084123611896
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7012769244476563
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7002000478097333
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7033203116396916
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7027884000644871
name: Spearman Euclidean
- type: pearson_dot
value: 0.6353839704898405
name: Pearson Dot
- type: spearman_dot
value: 0.6242173680909447
name: Spearman Dot
- type: pearson_max
value: 0.7033203116396916
name: Pearson Max
- type: spearman_max
value: 0.7027884000644871
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 128
type: sts-test-128
metrics:
- type: pearson_cosine
value: 0.6909605436368886
name: Pearson Cosine
- type: spearman_cosine
value: 0.6880114885304113
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7044693468919807
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7001174190718876
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7063530897910422
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7028721535481625
name: Spearman Euclidean
- type: pearson_dot
value: 0.5846530941942547
name: Pearson Dot
- type: spearman_dot
value: 0.5728728042034709
name: Spearman Dot
- type: pearson_max
value: 0.7063530897910422
name: Pearson Max
- type: spearman_max
value: 0.7028721535481625
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 64
type: sts-test-64
metrics:
- type: pearson_cosine
value: 0.680996097859508
name: Pearson Cosine
- type: spearman_cosine
value: 0.6803001320954455
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7053262249895214
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6987184531053297
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7061173611755747
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7003828247494553
name: Spearman Euclidean
- type: pearson_dot
value: 0.5177214664781289
name: Pearson Dot
- type: spearman_dot
value: 0.5019887605325859
name: Spearman Dot
- type: pearson_max
value: 0.7061173611755747
name: Pearson Max
- type: spearman_max
value: 0.7003828247494553
name: Spearman Max
---
# SentenceTransformer based on Alibaba-NLP/gte-base-en-v1.5
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5). 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:** [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) <!-- at revision 269b9aca14a582d83e31b8c76b2e85a266fc1d77 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/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: NewModel
(1): Pooling({'word_embedding_dimension': 768, '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/swahili-gte-base-en-v1.5-nli-matryoshka")
# Run inference
sentences = [
'Mwanamume aliyevalia koti la bluu la kuzuia upepo, amelala uso chini kwenye benchi ya bustani, akiwa na chupa ya pombe iliyofungwa kwenye mojawapo ya miguu ya benchi.',
'Mwanamume amelala uso chini kwenye benchi ya bustani.',
'Mwanamume fulani anacheza dansi kwenye klabu hiyo akifungua chupa.',
]
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
* 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 | 0.7043 |
| **spearman_cosine** | **0.6964** |
| pearson_manhattan | 0.6909 |
| spearman_manhattan | 0.6919 |
| pearson_euclidean | 0.6929 |
| spearman_euclidean | 0.6938 |
| pearson_dot | 0.7012 |
| spearman_dot | 0.6918 |
| pearson_max | 0.7043 |
| spearman_max | 0.6964 |
#### 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 | 0.7024 |
| **spearman_cosine** | **0.6961** |
| pearson_manhattan | 0.6937 |
| spearman_manhattan | 0.6943 |
| pearson_euclidean | 0.6959 |
| spearman_euclidean | 0.6965 |
| pearson_dot | 0.674 |
| spearman_dot | 0.6636 |
| pearson_max | 0.7024 |
| spearman_max | 0.6965 |
#### 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 | 0.6976 |
| **spearman_cosine** | **0.6922** |
| pearson_manhattan | 0.7013 |
| spearman_manhattan | 0.7002 |
| pearson_euclidean | 0.7033 |
| spearman_euclidean | 0.7028 |
| pearson_dot | 0.6354 |
| spearman_dot | 0.6242 |
| pearson_max | 0.7033 |
| spearman_max | 0.7028 |
#### 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 | 0.691 |
| **spearman_cosine** | **0.688** |
| pearson_manhattan | 0.7045 |
| spearman_manhattan | 0.7001 |
| pearson_euclidean | 0.7064 |
| spearman_euclidean | 0.7029 |
| pearson_dot | 0.5847 |
| spearman_dot | 0.5729 |
| pearson_max | 0.7064 |
| spearman_max | 0.7029 |
#### 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 | 0.681 |
| **spearman_cosine** | **0.6803** |
| pearson_manhattan | 0.7053 |
| spearman_manhattan | 0.6987 |
| pearson_euclidean | 0.7061 |
| spearman_euclidean | 0.7004 |
| pearson_dot | 0.5177 |
| spearman_dot | 0.502 |
| pearson_max | 0.7061 |
| spearman_max | 0.7004 |
<!--
## 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 Hyperparameters
#### Non-Default Hyperparameters
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: 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`: 8
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_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
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `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 | 13.2716 | - | - | - | - | - |
| 0.0057 | 200 | 9.83 | - | - | - | - | - |
| 0.0086 | 300 | 9.9047 | - | - | - | - | - |
| 0.0115 | 400 | 7.5137 | - | - | - | - | - |
| 0.0143 | 500 | 7.6419 | - | - | - | - | - |
| 0.0172 | 600 | 6.9603 | - | - | - | - | - |
| 0.0201 | 700 | 7.3009 | - | - | - | - | - |
| 0.0229 | 800 | 7.1397 | - | - | - | - | - |
| 0.0258 | 900 | 8.1352 | - | - | - | - | - |
| 0.0287 | 1000 | 7.5945 | - | - | - | - | - |
| 0.0315 | 1100 | 7.0476 | - | - | - | - | - |
| 0.0344 | 1200 | 5.3356 | - | - | - | - | - |
| 0.0373 | 1300 | 5.1529 | - | - | - | - | - |
| 0.0402 | 1400 | 4.9726 | - | - | - | - | - |
| 0.0430 | 1500 | 5.1683 | - | - | - | - | - |
| 0.0459 | 1600 | 4.7945 | - | - | - | - | - |
| 0.0488 | 1700 | 4.9624 | - | - | - | - | - |
| 0.0516 | 1800 | 4.4254 | - | - | - | - | - |
| 0.0545 | 1900 | 4.4379 | - | - | - | - | - |
| 0.0574 | 2000 | 4.0327 | - | - | - | - | - |
| 0.0602 | 2100 | 3.5138 | - | - | - | - | - |
| 0.0631 | 2200 | 4.5055 | - | - | - | - | - |
| 0.0660 | 2300 | 3.8966 | - | - | - | - | - |
| 0.0688 | 2400 | 4.4884 | - | - | - | - | - |
| 0.0717 | 2500 | 3.5825 | - | - | - | - | - |
| 0.0746 | 2600 | 4.0155 | - | - | - | - | - |
| 0.0774 | 2700 | 4.9842 | - | - | - | - | - |
| 0.0803 | 2800 | 4.7732 | - | - | - | - | - |
| 0.0832 | 2900 | 4.5095 | - | - | - | - | - |
| 0.0860 | 3000 | 4.2526 | - | - | - | - | - |
| 0.0889 | 3100 | 4.033 | - | - | - | - | - |
| 0.0918 | 3200 | 4.0052 | - | - | - | - | - |
| 0.0946 | 3300 | 3.197 | - | - | - | - | - |
| 0.0975 | 3400 | 3.3423 | - | - | - | - | - |
| 0.1004 | 3500 | 2.9528 | - | - | - | - | - |
| 0.1033 | 3600 | 3.9315 | - | - | - | - | - |
| 0.1061 | 3700 | 3.7733 | - | - | - | - | - |
| 0.1090 | 3800 | 3.5153 | - | - | - | - | - |
| 0.1119 | 3900 | 4.1326 | - | - | - | - | - |
| 0.1147 | 4000 | 5.2179 | - | - | - | - | - |
| 0.1176 | 4100 | 6.4314 | - | - | - | - | - |
| 0.1205 | 4200 | 6.3485 | - | - | - | - | - |
| 0.1233 | 4300 | 4.7771 | - | - | - | - | - |
| 0.1262 | 4400 | 4.9055 | - | - | - | - | - |
| 0.1291 | 4500 | 3.9025 | - | - | - | - | - |
| 0.1319 | 4600 | 4.4638 | - | - | - | - | - |
| 0.1348 | 4700 | 5.0049 | - | - | - | - | - |
| 0.1377 | 4800 | 4.3124 | - | - | - | - | - |
| 0.1405 | 4900 | 4.0027 | - | - | - | - | - |
| 0.1434 | 5000 | 4.3173 | - | - | - | - | - |
| 0.1463 | 5100 | 3.6629 | - | - | - | - | - |
| 0.1491 | 5200 | 4.2759 | - | - | - | - | - |
| 0.1520 | 5300 | 3.4621 | - | - | - | - | - |
| 0.1549 | 5400 | 3.9251 | - | - | - | - | - |
| 0.1577 | 5500 | 4.2294 | - | - | - | - | - |
| 0.1606 | 5600 | 3.6244 | - | - | - | - | - |
| 0.1635 | 5700 | 4.283 | - | - | - | - | - |
| 0.1664 | 5800 | 4.4665 | - | - | - | - | - |
| 0.1692 | 5900 | 4.956 | - | - | - | - | - |
| 0.1721 | 6000 | 4.795 | - | - | - | - | - |
| 0.1750 | 6100 | 4.998 | - | - | - | - | - |
| 0.1778 | 6200 | 5.3316 | - | - | - | - | - |
| 0.1807 | 6300 | 5.2247 | - | - | - | - | - |
| 0.1836 | 6400 | 4.6554 | - | - | - | - | - |
| 0.1864 | 6500 | 5.2474 | - | - | - | - | - |
| 0.1893 | 6600 | 5.1168 | - | - | - | - | - |
| 0.1922 | 6700 | 5.1372 | - | - | - | - | - |
| 0.1950 | 6800 | 4.1564 | - | - | - | - | - |
| 0.1979 | 6900 | 4.6997 | - | - | - | - | - |
| 0.2008 | 7000 | 4.1854 | - | - | - | - | - |
| 0.2036 | 7100 | 4.4574 | - | - | - | - | - |
| 0.2065 | 7200 | 4.1859 | - | - | - | - | - |
| 0.2094 | 7300 | 4.8306 | - | - | - | - | - |
| 0.2122 | 7400 | 4.4487 | - | - | - | - | - |
| 0.2151 | 7500 | 4.4606 | - | - | - | - | - |
| 0.2180 | 7600 | 4.4222 | - | - | - | - | - |
| 0.2208 | 7700 | 4.7836 | - | - | - | - | - |
| 0.2237 | 7800 | 4.1475 | - | - | - | - | - |
| 0.2266 | 7900 | 5.1679 | - | - | - | - | - |
| 0.2294 | 8000 | 5.0106 | - | - | - | - | - |
| 0.2323 | 8100 | 4.1899 | - | - | - | - | - |
| 0.2352 | 8200 | 4.9873 | - | - | - | - | - |
| 0.2381 | 8300 | 4.3656 | - | - | - | - | - |
| 0.2409 | 8400 | 4.6117 | - | - | - | - | - |
| 0.2438 | 8500 | 4.1785 | - | - | - | - | - |
| 0.2467 | 8600 | 3.7809 | - | - | - | - | - |
| 0.2495 | 8700 | 4.9116 | - | - | - | - | - |
| 0.2524 | 8800 | 4.553 | - | - | - | - | - |
| 0.2553 | 8900 | 4.3178 | - | - | - | - | - |
| 0.2581 | 9000 | 5.6111 | - | - | - | - | - |
| 0.2610 | 9100 | 5.4219 | - | - | - | - | - |
| 0.2639 | 9200 | 5.5628 | - | - | - | - | - |
| 0.2667 | 9300 | 4.4221 | - | - | - | - | - |
| 0.2696 | 9400 | 4.7988 | - | - | - | - | - |
| 0.2725 | 9500 | 4.9361 | - | - | - | - | - |
| 0.2753 | 9600 | 4.7225 | - | - | - | - | - |
| 0.2782 | 9700 | 4.7258 | - | - | - | - | - |
| 0.2811 | 9800 | 4.7071 | - | - | - | - | - |
| 0.2839 | 9900 | 4.5519 | - | - | - | - | - |
| 0.2868 | 10000 | 4.5354 | - | - | - | - | - |
| 0.2897 | 10100 | 4.3893 | - | - | - | - | - |
| 0.2925 | 10200 | 4.7848 | - | - | - | - | - |
| 0.2954 | 10300 | 4.7195 | - | - | - | - | - |
| 0.2983 | 10400 | 4.0155 | - | - | - | - | - |
| 0.3012 | 10500 | 5.1602 | - | - | - | - | - |
| 0.3040 | 10600 | 4.6345 | - | - | - | - | - |
| 0.3069 | 10700 | 5.39 | - | - | - | - | - |
| 0.3098 | 10800 | 4.7974 | - | - | - | - | - |
| 0.3126 | 10900 | 4.9736 | - | - | - | - | - |
| 0.3155 | 11000 | 5.0949 | - | - | - | - | - |
| 0.3184 | 11100 | 4.6704 | - | - | - | - | - |
| 0.3212 | 11200 | 4.7001 | - | - | - | - | - |
| 0.3241 | 11300 | 4.2913 | - | - | - | - | - |
| 0.3270 | 11400 | 4.7536 | - | - | - | - | - |
| 0.3298 | 11500 | 4.8349 | - | - | - | - | - |
| 0.3327 | 11600 | 4.2567 | - | - | - | - | - |
| 0.3356 | 11700 | 4.6754 | - | - | - | - | - |
| 0.3384 | 11800 | 4.8534 | - | - | - | - | - |
| 0.3413 | 11900 | 4.7486 | - | - | - | - | - |
| 0.3442 | 12000 | 4.9194 | - | - | - | - | - |
| 0.3470 | 12100 | 4.4572 | - | - | - | - | - |
| 0.3499 | 12200 | 4.6173 | - | - | - | - | - |
| 0.3528 | 12300 | 5.1292 | - | - | - | - | - |
| 0.3556 | 12400 | 4.6138 | - | - | - | - | - |
| 0.3585 | 12500 | 4.6884 | - | - | - | - | - |
| 0.3614 | 12600 | 4.4245 | - | - | - | - | - |
| 0.3643 | 12700 | 4.7534 | - | - | - | - | - |
| 0.3671 | 12800 | 4.7027 | - | - | - | - | - |
| 0.3700 | 12900 | 4.5186 | - | - | - | - | - |
| 0.3729 | 13000 | 3.8917 | - | - | - | - | - |
| 0.3757 | 13100 | 4.507 | - | - | - | - | - |
| 0.3786 | 13200 | 5.4866 | - | - | - | - | - |
| 0.3815 | 13300 | 4.0424 | - | - | - | - | - |
| 0.3843 | 13400 | 4.4017 | - | - | - | - | - |
| 0.3872 | 13500 | 4.0016 | - | - | - | - | - |
| 0.3901 | 13600 | 4.0695 | - | - | - | - | - |
| 0.3929 | 13700 | 4.4957 | - | - | - | - | - |
| 0.3958 | 13800 | 4.4655 | - | - | - | - | - |
| 0.3987 | 13900 | 4.5717 | - | - | - | - | - |
| 0.4015 | 14000 | 4.134 | - | - | - | - | - |
| 0.4044 | 14100 | 4.2704 | - | - | - | - | - |
| 0.4073 | 14200 | 4.7712 | - | - | - | - | - |
| 0.4101 | 14300 | 4.3946 | - | - | - | - | - |
| 0.4130 | 14400 | 4.5848 | - | - | - | - | - |
| 0.4159 | 14500 | 4.4655 | - | - | - | - | - |
| 0.4187 | 14600 | 4.278 | - | - | - | - | - |
| 0.4216 | 14700 | 4.2877 | - | - | - | - | - |
| 0.4245 | 14800 | 3.9299 | - | - | - | - | - |
| 0.4274 | 14900 | 4.7078 | - | - | - | - | - |
| 0.4302 | 15000 | 4.8527 | - | - | - | - | - |
| 0.4331 | 15100 | 4.3476 | - | - | - | - | - |
| 0.4360 | 15200 | 4.2012 | - | - | - | - | - |
| 0.4388 | 15300 | 4.1766 | - | - | - | - | - |
| 0.4417 | 15400 | 3.9842 | - | - | - | - | - |
| 0.4446 | 15500 | 4.1244 | - | - | - | - | - |
| 0.4474 | 15600 | 4.7983 | - | - | - | - | - |
| 0.4503 | 15700 | 4.2341 | - | - | - | - | - |
| 0.4532 | 15800 | 4.9829 | - | - | - | - | - |
| 0.4560 | 15900 | 4.0221 | - | - | - | - | - |
| 0.4589 | 16000 | 4.1082 | - | - | - | - | - |
| 0.4618 | 16100 | 3.8922 | - | - | - | - | - |
| 0.4646 | 16200 | 4.5382 | - | - | - | - | - |
| 0.4675 | 16300 | 4.4428 | - | - | - | - | - |
| 0.4704 | 16400 | 3.9087 | - | - | - | - | - |
| 0.4732 | 16500 | 3.7465 | - | - | - | - | - |
| 0.4761 | 16600 | 4.149 | - | - | - | - | - |
| 0.4790 | 16700 | 4.5691 | - | - | - | - | - |
| 0.4818 | 16800 | 3.8776 | - | - | - | - | - |
| 0.4847 | 16900 | 3.7354 | - | - | - | - | - |
| 0.4876 | 17000 | 4.25 | - | - | - | - | - |
| 0.4904 | 17100 | 4.4119 | - | - | - | - | - |
| 0.4933 | 17200 | 4.2319 | - | - | - | - | - |
| 0.4962 | 17300 | 4.3736 | - | - | - | - | - |
| 0.4991 | 17400 | 4.5345 | - | - | - | - | - |
| 0.5019 | 17500 | 4.1824 | - | - | - | - | - |
| 0.5048 | 17600 | 4.0033 | - | - | - | - | - |
| 0.5077 | 17700 | 4.277 | - | - | - | - | - |
| 0.5105 | 17800 | 4.3553 | - | - | - | - | - |
| 0.5134 | 17900 | 3.9528 | - | - | - | - | - |
| 0.5163 | 18000 | 4.068 | - | - | - | - | - |
| 0.5191 | 18100 | 4.0464 | - | - | - | - | - |
| 0.5220 | 18200 | 4.1665 | - | - | - | - | - |
| 0.5249 | 18300 | 3.7445 | - | - | - | - | - |
| 0.5277 | 18400 | 4.2248 | - | - | - | - | - |
| 0.5306 | 18500 | 3.9295 | - | - | - | - | - |
| 0.5335 | 18600 | 3.546 | - | - | - | - | - |
| 0.5363 | 18700 | 3.7463 | - | - | - | - | - |
| 0.5392 | 18800 | 3.9798 | - | - | - | - | - |
| 0.5421 | 18900 | 4.4773 | - | - | - | - | - |
| 0.5449 | 19000 | 4.3534 | - | - | - | - | - |
| 0.5478 | 19100 | 4.2347 | - | - | - | - | - |
| 0.5507 | 19200 | 3.8113 | - | - | - | - | - |
| 0.5535 | 19300 | 4.4689 | - | - | - | - | - |
| 0.5564 | 19400 | 4.2188 | - | - | - | - | - |
| 0.5593 | 19500 | 4.1266 | - | - | - | - | - |
| 0.5622 | 19600 | 3.9222 | - | - | - | - | - |
| 0.5650 | 19700 | 4.38 | - | - | - | - | - |
| 0.5679 | 19800 | 4.4557 | - | - | - | - | - |
| 0.5708 | 19900 | 4.7566 | - | - | - | - | - |
| 0.5736 | 20000 | 3.8922 | - | - | - | - | - |
| 0.5765 | 20100 | 4.0263 | - | - | - | - | - |
| 0.5794 | 20200 | 3.9258 | - | - | - | - | - |
| 0.5822 | 20300 | 4.3767 | - | - | - | - | - |
| 0.5851 | 20400 | 4.1211 | - | - | - | - | - |
| 0.5880 | 20500 | 4.3083 | - | - | - | - | - |
| 0.5908 | 20600 | 4.4544 | - | - | - | - | - |
| 0.5937 | 20700 | 4.0118 | - | - | - | - | - |
| 0.5966 | 20800 | 3.9136 | - | - | - | - | - |
| 0.5994 | 20900 | 3.8614 | - | - | - | - | - |
| 0.6023 | 21000 | 3.8057 | - | - | - | - | - |
| 0.6052 | 21100 | 4.4934 | - | - | - | - | - |
| 0.6080 | 21200 | 3.9206 | - | - | - | - | - |
| 0.6109 | 21300 | 4.43 | - | - | - | - | - |
| 0.6138 | 21400 | 4.0576 | - | - | - | - | - |
| 0.6166 | 21500 | 3.9019 | - | - | - | - | - |
| 0.6195 | 21600 | 4.4216 | - | - | - | - | - |
| 0.6224 | 21700 | 4.0959 | - | - | - | - | - |
| 0.6253 | 21800 | 3.8756 | - | - | - | - | - |
| 0.6281 | 21900 | 4.7791 | - | - | - | - | - |
| 0.6310 | 22000 | 3.6284 | - | - | - | - | - |
| 0.6339 | 22100 | 4.5534 | - | - | - | - | - |
| 0.6367 | 22200 | 4.18 | - | - | - | - | - |
| 0.6396 | 22300 | 4.3002 | - | - | - | - | - |
| 0.6425 | 22400 | 3.7162 | - | - | - | - | - |
| 0.6453 | 22500 | 4.8495 | - | - | - | - | - |
| 0.6482 | 22600 | 4.2966 | - | - | - | - | - |
| 0.6511 | 22700 | 3.7718 | - | - | - | - | - |
| 0.6539 | 22800 | 4.2257 | - | - | - | - | - |
| 0.6568 | 22900 | 3.9821 | - | - | - | - | - |
| 0.6597 | 23000 | 4.0853 | - | - | - | - | - |
| 0.6625 | 23100 | 3.6124 | - | - | - | - | - |
| 0.6654 | 23200 | 3.732 | - | - | - | - | - |
| 0.6683 | 23300 | 4.3821 | - | - | - | - | - |
| 0.6711 | 23400 | 4.229 | - | - | - | - | - |
| 0.6740 | 23500 | 4.2589 | - | - | - | - | - |
| 0.6769 | 23600 | 4.4975 | - | - | - | - | - |
| 0.6797 | 23700 | 3.8062 | - | - | - | - | - |
| 0.6826 | 23800 | 3.6924 | - | - | - | - | - |
| 0.6855 | 23900 | 3.7736 | - | - | - | - | - |
| 0.6883 | 24000 | 3.7815 | - | - | - | - | - |
| 0.6912 | 24100 | 4.1192 | - | - | - | - | - |
| 0.6941 | 24200 | 4.2336 | - | - | - | - | - |
| 0.6970 | 24300 | 4.1145 | - | - | - | - | - |
| 0.6998 | 24400 | 4.0681 | - | - | - | - | - |
| 0.7027 | 24500 | 4.0492 | - | - | - | - | - |
| 0.7056 | 24600 | 3.7831 | - | - | - | - | - |
| 0.7084 | 24700 | 4.2445 | - | - | - | - | - |
| 0.7113 | 24800 | 3.9308 | - | - | - | - | - |
| 0.7142 | 24900 | 3.8705 | - | - | - | - | - |
| 0.7170 | 25000 | 3.6998 | - | - | - | - | - |
| 0.7199 | 25100 | 3.4736 | - | - | - | - | - |
| 0.7228 | 25200 | 3.9971 | - | - | - | - | - |
| 0.7256 | 25300 | 3.8292 | - | - | - | - | - |
| 0.7285 | 25400 | 3.8499 | - | - | - | - | - |
| 0.7314 | 25500 | 3.8732 | - | - | - | - | - |
| 0.7342 | 25600 | 3.9409 | - | - | - | - | - |
| 0.7371 | 25700 | 4.4416 | - | - | - | - | - |
| 0.7400 | 25800 | 3.663 | - | - | - | - | - |
| 0.7428 | 25900 | 3.9786 | - | - | - | - | - |
| 0.7457 | 26000 | 4.1781 | - | - | - | - | - |
| 0.7486 | 26100 | 3.692 | - | - | - | - | - |
| 0.7514 | 26200 | 3.2601 | - | - | - | - | - |
| 0.7543 | 26300 | 7.1759 | - | - | - | - | - |
| 0.7572 | 26400 | 7.0459 | - | - | - | - | - |
| 0.7601 | 26500 | 6.1797 | - | - | - | - | - |
| 0.7629 | 26600 | 6.2055 | - | - | - | - | - |
| 0.7658 | 26700 | 6.1403 | - | - | - | - | - |
| 0.7687 | 26800 | 5.703 | - | - | - | - | - |
| 0.7715 | 26900 | 6.1283 | - | - | - | - | - |
| 0.7744 | 27000 | 5.71 | - | - | - | - | - |
| 0.7773 | 27100 | 5.3105 | - | - | - | - | - |
| 0.7801 | 27200 | 5.4202 | - | - | - | - | - |
| 0.7830 | 27300 | 5.2964 | - | - | - | - | - |
| 0.7859 | 27400 | 5.4852 | - | - | - | - | - |
| 0.7887 | 27500 | 5.241 | - | - | - | - | - |
| 0.7916 | 27600 | 5.4322 | - | - | - | - | - |
| 0.7945 | 27700 | 5.6285 | - | - | - | - | - |
| 0.7973 | 27800 | 5.0215 | - | - | - | - | - |
| 0.8002 | 27900 | 5.2433 | - | - | - | - | - |
| 0.8031 | 28000 | 4.9617 | - | - | - | - | - |
| 0.8059 | 28100 | 4.9479 | - | - | - | - | - |
| 0.8088 | 28200 | 4.9077 | - | - | - | - | - |
| 0.8117 | 28300 | 4.853 | - | - | - | - | - |
| 0.8145 | 28400 | 4.6727 | - | - | - | - | - |
| 0.8174 | 28500 | 4.9987 | - | - | - | - | - |
| 0.8203 | 28600 | 4.8405 | - | - | - | - | - |
| 0.8232 | 28700 | 4.9627 | - | - | - | - | - |
| 0.8260 | 28800 | 4.5608 | - | - | - | - | - |
| 0.8289 | 28900 | 5.0802 | - | - | - | - | - |
| 0.8318 | 29000 | 4.9069 | - | - | - | - | - |
| 0.8346 | 29100 | 4.8605 | - | - | - | - | - |
| 0.8375 | 29200 | 4.6424 | - | - | - | - | - |
| 0.8404 | 29300 | 4.7813 | - | - | - | - | - |
| 0.8432 | 29400 | 4.5925 | - | - | - | - | - |
| 0.8461 | 29500 | 4.7081 | - | - | - | - | - |
| 0.8490 | 29600 | 4.4319 | - | - | - | - | - |
| 0.8518 | 29700 | 4.7291 | - | - | - | - | - |
| 0.8547 | 29800 | 4.749 | - | - | - | - | - |
| 0.8576 | 29900 | 4.6148 | - | - | - | - | - |
| 0.8604 | 30000 | 4.2549 | - | - | - | - | - |
| 0.8633 | 30100 | 4.3415 | - | - | - | - | - |
| 0.8662 | 30200 | 4.1999 | - | - | - | - | - |
| 0.8690 | 30300 | 4.4298 | - | - | - | - | - |
| 0.8719 | 30400 | 4.3612 | - | - | - | - | - |
| 0.8748 | 30500 | 4.4834 | - | - | - | - | - |
| 0.8776 | 30600 | 4.4774 | - | - | - | - | - |
| 0.8805 | 30700 | 4.2524 | - | - | - | - | - |
| 0.8834 | 30800 | 4.5562 | - | - | - | - | - |
| 0.8863 | 30900 | 4.5261 | - | - | - | - | - |
| 0.8891 | 31000 | 4.0262 | - | - | - | - | - |
| 0.8920 | 31100 | 4.1109 | - | - | - | - | - |
| 0.8949 | 31200 | 4.1955 | - | - | - | - | - |
| 0.8977 | 31300 | 4.3169 | - | - | - | - | - |
| 0.9006 | 31400 | 4.5862 | - | - | - | - | - |
| 0.9035 | 31500 | 4.5503 | - | - | - | - | - |
| 0.9063 | 31600 | 4.2587 | - | - | - | - | - |
| 0.9092 | 31700 | 4.0028 | - | - | - | - | - |
| 0.9121 | 31800 | 4.3575 | - | - | - | - | - |
| 0.9149 | 31900 | 4.1033 | - | - | - | - | - |
| 0.9178 | 32000 | 4.2877 | - | - | - | - | - |
| 0.9207 | 32100 | 3.9537 | - | - | - | - | - |
| 0.9235 | 32200 | 4.107 | - | - | - | - | - |
| 0.9264 | 32300 | 4.3288 | - | - | - | - | - |
| 0.9293 | 32400 | 4.102 | - | - | - | - | - |
| 0.9321 | 32500 | 4.1751 | - | - | - | - | - |
| 0.9350 | 32600 | 3.7919 | - | - | - | - | - |
| 0.9379 | 32700 | 4.0939 | - | - | - | - | - |
| 0.9407 | 32800 | 4.1822 | - | - | - | - | - |
| 0.9436 | 32900 | 3.959 | - | - | - | - | - |
| 0.9465 | 33000 | 3.9173 | - | - | - | - | - |
| 0.9493 | 33100 | 4.3087 | - | - | - | - | - |
| 0.9522 | 33200 | 4.1239 | - | - | - | - | - |
| 0.9551 | 33300 | 4.1012 | - | - | - | - | - |
| 0.9580 | 33400 | 3.9988 | - | - | - | - | - |
| 0.9608 | 33500 | 4.1478 | - | - | - | - | - |
| 0.9637 | 33600 | 4.1669 | - | - | - | - | - |
| 0.9666 | 33700 | 4.0398 | - | - | - | - | - |
| 0.9694 | 33800 | 3.9814 | - | - | - | - | - |
| 0.9723 | 33900 | 4.3764 | - | - | - | - | - |
| 0.9752 | 34000 | 4.2847 | - | - | - | - | - |
| 0.9780 | 34100 | 3.9461 | - | - | - | - | - |
| 0.9809 | 34200 | 4.3377 | - | - | - | - | - |
| 0.9838 | 34300 | 3.8114 | - | - | - | - | - |
| 0.9866 | 34400 | 4.0827 | - | - | - | - | - |
| 0.9895 | 34500 | 4.0014 | - | - | - | - | - |
| 0.9924 | 34600 | 4.3964 | - | - | - | - | - |
| 0.9952 | 34700 | 3.9103 | - | - | - | - | - |
| 0.9981 | 34800 | 4.0363 | - | - | - | - | - |
| 1.0 | 34866 | - | 0.6880 | 0.6922 | 0.6961 | 0.6803 | 0.6964 |
</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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