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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: Geotrend/bert-base-sw-cased
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 Geotrend/bert-base-sw-cased
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 768
type: sts-test-768
metrics:
- type: pearson_cosine
value: 0.6868804546581948
name: Pearson Cosine
- type: spearman_cosine
value: 0.6801625382694466
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6719079171543956
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6653137984517007
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6734384393604611
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6665812962708187
name: Spearman Euclidean
- type: pearson_dot
value: 0.5540255947111082
name: Pearson Dot
- type: spearman_dot
value: 0.5399212934179993
name: Spearman Dot
- type: pearson_max
value: 0.6868804546581948
name: Pearson Max
- type: spearman_max
value: 0.6801625382694466
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 512
type: sts-test-512
metrics:
- type: pearson_cosine
value: 0.6827780698031986
name: Pearson Cosine
- type: spearman_cosine
value: 0.6770486364807735
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6729437410000495
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6664360018282044
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6738342605019458
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6666791464094138
name: Spearman Euclidean
- type: pearson_dot
value: 0.5296210420398023
name: Pearson Dot
- type: spearman_dot
value: 0.5173769714392553
name: Spearman Dot
- type: pearson_max
value: 0.6827780698031986
name: Pearson Max
- type: spearman_max
value: 0.6770486364807735
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 256
type: sts-test-256
metrics:
- type: pearson_cosine
value: 0.6758051721795716
name: Pearson Cosine
- type: spearman_cosine
value: 0.6701833115162764
name: Spearman Cosine
- type: pearson_manhattan
value: 0.671762500960023
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6643430423969034
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6730238156482042
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6649839339725255
name: Spearman Euclidean
- type: pearson_dot
value: 0.48923961423508167
name: Pearson Dot
- type: spearman_dot
value: 0.4783312389130331
name: Spearman Dot
- type: pearson_max
value: 0.6758051721795716
name: Pearson Max
- type: spearman_max
value: 0.6701833115162764
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 128
type: sts-test-128
metrics:
- type: pearson_cosine
value: 0.6700363607439113
name: Pearson Cosine
- type: spearman_cosine
value: 0.6637709194412489
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6692814840348797
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6594295578885248
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.671006713633375
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6600674238087292
name: Spearman Euclidean
- type: pearson_dot
value: 0.45094972472157246
name: Pearson Dot
- type: spearman_dot
value: 0.44023350072779777
name: Spearman Dot
- type: pearson_max
value: 0.671006713633375
name: Pearson Max
- type: spearman_max
value: 0.6637709194412489
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 64
type: sts-test-64
metrics:
- type: pearson_cosine
value: 0.6614685875750459
name: Pearson Cosine
- type: spearman_cosine
value: 0.6556282400518681
name: Spearman Cosine
- type: pearson_manhattan
value: 0.665261323713716
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6533415018004937
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6671725346980402
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6540012112658994
name: Spearman Euclidean
- type: pearson_dot
value: 0.38682442010639634
name: Pearson Dot
- type: spearman_dot
value: 0.37712136401470375
name: Spearman Dot
- type: pearson_max
value: 0.6671725346980402
name: Pearson Max
- type: spearman_max
value: 0.6556282400518681
name: Spearman Max
---
# SentenceTransformer based on Geotrend/bert-base-sw-cased
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Geotrend/bert-base-sw-cased](https://huggingface.co/Geotrend/bert-base-sw-cased). 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:** [Geotrend/bert-base-sw-cased](https://huggingface.co/Geotrend/bert-base-sw-cased) <!-- at revision 7d9ca957a81d2449cf1319af0b91f75f11642336 -->
- **Maximum Sequence Length:** 512 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': 512, 'do_lower_case': False}) with Transformer model: BertModel
(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("Mollel/swahili-bert-base-sw-cased-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 | 0.6869 |
| **spearman_cosine** | **0.6802** |
| pearson_manhattan | 0.6719 |
| spearman_manhattan | 0.6653 |
| pearson_euclidean | 0.6734 |
| spearman_euclidean | 0.6666 |
| pearson_dot | 0.554 |
| spearman_dot | 0.5399 |
| pearson_max | 0.6869 |
| spearman_max | 0.6802 |
#### 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 | 0.6828 |
| **spearman_cosine** | **0.677** |
| pearson_manhattan | 0.6729 |
| spearman_manhattan | 0.6664 |
| pearson_euclidean | 0.6738 |
| spearman_euclidean | 0.6667 |
| pearson_dot | 0.5296 |
| spearman_dot | 0.5174 |
| pearson_max | 0.6828 |
| spearman_max | 0.677 |
#### 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 | 0.6758 |
| **spearman_cosine** | **0.6702** |
| pearson_manhattan | 0.6718 |
| spearman_manhattan | 0.6643 |
| pearson_euclidean | 0.673 |
| spearman_euclidean | 0.665 |
| pearson_dot | 0.4892 |
| spearman_dot | 0.4783 |
| pearson_max | 0.6758 |
| spearman_max | 0.6702 |
#### 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 | 0.67 |
| **spearman_cosine** | **0.6638** |
| pearson_manhattan | 0.6693 |
| spearman_manhattan | 0.6594 |
| pearson_euclidean | 0.671 |
| spearman_euclidean | 0.6601 |
| pearson_dot | 0.4509 |
| spearman_dot | 0.4402 |
| pearson_max | 0.671 |
| spearman_max | 0.6638 |
#### 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 | 0.6615 |
| **spearman_cosine** | **0.6556** |
| pearson_manhattan | 0.6653 |
| spearman_manhattan | 0.6533 |
| pearson_euclidean | 0.6672 |
| spearman_euclidean | 0.654 |
| pearson_dot | 0.3868 |
| spearman_dot | 0.3771 |
| pearson_max | 0.6672 |
| spearman_max | 0.6556 |
<!--
## 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
- `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 |
|:------:|:-----:|:-------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|
| 0.0057 | 100 | 20.0932 | - | - | - | - | - |
| 0.0115 | 200 | 16.2641 | - | - | - | - | - |
| 0.0172 | 300 | 12.797 | - | - | - | - | - |
| 0.0229 | 400 | 12.1927 | - | - | - | - | - |
| 0.0287 | 500 | 11.0423 | - | - | - | - | - |
| 0.0344 | 600 | 9.676 | - | - | - | - | - |
| 0.0402 | 700 | 8.1545 | - | - | - | - | - |
| 0.0459 | 800 | 7.7822 | - | - | - | - | - |
| 0.0516 | 900 | 7.9352 | - | - | - | - | - |
| 0.0574 | 1000 | 7.9534 | - | - | - | - | - |
| 0.0631 | 1100 | 8.1006 | - | - | - | - | - |
| 0.0688 | 1200 | 7.4767 | - | - | - | - | - |
| 0.0746 | 1300 | 8.3747 | - | - | - | - | - |
| 0.0803 | 1400 | 7.7686 | - | - | - | - | - |
| 0.0860 | 1500 | 6.8076 | - | - | - | - | - |
| 0.0918 | 1600 | 6.9238 | - | - | - | - | - |
| 0.0975 | 1700 | 6.5503 | - | - | - | - | - |
| 0.1033 | 1800 | 6.74 | - | - | - | - | - |
| 0.1090 | 1900 | 7.7802 | - | - | - | - | - |
| 0.1147 | 2000 | 7.2594 | - | - | - | - | - |
| 0.1205 | 2100 | 7.091 | - | - | - | - | - |
| 0.1262 | 2200 | 6.8677 | - | - | - | - | - |
| 0.1319 | 2300 | 6.4249 | - | - | - | - | - |
| 0.1377 | 2400 | 6.1512 | - | - | - | - | - |
| 0.1434 | 2500 | 5.9714 | - | - | - | - | - |
| 0.1491 | 2600 | 5.4914 | - | - | - | - | - |
| 0.1549 | 2700 | 5.5825 | - | - | - | - | - |
| 0.1606 | 2800 | 5.9456 | - | - | - | - | - |
| 0.1664 | 2900 | 6.4012 | - | - | - | - | - |
| 0.1721 | 3000 | 7.1999 | - | - | - | - | - |
| 0.1778 | 3100 | 6.8254 | - | - | - | - | - |
| 0.1836 | 3200 | 6.541 | - | - | - | - | - |
| 0.1893 | 3300 | 6.5411 | - | - | - | - | - |
| 0.1950 | 3400 | 5.56 | - | - | - | - | - |
| 0.2008 | 3500 | 6.4692 | - | - | - | - | - |
| 0.2065 | 3600 | 5.9266 | - | - | - | - | - |
| 0.2122 | 3700 | 6.2055 | - | - | - | - | - |
| 0.2180 | 3800 | 6.0835 | - | - | - | - | - |
| 0.2237 | 3900 | 6.6112 | - | - | - | - | - |
| 0.2294 | 4000 | 6.3391 | - | - | - | - | - |
| 0.2352 | 4100 | 5.8379 | - | - | - | - | - |
| 0.2409 | 4200 | 5.8107 | - | - | - | - | - |
| 0.2467 | 4300 | 6.1473 | - | - | - | - | - |
| 0.2524 | 4400 | 6.2827 | - | - | - | - | - |
| 0.2581 | 4500 | 6.2299 | - | - | - | - | - |
| 0.2639 | 4600 | 6.1013 | - | - | - | - | - |
| 0.2696 | 4700 | 5.6491 | - | - | - | - | - |
| 0.2753 | 4800 | 5.8641 | - | - | - | - | - |
| 0.2811 | 4900 | 5.4278 | - | - | - | - | - |
| 0.2868 | 5000 | 5.7304 | - | - | - | - | - |
| 0.2925 | 5100 | 5.4652 | - | - | - | - | - |
| 0.2983 | 5200 | 5.9031 | - | - | - | - | - |
| 0.3040 | 5300 | 6.1014 | - | - | - | - | - |
| 0.3098 | 5400 | 5.9282 | - | - | - | - | - |
| 0.3155 | 5500 | 5.6618 | - | - | - | - | - |
| 0.3212 | 5600 | 5.3803 | - | - | - | - | - |
| 0.3270 | 5700 | 5.5759 | - | - | - | - | - |
| 0.3327 | 5800 | 5.6936 | - | - | - | - | - |
| 0.3384 | 5900 | 5.7249 | - | - | - | - | - |
| 0.3442 | 6000 | 5.5926 | - | - | - | - | - |
| 0.3499 | 6100 | 5.6329 | - | - | - | - | - |
| 0.3556 | 6200 | 5.7456 | - | - | - | - | - |
| 0.3614 | 6300 | 5.1638 | - | - | - | - | - |
| 0.3671 | 6400 | 5.3258 | - | - | - | - | - |
| 0.3729 | 6500 | 5.1216 | - | - | - | - | - |
| 0.3786 | 6600 | 5.7453 | - | - | - | - | - |
| 0.3843 | 6700 | 4.9906 | - | - | - | - | - |
| 0.3901 | 6800 | 5.1126 | - | - | - | - | - |
| 0.3958 | 6900 | 5.2389 | - | - | - | - | - |
| 0.4015 | 7000 | 5.1483 | - | - | - | - | - |
| 0.4073 | 7100 | 5.6072 | - | - | - | - | - |
| 0.4130 | 7200 | 5.2018 | - | - | - | - | - |
| 0.4187 | 7300 | 5.4083 | - | - | - | - | - |
| 0.4245 | 7400 | 5.1995 | - | - | - | - | - |
| 0.4302 | 7500 | 5.5787 | - | - | - | - | - |
| 0.4360 | 7600 | 4.9942 | - | - | - | - | - |
| 0.4417 | 7700 | 4.9196 | - | - | - | - | - |
| 0.4474 | 7800 | 5.3938 | - | - | - | - | - |
| 0.4532 | 7900 | 5.381 | - | - | - | - | - |
| 0.4589 | 8000 | 4.908 | - | - | - | - | - |
| 0.4646 | 8100 | 4.8871 | - | - | - | - | - |
| 0.4704 | 8200 | 5.2298 | - | - | - | - | - |
| 0.4761 | 8300 | 4.6157 | - | - | - | - | - |
| 0.4818 | 8400 | 5.0344 | - | - | - | - | - |
| 0.4876 | 8500 | 5.0713 | - | - | - | - | - |
| 0.4933 | 8600 | 5.1952 | - | - | - | - | - |
| 0.4991 | 8700 | 5.5352 | - | - | - | - | - |
| 0.5048 | 8800 | 5.1556 | - | - | - | - | - |
| 0.5105 | 8900 | 5.2318 | - | - | - | - | - |
| 0.5163 | 9000 | 4.7887 | - | - | - | - | - |
| 0.5220 | 9100 | 4.868 | - | - | - | - | - |
| 0.5277 | 9200 | 4.9544 | - | - | - | - | - |
| 0.5335 | 9300 | 4.816 | - | - | - | - | - |
| 0.5392 | 9400 | 4.8374 | - | - | - | - | - |
| 0.5449 | 9500 | 5.3242 | - | - | - | - | - |
| 0.5507 | 9600 | 4.9039 | - | - | - | - | - |
| 0.5564 | 9700 | 5.2907 | - | - | - | - | - |
| 0.5622 | 9800 | 5.4007 | - | - | - | - | - |
| 0.5679 | 9900 | 5.3016 | - | - | - | - | - |
| 0.5736 | 10000 | 5.3235 | - | - | - | - | - |
| 0.5794 | 10100 | 5.1566 | - | - | - | - | - |
| 0.5851 | 10200 | 5.1348 | - | - | - | - | - |
| 0.5908 | 10300 | 5.4583 | - | - | - | - | - |
| 0.5966 | 10400 | 4.9528 | - | - | - | - | - |
| 0.6023 | 10500 | 5.0073 | - | - | - | - | - |
| 0.6080 | 10600 | 5.0324 | - | - | - | - | - |
| 0.6138 | 10700 | 5.4107 | - | - | - | - | - |
| 0.6195 | 10800 | 5.3643 | - | - | - | - | - |
| 0.6253 | 10900 | 5.1267 | - | - | - | - | - |
| 0.6310 | 11000 | 5.0443 | - | - | - | - | - |
| 0.6367 | 11100 | 5.2001 | - | - | - | - | - |
| 0.6425 | 11200 | 4.8813 | - | - | - | - | - |
| 0.6482 | 11300 | 5.4734 | - | - | - | - | - |
| 0.6539 | 11400 | 5.0344 | - | - | - | - | - |
| 0.6597 | 11500 | 5.5043 | - | - | - | - | - |
| 0.6654 | 11600 | 4.6201 | - | - | - | - | - |
| 0.6711 | 11700 | 5.4626 | - | - | - | - | - |
| 0.6769 | 11800 | 5.3813 | - | - | - | - | - |
| 0.6826 | 11900 | 4.626 | - | - | - | - | - |
| 0.6883 | 12000 | 4.87 | - | - | - | - | - |
| 0.6941 | 12100 | 5.0015 | - | - | - | - | - |
| 0.6998 | 12200 | 4.962 | - | - | - | - | - |
| 0.7056 | 12300 | 5.1613 | - | - | - | - | - |
| 0.7113 | 12400 | 5.2074 | - | - | - | - | - |
| 0.7170 | 12500 | 4.958 | - | - | - | - | - |
| 0.7228 | 12600 | 4.4516 | - | - | - | - | - |
| 0.7285 | 12700 | 4.8421 | - | - | - | - | - |
| 0.7342 | 12800 | 4.9242 | - | - | - | - | - |
| 0.7400 | 12900 | 4.9256 | - | - | - | - | - |
| 0.7457 | 13000 | 4.8254 | - | - | - | - | - |
| 0.7514 | 13100 | 4.5114 | - | - | - | - | - |
| 0.7572 | 13200 | 7.7118 | - | - | - | - | - |
| 0.7629 | 13300 | 7.0822 | - | - | - | - | - |
| 0.7687 | 13400 | 6.8022 | - | - | - | - | - |
| 0.7744 | 13500 | 6.7295 | - | - | - | - | - |
| 0.7801 | 13600 | 6.0547 | - | - | - | - | - |
| 0.7859 | 13700 | 6.5285 | - | - | - | - | - |
| 0.7916 | 13800 | 6.2666 | - | - | - | - | - |
| 0.7973 | 13900 | 6.1031 | - | - | - | - | - |
| 0.8031 | 14000 | 5.9138 | - | - | - | - | - |
| 0.8088 | 14100 | 5.6636 | - | - | - | - | - |
| 0.8145 | 14200 | 5.7073 | - | - | - | - | - |
| 0.8203 | 14300 | 5.7963 | - | - | - | - | - |
| 0.8260 | 14400 | 5.7336 | - | - | - | - | - |
| 0.8318 | 14500 | 5.8113 | - | - | - | - | - |
| 0.8375 | 14600 | 5.6708 | - | - | - | - | - |
| 0.8432 | 14700 | 5.4565 | - | - | - | - | - |
| 0.8490 | 14800 | 5.4293 | - | - | - | - | - |
| 0.8547 | 14900 | 5.4166 | - | - | - | - | - |
| 0.8604 | 15000 | 5.3616 | - | - | - | - | - |
| 0.8662 | 15100 | 5.1579 | - | - | - | - | - |
| 0.8719 | 15200 | 5.3887 | - | - | - | - | - |
| 0.8776 | 15300 | 5.346 | - | - | - | - | - |
| 0.8834 | 15400 | 5.2762 | - | - | - | - | - |
| 0.8891 | 15500 | 5.3417 | - | - | - | - | - |
| 0.8949 | 15600 | 5.1607 | - | - | - | - | - |
| 0.9006 | 15700 | 5.4493 | - | - | - | - | - |
| 0.9063 | 15800 | 5.0268 | - | - | - | - | - |
| 0.9121 | 15900 | 5.0612 | - | - | - | - | - |
| 0.9178 | 16000 | 5.1471 | - | - | - | - | - |
| 0.9235 | 16100 | 4.8275 | - | - | - | - | - |
| 0.9293 | 16200 | 5.1464 | - | - | - | - | - |
| 0.9350 | 16300 | 4.958 | - | - | - | - | - |
| 0.9407 | 16400 | 5.1968 | - | - | - | - | - |
| 0.9465 | 16500 | 4.7783 | - | - | - | - | - |
| 0.9522 | 16600 | 5.0834 | - | - | - | - | - |
| 0.9580 | 16700 | 4.9839 | - | - | - | - | - |
| 0.9637 | 16800 | 5.0078 | - | - | - | - | - |
| 0.9694 | 16900 | 5.1624 | - | - | - | - | - |
| 0.9752 | 17000 | 5.2132 | - | - | - | - | - |
| 0.9809 | 17100 | 4.9741 | - | - | - | - | - |
| 0.9866 | 17200 | 4.96 | - | - | - | - | - |
| 0.9924 | 17300 | 5.1834 | - | - | - | - | - |
| 0.9981 | 17400 | 4.8955 | - | - | - | - | - |
| 1.0 | 17433 | - | 0.6638 | 0.6702 | 0.6770 | 0.6556 | 0.6802 |
</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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