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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: UBC-NLP/serengeti-E250
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 UBC-NLP/serengeti-E250
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 768
type: sts-test-768
metrics:
- type: pearson_cosine
value: 0.7084016023985643
name: Pearson Cosine
- type: spearman_cosine
value: 0.7080643276583263
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7163851544290831
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7066259909380899
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.716171309296757
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7064427148038006
name: Spearman Euclidean
- type: pearson_dot
value: 0.38463559218643695
name: Pearson Dot
- type: spearman_dot
value: 0.3566836293112297
name: Spearman Dot
- type: pearson_max
value: 0.7163851544290831
name: Pearson Max
- type: spearman_max
value: 0.7080643276583263
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 512
type: sts-test-512
metrics:
- type: pearson_cosine
value: 0.7059523092716506
name: Pearson Cosine
- type: spearman_cosine
value: 0.7046582726338858
name: Spearman Cosine
- type: pearson_manhattan
value: 0.714245009590492
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7048777976859945
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7150194670982656
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7055458365374757
name: Spearman Euclidean
- type: pearson_dot
value: 0.3855295554891442
name: Pearson Dot
- type: spearman_dot
value: 0.3585966097040326
name: Spearman Dot
- type: pearson_max
value: 0.7150194670982656
name: Pearson Max
- type: spearman_max
value: 0.7055458365374757
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 256
type: sts-test-256
metrics:
- type: pearson_cosine
value: 0.7069259070512649
name: Pearson Cosine
- type: spearman_cosine
value: 0.7072103115498357
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7151518946293685
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7050845216566457
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7154956682724514
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.70486417475867
name: Spearman Euclidean
- type: pearson_dot
value: 0.37291132473389677
name: Pearson Dot
- type: spearman_dot
value: 0.3480769113927452
name: Spearman Dot
- type: pearson_max
value: 0.7154956682724514
name: Pearson Max
- type: spearman_max
value: 0.7072103115498357
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 128
type: sts-test-128
metrics:
- type: pearson_cosine
value: 0.7022542784280805
name: Pearson Cosine
- type: spearman_cosine
value: 0.7062378358777478
name: Spearman Cosine
- type: pearson_manhattan
value: 0.711575484251127
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.701312903814612
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7125043324593673
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7011154675785318
name: Spearman Euclidean
- type: pearson_dot
value: 0.34394993785114003
name: Pearson Dot
- type: spearman_dot
value: 0.31686351995727197
name: Spearman Dot
- type: pearson_max
value: 0.7125043324593673
name: Pearson Max
- type: spearman_max
value: 0.7062378358777478
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 64
type: sts-test-64
metrics:
- type: pearson_cosine
value: 0.6950172826546709
name: Pearson Cosine
- type: spearman_cosine
value: 0.6993973161633343
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7059726901866531
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6938542774412633
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7066346687971139
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6949014564343952
name: Spearman Euclidean
- type: pearson_dot
value: 0.30982738809482646
name: Pearson Dot
- type: spearman_dot
value: 0.2855406388879541
name: Spearman Dot
- type: pearson_max
value: 0.7066346687971139
name: Pearson Max
- type: spearman_max
value: 0.6993973161633343
name: Spearman Max
---
# SentenceTransformer based on UBC-NLP/serengeti-E250
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [UBC-NLP/serengeti-E250](https://huggingface.co/UBC-NLP/serengeti-E250) on the Mollel/swahili-n_li-triplet-swh-eng dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [UBC-NLP/serengeti-E250](https://huggingface.co/UBC-NLP/serengeti-E250) <!-- at revision 41b5b8b6179c4af2859768cbf4f0f03e928d651d -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 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: ElectraModel
(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/MultiLinguSwahili-MultiLinguSwahili-serengeti-E250-nli-matryoshka-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, 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 | 0.7084 |
| **spearman_cosine** | **0.7081** |
| pearson_manhattan | 0.7164 |
| spearman_manhattan | 0.7066 |
| pearson_euclidean | 0.7162 |
| spearman_euclidean | 0.7064 |
| pearson_dot | 0.3846 |
| spearman_dot | 0.3567 |
| pearson_max | 0.7164 |
| spearman_max | 0.7081 |
#### 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 | 0.706 |
| **spearman_cosine** | **0.7047** |
| pearson_manhattan | 0.7142 |
| spearman_manhattan | 0.7049 |
| pearson_euclidean | 0.715 |
| spearman_euclidean | 0.7055 |
| pearson_dot | 0.3855 |
| spearman_dot | 0.3586 |
| pearson_max | 0.715 |
| spearman_max | 0.7055 |
#### 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 | 0.7069 |
| **spearman_cosine** | **0.7072** |
| pearson_manhattan | 0.7152 |
| spearman_manhattan | 0.7051 |
| pearson_euclidean | 0.7155 |
| spearman_euclidean | 0.7049 |
| pearson_dot | 0.3729 |
| spearman_dot | 0.3481 |
| pearson_max | 0.7155 |
| spearman_max | 0.7072 |
#### 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 | 0.7023 |
| **spearman_cosine** | **0.7062** |
| pearson_manhattan | 0.7116 |
| spearman_manhattan | 0.7013 |
| pearson_euclidean | 0.7125 |
| spearman_euclidean | 0.7011 |
| pearson_dot | 0.3439 |
| spearman_dot | 0.3169 |
| pearson_max | 0.7125 |
| spearman_max | 0.7062 |
#### 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 | 0.695 |
| **spearman_cosine** | **0.6994** |
| pearson_manhattan | 0.706 |
| spearman_manhattan | 0.6939 |
| pearson_euclidean | 0.7066 |
| spearman_euclidean | 0.6949 |
| pearson_dot | 0.3098 |
| spearman_dot | 0.2855 |
| pearson_max | 0.7066 |
| spearman_max | 0.6994 |
<!--
## 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: 6 tokens</li><li>mean: 11.27 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.0 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 12.56 tokens</li><li>max: 29 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: 5 tokens</li><li>mean: 18.07 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.45 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.27 tokens</li><li>max: 29 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:-------------------------------------------------------------------|
| <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code> | <code>The men are fighting outside a deli.</code> |
| <code>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`: 32
- `per_device_eval_batch_size`: 32
- `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`: 32
- `per_device_eval_batch_size`: 32
- `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 | 26.7003 | - | - | - | - | - |
| 0.0115 | 200 | 20.7097 | - | - | - | - | - |
| 0.0172 | 300 | 17.2266 | - | - | - | - | - |
| 0.0229 | 400 | 15.7511 | - | - | - | - | - |
| 0.0287 | 500 | 14.5329 | - | - | - | - | - |
| 0.0344 | 600 | 12.6534 | - | - | - | - | - |
| 0.0402 | 700 | 10.6758 | - | - | - | - | - |
| 0.0459 | 800 | 9.421 | - | - | - | - | - |
| 0.0516 | 900 | 9.5664 | - | - | - | - | - |
| 0.0574 | 1000 | 8.5166 | - | - | - | - | - |
| 0.0631 | 1100 | 8.657 | - | - | - | - | - |
| 0.0688 | 1200 | 8.5473 | - | - | - | - | - |
| 0.0746 | 1300 | 8.3018 | - | - | - | - | - |
| 0.0803 | 1400 | 8.4488 | - | - | - | - | - |
| 0.0860 | 1500 | 7.1796 | - | - | - | - | - |
| 0.0918 | 1600 | 6.6136 | - | - | - | - | - |
| 0.0975 | 1700 | 6.2638 | - | - | - | - | - |
| 0.1033 | 1800 | 6.6955 | - | - | - | - | - |
| 0.1090 | 1900 | 7.3585 | - | - | - | - | - |
| 0.1147 | 2000 | 6.9043 | - | - | - | - | - |
| 0.1205 | 2100 | 6.677 | - | - | - | - | - |
| 0.1262 | 2200 | 6.3914 | - | - | - | - | - |
| 0.1319 | 2300 | 6.0045 | - | - | - | - | - |
| 0.1377 | 2400 | 5.8048 | - | - | - | - | - |
| 0.1434 | 2500 | 5.6898 | - | - | - | - | - |
| 0.1491 | 2600 | 5.229 | - | - | - | - | - |
| 0.1549 | 2700 | 5.2407 | - | - | - | - | - |
| 0.1606 | 2800 | 5.7074 | - | - | - | - | - |
| 0.1664 | 2900 | 6.2917 | - | - | - | - | - |
| 0.1721 | 3000 | 6.5651 | - | - | - | - | - |
| 0.1778 | 3100 | 6.7751 | - | - | - | - | - |
| 0.1836 | 3200 | 6.195 | - | - | - | - | - |
| 0.1893 | 3300 | 5.4697 | - | - | - | - | - |
| 0.1950 | 3400 | 5.1362 | - | - | - | - | - |
| 0.2008 | 3500 | 5.581 | - | - | - | - | - |
| 0.2065 | 3600 | 5.4309 | - | - | - | - | - |
| 0.2122 | 3700 | 5.6688 | - | - | - | - | - |
| 0.2180 | 3800 | 5.6923 | - | - | - | - | - |
| 0.2237 | 3900 | 5.8598 | - | - | - | - | - |
| 0.2294 | 4000 | 5.3498 | - | - | - | - | - |
| 0.2352 | 4100 | 5.3797 | - | - | - | - | - |
| 0.2409 | 4200 | 5.0389 | - | - | - | - | - |
| 0.2467 | 4300 | 5.6622 | - | - | - | - | - |
| 0.2524 | 4400 | 5.6249 | - | - | - | - | - |
| 0.2581 | 4500 | 5.6927 | - | - | - | - | - |
| 0.2639 | 4600 | 5.3612 | - | - | - | - | - |
| 0.2696 | 4700 | 5.2751 | - | - | - | - | - |
| 0.2753 | 4800 | 5.4224 | - | - | - | - | - |
| 0.2811 | 4900 | 5.0338 | - | - | - | - | - |
| 0.2868 | 5000 | 4.9813 | - | - | - | - | - |
| 0.2925 | 5100 | 4.8533 | - | - | - | - | - |
| 0.2983 | 5200 | 5.4137 | - | - | - | - | - |
| 0.3040 | 5300 | 5.4063 | - | - | - | - | - |
| 0.3098 | 5400 | 5.3107 | - | - | - | - | - |
| 0.3155 | 5500 | 5.0907 | - | - | - | - | - |
| 0.3212 | 5600 | 4.8644 | - | - | - | - | - |
| 0.3270 | 5700 | 4.7926 | - | - | - | - | - |
| 0.3327 | 5800 | 5.0268 | - | - | - | - | - |
| 0.3384 | 5900 | 5.3029 | - | - | - | - | - |
| 0.3442 | 6000 | 5.1246 | - | - | - | - | - |
| 0.3499 | 6100 | 5.1152 | - | - | - | - | - |
| 0.3556 | 6200 | 5.4265 | - | - | - | - | - |
| 0.3614 | 6300 | 4.7079 | - | - | - | - | - |
| 0.3671 | 6400 | 4.6368 | - | - | - | - | - |
| 0.3729 | 6500 | 4.662 | - | - | - | - | - |
| 0.3786 | 6600 | 5.3695 | - | - | - | - | - |
| 0.3843 | 6700 | 4.6974 | - | - | - | - | - |
| 0.3901 | 6800 | 4.6584 | - | - | - | - | - |
| 0.3958 | 6900 | 4.7413 | - | - | - | - | - |
| 0.4015 | 7000 | 4.6604 | - | - | - | - | - |
| 0.4073 | 7100 | 5.2476 | - | - | - | - | - |
| 0.4130 | 7200 | 4.9966 | - | - | - | - | - |
| 0.4187 | 7300 | 4.656 | - | - | - | - | - |
| 0.4245 | 7400 | 4.5711 | - | - | - | - | - |
| 0.4302 | 7500 | 5.0256 | - | - | - | - | - |
| 0.4360 | 7600 | 4.3856 | - | - | - | - | - |
| 0.4417 | 7700 | 4.2548 | - | - | - | - | - |
| 0.4474 | 7800 | 4.8584 | - | - | - | - | - |
| 0.4532 | 7900 | 4.8563 | - | - | - | - | - |
| 0.4589 | 8000 | 4.5101 | - | - | - | - | - |
| 0.4646 | 8100 | 4.4688 | - | - | - | - | - |
| 0.4704 | 8200 | 4.7076 | - | - | - | - | - |
| 0.4761 | 8300 | 4.3268 | - | - | - | - | - |
| 0.4818 | 8400 | 4.6622 | - | - | - | - | - |
| 0.4876 | 8500 | 4.4808 | - | - | - | - | - |
| 0.4933 | 8600 | 4.676 | - | - | - | - | - |
| 0.4991 | 8700 | 5.0348 | - | - | - | - | - |
| 0.5048 | 8800 | 4.5497 | - | - | - | - | - |
| 0.5105 | 8900 | 4.7428 | - | - | - | - | - |
| 0.5163 | 9000 | 4.4418 | - | - | - | - | - |
| 0.5220 | 9100 | 4.4946 | - | - | - | - | - |
| 0.5277 | 9200 | 4.5249 | - | - | - | - | - |
| 0.5335 | 9300 | 4.2413 | - | - | - | - | - |
| 0.5392 | 9400 | 4.4799 | - | - | - | - | - |
| 0.5449 | 9500 | 4.6807 | - | - | - | - | - |
| 0.5507 | 9600 | 4.5901 | - | - | - | - | - |
| 0.5564 | 9700 | 4.7266 | - | - | - | - | - |
| 0.5622 | 9800 | 4.692 | - | - | - | - | - |
| 0.5679 | 9900 | 4.8651 | - | - | - | - | - |
| 0.5736 | 10000 | 4.7746 | - | - | - | - | - |
| 0.5794 | 10100 | 4.68 | - | - | - | - | - |
| 0.5851 | 10200 | 4.7697 | - | - | - | - | - |
| 0.5908 | 10300 | 4.8848 | - | - | - | - | - |
| 0.5966 | 10400 | 4.4004 | - | - | - | - | - |
| 0.6023 | 10500 | 4.2979 | - | - | - | - | - |
| 0.6080 | 10600 | 4.7266 | - | - | - | - | - |
| 0.6138 | 10700 | 4.8605 | - | - | - | - | - |
| 0.6195 | 10800 | 4.7436 | - | - | - | - | - |
| 0.6253 | 10900 | 4.6239 | - | - | - | - | - |
| 0.6310 | 11000 | 4.394 | - | - | - | - | - |
| 0.6367 | 11100 | 4.8081 | - | - | - | - | - |
| 0.6425 | 11200 | 4.2329 | - | - | - | - | - |
| 0.6482 | 11300 | 4.873 | - | - | - | - | - |
| 0.6539 | 11400 | 4.5557 | - | - | - | - | - |
| 0.6597 | 11500 | 4.7918 | - | - | - | - | - |
| 0.6654 | 11600 | 4.1607 | - | - | - | - | - |
| 0.6711 | 11700 | 4.8744 | - | - | - | - | - |
| 0.6769 | 11800 | 5.0072 | - | - | - | - | - |
| 0.6826 | 11900 | 4.3532 | - | - | - | - | - |
| 0.6883 | 12000 | 4.3319 | - | - | - | - | - |
| 0.6941 | 12100 | 4.6885 | - | - | - | - | - |
| 0.6998 | 12200 | 4.6682 | - | - | - | - | - |
| 0.7056 | 12300 | 4.4258 | - | - | - | - | - |
| 0.7113 | 12400 | 4.6136 | - | - | - | - | - |
| 0.7170 | 12500 | 4.3594 | - | - | - | - | - |
| 0.7228 | 12600 | 4.0627 | - | - | - | - | - |
| 0.7285 | 12700 | 4.5244 | - | - | - | - | - |
| 0.7342 | 12800 | 4.504 | - | - | - | - | - |
| 0.7400 | 12900 | 4.4694 | - | - | - | - | - |
| 0.7457 | 13000 | 4.4804 | - | - | - | - | - |
| 0.7514 | 13100 | 4.0588 | - | - | - | - | - |
| 0.7572 | 13200 | 4.8016 | - | - | - | - | - |
| 0.7629 | 13300 | 4.2971 | - | - | - | - | - |
| 0.7687 | 13400 | 4.1326 | - | - | - | - | - |
| 0.7744 | 13500 | 3.9763 | - | - | - | - | - |
| 0.7801 | 13600 | 3.7716 | - | - | - | - | - |
| 0.7859 | 13700 | 3.8448 | - | - | - | - | - |
| 0.7916 | 13800 | 3.6779 | - | - | - | - | - |
| 0.7973 | 13900 | 3.5938 | - | - | - | - | - |
| 0.8031 | 14000 | 3.3981 | - | - | - | - | - |
| 0.8088 | 14100 | 3.4151 | - | - | - | - | - |
| 0.8145 | 14200 | 3.2498 | - | - | - | - | - |
| 0.8203 | 14300 | 3.4909 | - | - | - | - | - |
| 0.8260 | 14400 | 3.4098 | - | - | - | - | - |
| 0.8318 | 14500 | 3.4448 | - | - | - | - | - |
| 0.8375 | 14600 | 3.2868 | - | - | - | - | - |
| 0.8432 | 14700 | 3.2196 | - | - | - | - | - |
| 0.8490 | 14800 | 3.0852 | - | - | - | - | - |
| 0.8547 | 14900 | 3.2341 | - | - | - | - | - |
| 0.8604 | 15000 | 3.164 | - | - | - | - | - |
| 0.8662 | 15100 | 3.0919 | - | - | - | - | - |
| 0.8719 | 15200 | 3.176 | - | - | - | - | - |
| 0.8776 | 15300 | 3.1361 | - | - | - | - | - |
| 0.8834 | 15400 | 3.0683 | - | - | - | - | - |
| 0.8891 | 15500 | 3.0275 | - | - | - | - | - |
| 0.8949 | 15600 | 3.0763 | - | - | - | - | - |
| 0.9006 | 15700 | 3.1828 | - | - | - | - | - |
| 0.9063 | 15800 | 3.0053 | - | - | - | - | - |
| 0.9121 | 15900 | 2.9696 | - | - | - | - | - |
| 0.9178 | 16000 | 2.8919 | - | - | - | - | - |
| 0.9235 | 16100 | 2.9922 | - | - | - | - | - |
| 0.9293 | 16200 | 2.9063 | - | - | - | - | - |
| 0.9350 | 16300 | 3.0633 | - | - | - | - | - |
| 0.9407 | 16400 | 3.1782 | - | - | - | - | - |
| 0.9465 | 16500 | 2.9206 | - | - | - | - | - |
| 0.9522 | 16600 | 2.8785 | - | - | - | - | - |
| 0.9580 | 16700 | 2.9934 | - | - | - | - | - |
| 0.9637 | 16800 | 3.0125 | - | - | - | - | - |
| 0.9694 | 16900 | 2.9338 | - | - | - | - | - |
| 0.9752 | 17000 | 2.9931 | - | - | - | - | - |
| 0.9809 | 17100 | 2.956 | - | - | - | - | - |
| 0.9866 | 17200 | 2.8415 | - | - | - | - | - |
| 0.9924 | 17300 | 3.0072 | - | - | - | - | - |
| 0.9981 | 17400 | 2.9046 | - | - | - | - | - |
| 1.0 | 17433 | - | 0.7062 | 0.7072 | 0.7047 | 0.6994 | 0.7081 |
</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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