---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1115700
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: nomic-ai/nomic-embed-text-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: 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 nomic-ai/nomic-embed-text-v1.5
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 768
type: sts-test-768
metrics:
- type: pearson_cosine
value: 0.6944960057464138
name: Pearson Cosine
- type: spearman_cosine
value: 0.6872396378196957
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7086043588614903
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7136479613274518
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7084460037709435
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7128357831285198
name: Spearman Euclidean
- type: pearson_dot
value: 0.481902874304561
name: Pearson Dot
- type: spearman_dot
value: 0.46588918379526945
name: Spearman Dot
- type: pearson_max
value: 0.7086043588614903
name: Pearson Max
- type: spearman_max
value: 0.7136479613274518
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 512
type: sts-test-512
metrics:
- type: pearson_cosine
value: 0.6925787246105148
name: Pearson Cosine
- type: spearman_cosine
value: 0.6859479129419207
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7087290093387656
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7127968133455542
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7088805484816247
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7123606046721803
name: Spearman Euclidean
- type: pearson_dot
value: 0.4684333245586192
name: Pearson Dot
- type: spearman_dot
value: 0.45257836578849003
name: Spearman Dot
- type: pearson_max
value: 0.7088805484816247
name: Pearson Max
- type: spearman_max
value: 0.7127968133455542
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 256
type: sts-test-256
metrics:
- type: pearson_cosine
value: 0.6876956481856266
name: Pearson Cosine
- type: spearman_cosine
value: 0.6814892249857147
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7083882582081078
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7097524143994903
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7094190252305796
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7104287347206688
name: Spearman Euclidean
- type: pearson_dot
value: 0.4438925722484721
name: Pearson Dot
- type: spearman_dot
value: 0.4255299982188107
name: Spearman Dot
- type: pearson_max
value: 0.7094190252305796
name: Pearson Max
- type: spearman_max
value: 0.7104287347206688
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 128
type: sts-test-128
metrics:
- type: pearson_cosine
value: 0.6708560165075523
name: Pearson Cosine
- type: spearman_cosine
value: 0.6669935075512006
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7041961281711793
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7000807688296651
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7055061381768357
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7022686907818495
name: Spearman Euclidean
- type: pearson_dot
value: 0.37855771167572094
name: Pearson Dot
- type: spearman_dot
value: 0.35930717422088765
name: Spearman Dot
- type: pearson_max
value: 0.7055061381768357
name: Pearson Max
- type: spearman_max
value: 0.7022686907818495
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 64
type: sts-test-64
metrics:
- type: pearson_cosine
value: 0.6533817775144477
name: Pearson Cosine
- type: spearman_cosine
value: 0.6523997361414113
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6919834348567717
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6857245312336051
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6950438027503257
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6899151458827059
name: Spearman Euclidean
- type: pearson_dot
value: 0.33502302384042637
name: Pearson Dot
- type: spearman_dot
value: 0.3097469345046609
name: Spearman Dot
- type: pearson_max
value: 0.6950438027503257
name: Pearson Max
- type: spearman_max
value: 0.6899151458827059
name: Spearman Max
---
# SentenceTransformer based on nomic-ai/nomic-embed-text-v1.5
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) 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:** [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) <!-- at revision b0753ae76394dd36bcfb912a46018088bca48be0 -->
- **Maximum Sequence Length:** 8192 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': 8192, 'do_lower_case': False}) with Transformer model: NomicBertModel
(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-nomic-embed-text-v1.5-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 | 0.6945 |
| **spearman_cosine** | **0.6872** |
| pearson_manhattan | 0.7086 |
| spearman_manhattan | 0.7136 |
| pearson_euclidean | 0.7084 |
| spearman_euclidean | 0.7128 |
| pearson_dot | 0.4819 |
| spearman_dot | 0.4659 |
| pearson_max | 0.7086 |
| spearman_max | 0.7136 |
#### 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 | 0.6926 |
| **spearman_cosine** | **0.6859** |
| pearson_manhattan | 0.7087 |
| spearman_manhattan | 0.7128 |
| pearson_euclidean | 0.7089 |
| spearman_euclidean | 0.7124 |
| pearson_dot | 0.4684 |
| spearman_dot | 0.4526 |
| pearson_max | 0.7089 |
| spearman_max | 0.7128 |
#### 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 | 0.6877 |
| **spearman_cosine** | **0.6815** |
| pearson_manhattan | 0.7084 |
| spearman_manhattan | 0.7098 |
| pearson_euclidean | 0.7094 |
| spearman_euclidean | 0.7104 |
| pearson_dot | 0.4439 |
| spearman_dot | 0.4255 |
| pearson_max | 0.7094 |
| spearman_max | 0.7104 |
#### 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 | 0.6709 |
| **spearman_cosine** | **0.667** |
| pearson_manhattan | 0.7042 |
| spearman_manhattan | 0.7001 |
| pearson_euclidean | 0.7055 |
| spearman_euclidean | 0.7023 |
| pearson_dot | 0.3786 |
| spearman_dot | 0.3593 |
| pearson_max | 0.7055 |
| spearman_max | 0.7023 |
#### 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 | 0.6534 |
| **spearman_cosine** | **0.6524** |
| pearson_manhattan | 0.692 |
| spearman_manhattan | 0.6857 |
| pearson_euclidean | 0.695 |
| spearman_euclidean | 0.6899 |
| pearson_dot | 0.335 |
| spearman_dot | 0.3097 |
| pearson_max | 0.695 |
| spearman_max | 0.6899 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### 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`: 24
- `per_device_eval_batch_size`: 24
- `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`: 24
- `per_device_eval_batch_size`: 24
- `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 |
|:------:|:-----:|:-------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|
| 0.0043 | 100 | 10.0627 | - | - | - | - | - |
| 0.0086 | 200 | 8.2355 | - | - | - | - | - |
| 0.0129 | 300 | 6.7233 | - | - | - | - | - |
| 0.0172 | 400 | 6.5832 | - | - | - | - | - |
| 0.0215 | 500 | 6.7512 | - | - | - | - | - |
| 0.0258 | 600 | 6.7634 | - | - | - | - | - |
| 0.0301 | 700 | 6.5592 | - | - | - | - | - |
| 0.0344 | 800 | 5.0689 | - | - | - | - | - |
| 0.0387 | 900 | 4.7079 | - | - | - | - | - |
| 0.0430 | 1000 | 4.6359 | - | - | - | - | - |
| 0.0473 | 1100 | 4.4513 | - | - | - | - | - |
| 0.0516 | 1200 | 4.2328 | - | - | - | - | - |
| 0.0559 | 1300 | 3.7454 | - | - | - | - | - |
| 0.0602 | 1400 | 3.9198 | - | - | - | - | - |
| 0.0645 | 1500 | 4.0727 | - | - | - | - | - |
| 0.0688 | 1600 | 3.8923 | - | - | - | - | - |
| 0.0731 | 1700 | 3.8137 | - | - | - | - | - |
| 0.0774 | 1800 | 4.1512 | - | - | - | - | - |
| 0.0817 | 1900 | 4.1304 | - | - | - | - | - |
| 0.0860 | 2000 | 4.0195 | - | - | - | - | - |
| 0.0903 | 2100 | 3.6836 | - | - | - | - | - |
| 0.0946 | 2200 | 2.9968 | - | - | - | - | - |
| 0.0990 | 2300 | 2.8909 | - | - | - | - | - |
| 0.1033 | 2400 | 3.0884 | - | - | - | - | - |
| 0.1076 | 2500 | 3.3081 | - | - | - | - | - |
| 0.1119 | 2600 | 3.6266 | - | - | - | - | - |
| 0.1162 | 2700 | 4.3754 | - | - | - | - | - |
| 0.1205 | 2800 | 4.0218 | - | - | - | - | - |
| 0.1248 | 2900 | 3.7167 | - | - | - | - | - |
| 0.1291 | 3000 | 3.4815 | - | - | - | - | - |
| 0.1334 | 3100 | 3.6446 | - | - | - | - | - |
| 0.1377 | 3200 | 3.44 | - | - | - | - | - |
| 0.1420 | 3300 | 3.6725 | - | - | - | - | - |
| 0.1463 | 3400 | 3.4699 | - | - | - | - | - |
| 0.1506 | 3500 | 3.076 | - | - | - | - | - |
| 0.1549 | 3600 | 3.1179 | - | - | - | - | - |
| 0.1592 | 3700 | 3.1704 | - | - | - | - | - |
| 0.1635 | 3800 | 3.4614 | - | - | - | - | - |
| 0.1678 | 3900 | 4.1157 | - | - | - | - | - |
| 0.1721 | 4000 | 4.1584 | - | - | - | - | - |
| 0.1764 | 4100 | 4.5602 | - | - | - | - | - |
| 0.1807 | 4200 | 3.6875 | - | - | - | - | - |
| 0.1850 | 4300 | 4.1521 | - | - | - | - | - |
| 0.1893 | 4400 | 3.5475 | - | - | - | - | - |
| 0.1936 | 4500 | 3.4036 | - | - | - | - | - |
| 0.1979 | 4600 | 3.0564 | - | - | - | - | - |
| 0.2022 | 4700 | 3.7761 | - | - | - | - | - |
| 0.2065 | 4800 | 3.6857 | - | - | - | - | - |
| 0.2108 | 4900 | 3.3534 | - | - | - | - | - |
| 0.2151 | 5000 | 4.1137 | - | - | - | - | - |
| 0.2194 | 5100 | 3.5239 | - | - | - | - | - |
| 0.2237 | 5200 | 4.1297 | - | - | - | - | - |
| 0.2280 | 5300 | 3.5339 | - | - | - | - | - |
| 0.2323 | 5400 | 3.9294 | - | - | - | - | - |
| 0.2366 | 5500 | 3.717 | - | - | - | - | - |
| 0.2409 | 5600 | 3.3346 | - | - | - | - | - |
| 0.2452 | 5700 | 4.0495 | - | - | - | - | - |
| 0.2495 | 5800 | 3.7869 | - | - | - | - | - |
| 0.2538 | 5900 | 3.9533 | - | - | - | - | - |
| 0.2581 | 6000 | 4.1135 | - | - | - | - | - |
| 0.2624 | 6100 | 3.6655 | - | - | - | - | - |
| 0.2667 | 6200 | 3.9111 | - | - | - | - | - |
| 0.2710 | 6300 | 3.8582 | - | - | - | - | - |
| 0.2753 | 6400 | 3.7712 | - | - | - | - | - |
| 0.2796 | 6500 | 3.6536 | - | - | - | - | - |
| 0.2839 | 6600 | 3.4516 | - | - | - | - | - |
| 0.2882 | 6700 | 3.7151 | - | - | - | - | - |
| 0.2925 | 6800 | 3.7659 | - | - | - | - | - |
| 0.2969 | 6900 | 3.3159 | - | - | - | - | - |
| 0.3012 | 7000 | 3.5753 | - | - | - | - | - |
| 0.3055 | 7100 | 4.2095 | - | - | - | - | - |
| 0.3098 | 7200 | 3.718 | - | - | - | - | - |
| 0.3141 | 7300 | 4.0709 | - | - | - | - | - |
| 0.3184 | 7400 | 3.8079 | - | - | - | - | - |
| 0.3227 | 7500 | 3.3735 | - | - | - | - | - |
| 0.3270 | 7600 | 3.7303 | - | - | - | - | - |
| 0.3313 | 7700 | 3.2693 | - | - | - | - | - |
| 0.3356 | 7800 | 3.6564 | - | - | - | - | - |
| 0.3399 | 7900 | 3.6702 | - | - | - | - | - |
| 0.3442 | 8000 | 3.7274 | - | - | - | - | - |
| 0.3485 | 8100 | 3.8536 | - | - | - | - | - |
| 0.3528 | 8200 | 3.9516 | - | - | - | - | - |
| 0.3571 | 8300 | 3.7351 | - | - | - | - | - |
| 0.3614 | 8400 | 3.649 | - | - | - | - | - |
| 0.3657 | 8500 | 3.5913 | - | - | - | - | - |
| 0.3700 | 8600 | 3.7733 | - | - | - | - | - |
| 0.3743 | 8700 | 3.6359 | - | - | - | - | - |
| 0.3786 | 8800 | 4.2983 | - | - | - | - | - |
| 0.3829 | 8900 | 3.6692 | - | - | - | - | - |
| 0.3872 | 9000 | 3.7309 | - | - | - | - | - |
| 0.3915 | 9100 | 3.8886 | - | - | - | - | - |
| 0.3958 | 9200 | 3.8999 | - | - | - | - | - |
| 0.4001 | 9300 | 3.5528 | - | - | - | - | - |
| 0.4044 | 9400 | 3.6309 | - | - | - | - | - |
| 0.4087 | 9500 | 4.2475 | - | - | - | - | - |
| 0.4130 | 9600 | 3.793 | - | - | - | - | - |
| 0.4173 | 9700 | 3.6575 | - | - | - | - | - |
| 0.4216 | 9800 | 3.84 | - | - | - | - | - |
| 0.4259 | 9900 | 3.3721 | - | - | - | - | - |
| 0.4302 | 10000 | 4.3743 | - | - | - | - | - |
| 0.4345 | 10100 | 3.5054 | - | - | - | - | - |
| 0.4388 | 10200 | 3.54 | - | - | - | - | - |
| 0.4431 | 10300 | 3.6197 | - | - | - | - | - |
| 0.4474 | 10400 | 3.7567 | - | - | - | - | - |
| 0.4517 | 10500 | 3.9814 | - | - | - | - | - |
| 0.4560 | 10600 | 3.6277 | - | - | - | - | - |
| 0.4603 | 10700 | 3.5071 | - | - | - | - | - |
| 0.4646 | 10800 | 3.8348 | - | - | - | - | - |
| 0.4689 | 10900 | 3.8674 | - | - | - | - | - |
| 0.4732 | 11000 | 3.0325 | - | - | - | - | - |
| 0.4775 | 11100 | 3.7262 | - | - | - | - | - |
| 0.4818 | 11200 | 3.6921 | - | - | - | - | - |
| 0.4861 | 11300 | 3.4946 | - | - | - | - | - |
| 0.4904 | 11400 | 3.7541 | - | - | - | - | - |
| 0.4948 | 11500 | 3.6751 | - | - | - | - | - |
| 0.4991 | 11600 | 3.8765 | - | - | - | - | - |
| 0.5034 | 11700 | 3.5058 | - | - | - | - | - |
| 0.5077 | 11800 | 3.5135 | - | - | - | - | - |
| 0.5120 | 11900 | 3.8052 | - | - | - | - | - |
| 0.5163 | 12000 | 3.3015 | - | - | - | - | - |
| 0.5206 | 12100 | 3.5389 | - | - | - | - | - |
| 0.5249 | 12200 | 3.5226 | - | - | - | - | - |
| 0.5292 | 12300 | 3.6715 | - | - | - | - | - |
| 0.5335 | 12400 | 3.2256 | - | - | - | - | - |
| 0.5378 | 12500 | 3.3447 | - | - | - | - | - |
| 0.5421 | 12600 | 3.6315 | - | - | - | - | - |
| 0.5464 | 12700 | 3.8674 | - | - | - | - | - |
| 0.5507 | 12800 | 3.4066 | - | - | - | - | - |
| 0.5550 | 12900 | 3.7356 | - | - | - | - | - |
| 0.5593 | 13000 | 3.5742 | - | - | - | - | - |
| 0.5636 | 13100 | 3.7676 | - | - | - | - | - |
| 0.5679 | 13200 | 3.7907 | - | - | - | - | - |
| 0.5722 | 13300 | 3.8089 | - | - | - | - | - |
| 0.5765 | 13400 | 3.4742 | - | - | - | - | - |
| 0.5808 | 13500 | 3.6536 | - | - | - | - | - |
| 0.5851 | 13600 | 3.7736 | - | - | - | - | - |
| 0.5894 | 13700 | 3.9072 | - | - | - | - | - |
| 0.5937 | 13800 | 3.7386 | - | - | - | - | - |
| 0.5980 | 13900 | 3.3387 | - | - | - | - | - |
| 0.6023 | 14000 | 3.5509 | - | - | - | - | - |
| 0.6066 | 14100 | 3.7056 | - | - | - | - | - |
| 0.6109 | 14200 | 3.7283 | - | - | - | - | - |
| 0.6152 | 14300 | 3.7301 | - | - | - | - | - |
| 0.6195 | 14400 | 3.8027 | - | - | - | - | - |
| 0.6238 | 14500 | 3.5606 | - | - | - | - | - |
| 0.6281 | 14600 | 3.9467 | - | - | - | - | - |
| 0.6324 | 14700 | 3.3394 | - | - | - | - | - |
| 0.6367 | 14800 | 4.1254 | - | - | - | - | - |
| 0.6410 | 14900 | 3.7121 | - | - | - | - | - |
| 0.6453 | 15000 | 3.9167 | - | - | - | - | - |
| 0.6496 | 15100 | 3.8084 | - | - | - | - | - |
| 0.6539 | 15200 | 3.7794 | - | - | - | - | - |
| 0.6582 | 15300 | 3.7664 | - | - | - | - | - |
| 0.6625 | 15400 | 3.4378 | - | - | - | - | - |
| 0.6668 | 15500 | 3.6632 | - | - | - | - | - |
| 0.6711 | 15600 | 3.8493 | - | - | - | - | - |
| 0.6754 | 15700 | 4.1475 | - | - | - | - | - |
| 0.6797 | 15800 | 3.5782 | - | - | - | - | - |
| 0.6840 | 15900 | 3.4341 | - | - | - | - | - |
| 0.6883 | 16000 | 3.3295 | - | - | - | - | - |
| 0.6927 | 16100 | 3.8165 | - | - | - | - | - |
| 0.6970 | 16200 | 3.9702 | - | - | - | - | - |
| 0.7013 | 16300 | 3.6555 | - | - | - | - | - |
| 0.7056 | 16400 | 3.6946 | - | - | - | - | - |
| 0.7099 | 16500 | 3.8027 | - | - | - | - | - |
| 0.7142 | 16600 | 3.4523 | - | - | - | - | - |
| 0.7185 | 16700 | 3.461 | - | - | - | - | - |
| 0.7228 | 16800 | 3.4403 | - | - | - | - | - |
| 0.7271 | 16900 | 3.6398 | - | - | - | - | - |
| 0.7314 | 17000 | 3.8443 | - | - | - | - | - |
| 0.7357 | 17100 | 3.6012 | - | - | - | - | - |
| 0.7400 | 17200 | 3.6645 | - | - | - | - | - |
| 0.7443 | 17300 | 3.4899 | - | - | - | - | - |
| 0.7486 | 17400 | 3.7186 | - | - | - | - | - |
| 0.7529 | 17500 | 3.6199 | - | - | - | - | - |
| 0.7572 | 17600 | 4.4274 | - | - | - | - | - |
| 0.7615 | 17700 | 4.0262 | - | - | - | - | - |
| 0.7658 | 17800 | 3.9325 | - | - | - | - | - |
| 0.7701 | 17900 | 3.6338 | - | - | - | - | - |
| 0.7744 | 18000 | 3.6136 | - | - | - | - | - |
| 0.7787 | 18100 | 3.4514 | - | - | - | - | - |
| 0.7830 | 18200 | 3.4427 | - | - | - | - | - |
| 0.7873 | 18300 | 3.3601 | - | - | - | - | - |
| 0.7916 | 18400 | 3.313 | - | - | - | - | - |
| 0.7959 | 18500 | 3.4062 | - | - | - | - | - |
| 0.8002 | 18600 | 3.098 | - | - | - | - | - |
| 0.8045 | 18700 | 3.183 | - | - | - | - | - |
| 0.8088 | 18800 | 3.1482 | - | - | - | - | - |
| 0.8131 | 18900 | 3.0122 | - | - | - | - | - |
| 0.8174 | 19000 | 3.0828 | - | - | - | - | - |
| 0.8217 | 19100 | 3.063 | - | - | - | - | - |
| 0.8260 | 19200 | 2.9688 | - | - | - | - | - |
| 0.8303 | 19300 | 3.0425 | - | - | - | - | - |
| 0.8346 | 19400 | 3.2018 | - | - | - | - | - |
| 0.8389 | 19500 | 2.9111 | - | - | - | - | - |
| 0.8432 | 19600 | 2.9516 | - | - | - | - | - |
| 0.8475 | 19700 | 2.9115 | - | - | - | - | - |
| 0.8518 | 19800 | 2.9323 | - | - | - | - | - |
| 0.8561 | 19900 | 2.8753 | - | - | - | - | - |
| 0.8604 | 20000 | 2.8344 | - | - | - | - | - |
| 0.8647 | 20100 | 2.7665 | - | - | - | - | - |
| 0.8690 | 20200 | 2.7732 | - | - | - | - | - |
| 0.8733 | 20300 | 2.8622 | - | - | - | - | - |
| 0.8776 | 20400 | 2.8749 | - | - | - | - | - |
| 0.8819 | 20500 | 2.8534 | - | - | - | - | - |
| 0.8863 | 20600 | 2.9254 | - | - | - | - | - |
| 0.8906 | 20700 | 2.7366 | - | - | - | - | - |
| 0.8949 | 20800 | 2.7287 | - | - | - | - | - |
| 0.8992 | 20900 | 2.9469 | - | - | - | - | - |
| 0.9035 | 21000 | 2.9052 | - | - | - | - | - |
| 0.9078 | 21100 | 2.7256 | - | - | - | - | - |
| 0.9121 | 21200 | 2.8469 | - | - | - | - | - |
| 0.9164 | 21300 | 2.6626 | - | - | - | - | - |
| 0.9207 | 21400 | 2.6796 | - | - | - | - | - |
| 0.9250 | 21500 | 2.6927 | - | - | - | - | - |
| 0.9293 | 21600 | 2.7125 | - | - | - | - | - |
| 0.9336 | 21700 | 2.6734 | - | - | - | - | - |
| 0.9379 | 21800 | 2.7199 | - | - | - | - | - |
| 0.9422 | 21900 | 2.6635 | - | - | - | - | - |
| 0.9465 | 22000 | 2.5218 | - | - | - | - | - |
| 0.9508 | 22100 | 2.7595 | - | - | - | - | - |
| 0.9551 | 22200 | 2.6821 | - | - | - | - | - |
| 0.9594 | 22300 | 2.6578 | - | - | - | - | - |
| 0.9637 | 22400 | 2.568 | - | - | - | - | - |
| 0.9680 | 22500 | 2.5527 | - | - | - | - | - |
| 0.9723 | 22600 | 2.6857 | - | - | - | - | - |
| 0.9766 | 22700 | 2.6637 | - | - | - | - | - |
| 0.9809 | 22800 | 2.6311 | - | - | - | - | - |
| 0.9852 | 22900 | 2.4635 | - | - | - | - | - |
| 0.9895 | 23000 | 2.6239 | - | - | - | - | - |
| 0.9938 | 23100 | 2.6873 | - | - | - | - | - |
| 0.9981 | 23200 | 2.5138 | - | - | - | - | - |
| 1.0 | 23244 | - | 0.6670 | 0.6815 | 0.6859 | 0.6524 | 0.6872 |
</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}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->