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---
language:
- tr
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:482091
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
- loss:CoSENTLoss
base_model: Alibaba-NLP/gte-multilingual-base
widget:
- source_sentence: Ya da dışarı çıkıp yürü ya da biraz koşun. Bunu düzenli olarak
yapmıyorum ama Washington bunu yapmak için harika bir yer.
sentences:
- “Washington's yürüyüş ya da koşu için harika bir yer.”
- H-2A uzaylılar Amerika Birleşik Devletleri'nde zaman kısa süreleri var.
- “Washington'da düzenli olarak yürüyüşe ya da koşuya çıkıyorum.”
- source_sentence: Orta yaylalar ve güney kıyıları arasındaki kontrast daha belirgin
olamazdı.
sentences:
- İşitme Yardımı Uyumluluğu Müzakere Kuralları Komitesi, Federal İletişim Komisyonu'nun
bir ürünüdür.
- Dağlık ve sahil arasındaki kontrast kolayca işaretlendi.
- Kontrast işaretlenemedi.
- source_sentence: Bir 1997 Henry J. Kaiser Aile Vakfı anket yönetilen bakım planlarında
Amerikalılar temelde kendi bakımı ile memnun olduğunu bulundu.
sentences:
- Kaplanları takip ederken çok sessiz olmalısın.
- Henry Kaiser vakfı insanların sağlık hizmetlerinden hoşlandığını gösteriyor.
- Henry Kaiser Vakfı insanların sağlık hizmetlerinden nefret ettiğini gösteriyor.
- source_sentence: Eminim yapmışlardır.
sentences:
- Eminim öyle yapmışlardır.
- Batı Teksas'ta 100 10 dereceydi.
- Eminim yapmamışlardır.
- source_sentence: Ve gerçekten, baba haklıydı, oğlu zaten her şeyi tecrübe etmişti,
her şeyi denedi ve daha az ilgileniyordu.
sentences:
- Oğlu her şeye olan ilgisini kaybediyordu.
- Pek bir şey yapmadım.
- Baba oğlunun tecrübe için hala çok şey olduğunu biliyordu.
datasets:
- emrecan/all-nli-tr
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on Alibaba-NLP/gte-multilingual-base
results:
- task:
type: triplet
name: Triplet
dataset:
name: all nli tr test
type: all-nli-tr-test
metrics:
- type: cosine_accuracy
value: 0.8966145437983908
name: Cosine Accuracy
- type: cosine_accuracy
value: 0.9351753453772582
name: Cosine Accuracy
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test
type: sts-test
metrics:
- type: pearson_cosine
value: 0.8043925123766598
name: Pearson Cosine
- type: spearman_cosine
value: 0.804133282756889
name: Spearman Cosine
- type: pearson_cosine
value: 0.8133873820848544
name: Pearson Cosine
- type: spearman_cosine
value: 0.8199552151367876
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts22 test
type: sts22-test
metrics:
- type: pearson_cosine
value: 0.647912337747937
name: Pearson Cosine
- type: spearman_cosine
value: 0.6694072470896322
name: Spearman Cosine
- type: pearson_cosine
value: 0.6514085062457564
name: Pearson Cosine
- type: spearman_cosine
value: 0.6827342891126081
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev gte multilingual base
type: sts-dev-gte-multilingual-base
metrics:
- type: pearson_cosine
value: 0.838717139426684
name: Pearson Cosine
- type: spearman_cosine
value: 0.8428367492381358
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test gte multilingual base
type: sts-test-gte-multilingual-base
metrics:
- type: pearson_cosine
value: 0.8133873820848544
name: Pearson Cosine
- type: spearman_cosine
value: 0.8199552151367876
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: stsb dev 768
type: stsb-dev-768
metrics:
- type: pearson_cosine
value: 0.870311456444647
name: Pearson Cosine
- type: spearman_cosine
value: 0.8747522169942328
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: stsb dev 512
type: stsb-dev-512
metrics:
- type: pearson_cosine
value: 0.8696934286998554
name: Pearson Cosine
- type: spearman_cosine
value: 0.8753487201891684
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: stsb dev 256
type: stsb-dev-256
metrics:
- type: pearson_cosine
value: 0.8644706498119142
name: Pearson Cosine
- type: spearman_cosine
value: 0.873468734899321
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: stsb dev 128
type: stsb-dev-128
metrics:
- type: pearson_cosine
value: 0.8591309130178328
name: Pearson Cosine
- type: spearman_cosine
value: 0.8700377378574327
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: stsb dev 64
type: stsb-dev-64
metrics:
- type: pearson_cosine
value: 0.8479124810212979
name: Pearson Cosine
- type: spearman_cosine
value: 0.8655596653561272
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: stsb test 768
type: stsb-test-768
metrics:
- type: pearson_cosine
value: 0.8455412308380735
name: Pearson Cosine
- type: spearman_cosine
value: 0.8535290217691063
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: stsb test 512
type: stsb-test-512
metrics:
- type: pearson_cosine
value: 0.8464773608783734
name: Pearson Cosine
- type: spearman_cosine
value: 0.8553900248212041
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: stsb test 256
type: stsb-test-256
metrics:
- type: pearson_cosine
value: 0.8443046458551826
name: Pearson Cosine
- type: spearman_cosine
value: 0.8550098621393595
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: stsb test 128
type: stsb-test-128
metrics:
- type: pearson_cosine
value: 0.8363964421208214
name: Pearson Cosine
- type: spearman_cosine
value: 0.8511193715667303
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: stsb test 64
type: stsb-test-64
metrics:
- type: pearson_cosine
value: 0.8235450515966374
name: Pearson Cosine
- type: spearman_cosine
value: 0.8460761238725121
name: Spearman Cosine
---
# SentenceTransformer based on Alibaba-NLP/gte-multilingual-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) on the [all-nli-tr](https://huggingface.co/datasets/emrecan/all-nli-tr) 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:** [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) <!-- at revision ca1791e0bcc104f6db161f27de1340241b13c5a4 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [all-nli-tr](https://huggingface.co/datasets/emrecan/all-nli-tr)
- **Language:** tr
<!-- - **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: 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})
(2): Normalize()
)
```
## 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("sentence_transformers_model_id")
# Run inference
sentences = [
'Ve gerçekten, baba haklıydı, oğlu zaten her şeyi tecrübe etmişti, her şeyi denedi ve daha az ilgileniyordu.',
'Oğlu her şeye olan ilgisini kaybediyordu.',
'Baba oğlunun tecrübe için hala çok şey olduğunu biliyordu.',
]
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
#### Triplet
* Dataset: `all-nli-tr-test`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.8966** |
#### Semantic Similarity
* Datasets: `sts-test`, `sts22-test`, `sts-dev-gte-multilingual-base`, `sts-test-gte-multilingual-base`, `sts-test`, `sts22-test`, `stsb-dev-768`, `stsb-dev-512`, `stsb-dev-256`, `stsb-dev-128`, `stsb-dev-64`, `stsb-test-768`, `stsb-test-512`, `stsb-test-256`, `stsb-test-128` and `stsb-test-64`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | sts-test | sts22-test | sts-dev-gte-multilingual-base | sts-test-gte-multilingual-base | stsb-dev-768 | stsb-dev-512 | stsb-dev-256 | stsb-dev-128 | stsb-dev-64 | stsb-test-768 | stsb-test-512 | stsb-test-256 | stsb-test-128 | stsb-test-64 |
|:--------------------|:---------|:-----------|:------------------------------|:-------------------------------|:-------------|:-------------|:-------------|:-------------|:------------|:--------------|:--------------|:--------------|:--------------|:-------------|
| pearson_cosine | 0.8134 | 0.6514 | 0.8387 | 0.8134 | 0.8703 | 0.8697 | 0.8645 | 0.8591 | 0.8479 | 0.8455 | 0.8465 | 0.8443 | 0.8364 | 0.8235 |
| **spearman_cosine** | **0.82** | **0.6827** | **0.8428** | **0.82** | **0.8748** | **0.8753** | **0.8735** | **0.87** | **0.8656** | **0.8535** | **0.8554** | **0.855** | **0.8511** | **0.8461** |
#### Triplet
* Dataset: `all-nli-tr-test`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.9352** |
<!--
## 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
#### all-nli-tr
* Dataset: [all-nli-tr](https://huggingface.co/datasets/emrecan/all-nli-tr) at [daeabfb](https://huggingface.co/datasets/emrecan/all-nli-tr/tree/daeabfbc01f82757ab998bd23ce0ddfceaa5e24d)
* Size: 482,091 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 10.51 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.47 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 2.23</li><li>max: 5.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:----------------------------------------------------------|:-------------------------------------------------------------------|:-----------------|
| <code>Bir uçak kalkıyor.</code> | <code>Bir hava uçağı kalkıyor.</code> | <code>5.0</code> |
| <code>Bir adam büyük bir flüt çalıyor.</code> | <code>Bir adam flüt çalıyor.</code> | <code>3.8</code> |
| <code>Bir adam pizzaya rendelenmiş peynir yayıyor.</code> | <code>Bir adam pişmemiş pizzaya rendelenmiş peynir yayıyor.</code> | <code>3.8</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "CoSENTLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Evaluation Dataset
#### all-nli-tr
* Dataset: [all-nli-tr](https://huggingface.co/datasets/emrecan/all-nli-tr) at [daeabfb](https://huggingface.co/datasets/emrecan/all-nli-tr/tree/daeabfbc01f82757ab998bd23ce0ddfceaa5e24d)
* Size: 6,567 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 15.89 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.02 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 2.1</li><li>max: 5.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:---------------------------------------------|:----------------------------------------------------|:------------------|
| <code>Şapkalı bir adam dans ediyor.</code> | <code>Sert şapka takan bir adam dans ediyor.</code> | <code>5.0</code> |
| <code>Küçük bir çocuk ata biniyor.</code> | <code>Bir çocuk ata biniyor.</code> | <code>4.75</code> |
| <code>Bir adam yılana fare yediriyor.</code> | <code>Adam yılana fare yediriyor.</code> | <code>5.0</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "CoSENTLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `learning_rate`: 1e-05
- `weight_decay`: 0.01
- `num_train_epochs`: 10
- `warmup_ratio`: 0.1
- `warmup_steps`: 144
- `bf16`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `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
- `torch_empty_cache_steps`: None
- `learning_rate`: 1e-05
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 10
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 144
- `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
- `restore_callback_states_from_checkpoint`: 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, 'non_blocking': False, '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`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | all-nli-tr-test_cosine_accuracy | sts-test_spearman_cosine | sts22-test_spearman_cosine | sts-dev-gte-multilingual-base_spearman_cosine | sts-test-gte-multilingual-base_spearman_cosine | stsb-dev-768_spearman_cosine | stsb-dev-512_spearman_cosine | stsb-dev-256_spearman_cosine | stsb-dev-128_spearman_cosine | stsb-dev-64_spearman_cosine | stsb-test-768_spearman_cosine | stsb-test-512_spearman_cosine | stsb-test-256_spearman_cosine | stsb-test-128_spearman_cosine | stsb-test-64_spearman_cosine |
|:------:|:----:|:-------------:|:---------------:|:-------------------------------:|:------------------------:|:--------------------------:|:---------------------------------------------:|:----------------------------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:----------------------------:|
| 0 | 0 | - | - | 0.8966 | 0.8041 | 0.6694 | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1327 | 1000 | 2.5299 | 3.3893 | - | - | - | 0.8318 | - | - | - | - | - | - | - | - | - | - | - |
| 0.2655 | 2000 | 2.1132 | 3.3050 | - | - | - | 0.8345 | - | - | - | - | - | - | - | - | - | - | - |
| 0.3982 | 3000 | 5.1488 | 2.7752 | - | - | - | 0.8481 | - | - | - | - | - | - | - | - | - | - | - |
| 0.5310 | 4000 | 5.4103 | 2.7242 | - | - | - | 0.8445 | - | - | - | - | - | - | - | - | - | - | - |
| 0.6637 | 5000 | 5.1896 | 2.6701 | - | - | - | 0.8451 | - | - | - | - | - | - | - | - | - | - | - |
| 0.7965 | 6000 | 5.0105 | 2.6489 | - | - | - | 0.8431 | - | - | - | - | - | - | - | - | - | - | - |
| 0.9292 | 7000 | 5.1059 | 2.6114 | - | - | - | 0.8428 | - | - | - | - | - | - | - | - | - | - | - |
| 1.0 | 7533 | - | - | 0.9352 | 0.8200 | 0.6827 | - | 0.8200 | - | - | - | - | - | - | - | - | - | - |
| 1.1111 | 200 | 34.2828 | 29.8737 | - | - | - | - | - | 0.8671 | 0.8671 | 0.8639 | 0.8606 | 0.8546 | - | - | - | - | - |
| 2.2222 | 400 | 28.038 | 28.8915 | - | - | - | - | - | 0.8740 | 0.8742 | 0.8720 | 0.8691 | 0.8648 | - | - | - | - | - |
| 3.3333 | 600 | 27.3829 | 29.3391 | - | - | - | - | - | 0.8747 | 0.8751 | 0.8728 | 0.8699 | 0.8653 | - | - | - | - | - |
| 4.4444 | 800 | 26.807 | 30.0090 | - | - | - | - | - | 0.8756 | 0.8761 | 0.8741 | 0.8710 | 0.8665 | - | - | - | - | - |
| 5.5556 | 1000 | 26.4543 | 30.5886 | - | - | - | - | - | 0.8753 | 0.8757 | 0.8739 | 0.8705 | 0.8662 | - | - | - | - | - |
| 6.6667 | 1200 | 26.0413 | 31.3750 | - | - | - | - | - | 0.8744 | 0.8751 | 0.8730 | 0.8698 | 0.8655 | - | - | - | - | - |
| 7.7778 | 1400 | 25.8221 | 31.6515 | - | - | - | - | - | 0.8752 | 0.8758 | 0.8739 | 0.8706 | 0.8661 | - | - | - | - | - |
| 8.8889 | 1600 | 25.6656 | 31.9805 | - | - | - | - | - | 0.8746 | 0.8752 | 0.8733 | 0.8700 | 0.8655 | - | - | - | - | - |
| 10.0 | 1800 | 25.5355 | 32.0454 | - | - | - | - | - | 0.8748 | 0.8753 | 0.8735 | 0.8700 | 0.8656 | 0.8535 | 0.8554 | 0.8550 | 0.8511 | 0.8461 |
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.3.1
- Transformers: 4.49.0.dev0
- PyTorch: 2.5.1+cu121
- Accelerate: 1.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## 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}
}
```
#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
author={Su Jianlin},
year={2022},
month={Jan},
url={https://kexue.fm/archives/8847},
}
```
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