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---
base_model: Snowflake/snowflake-arctic-embed-m
language:
- en
library_name: sentence-transformers
license: apache-2.0
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:1K<n<10K
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: kim był Steve Yzerman?
sentences:
- Łazik marsjański Opportunity
- w jakim kraju jest przyznawany Order Białego Lotosu?
- do powstania jakich instytucji przyczynił się pierwszy biskup Makau?
- source_sentence: gdzie rośnie bokkonia?
sentences:
- jak rozmnażają się Aeolosomatidae?
- kto 1 stycznia 2011 został gubernatorem Nowego Jorku?
- w której świątyni koronowany był król jerozolimski Baldwin I?
- source_sentence: Godło Republiki Ałtaju
sentences:
- co przedstawia godło Republiki Ałtaju?
- w którym kraju w noc sylwestrową je się oliebollen?
- który z członków załogi Międzynarodowej Stacji Kosmicznej nie ma nóg?
- source_sentence: co to jest meszne?
sentences:
- co to jest Mammoth Hot Springs?
- jak przebiegała kariera sportowa Witolda Sikorskiego?
- do uratowania ilu dzieł sztuki przyczynił się Borys Woźnicki?
- source_sentence: Chłopiec z Nariokotome
sentences:
- ile wynosiła objętość mózgu chłopca z Nariokotome?
- gdzie znajduje się czwarty polski cmentarz katyński?
- w jakich miejscach stał warszawski pomnik Ignacego Jana Paderewskiego?
model-index:
- name: snowflake-arctic-embed-m-klej-dyk
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.18509615384615385
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.4807692307692308
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.625
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7259615384615384
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.18509615384615385
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.16025641025641024
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.125
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07259615384615384
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.18509615384615385
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.4807692307692308
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.625
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.7259615384615384
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.44786216254546357
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.358972451159951
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3672210078826913
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.17548076923076922
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.47115384615384615
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6129807692307693
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7019230769230769
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.17548076923076922
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.15705128205128205
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.12259615384615384
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07019230769230768
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.17548076923076922
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.47115384615384615
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.6129807692307693
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.7019230769230769
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.43344535381311455
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3473920177045177
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3563798565478224
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.15625
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.4543269230769231
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5649038461538461
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6730769230769231
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.15625
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.15144230769230768
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.11298076923076923
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0673076923076923
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.15625
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.4543269230769231
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5649038461538461
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.6730769230769231
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4102597093872519
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.32613324175824177
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3350744652348361
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.16346153846153846
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.3918269230769231
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5072115384615384
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6057692307692307
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.16346153846153846
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.13060897435897434
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.10144230769230769
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.06057692307692307
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.16346153846153846
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.3918269230769231
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5072115384615384
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.6057692307692307
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.3757626519143444
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.30273962148962136
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3116992239855167
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.14903846153846154
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.3389423076923077
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.4182692307692308
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.49278846153846156
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.14903846153846154
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.11298076923076923
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.08365384615384615
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.04927884615384615
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.14903846153846154
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.3389423076923077
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.4182692307692308
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.49278846153846156
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.31783226267644227
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.26212320665445676
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.27044860532149884
name: Cosine Map@100
---
# snowflake-arctic-embed-m-klej-dyk
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m). 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:** [Snowflake/snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m) <!-- at revision 2ca412ec9505022eebd7d10286fbbad4b779f6e0 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0
### 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': 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 = [
'Chłopiec z Nariokotome',
'ile wynosiła objętość mózgu chłopca z Nariokotome?',
'gdzie znajduje się czwarty polski cmentarz katyński?',
]
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
#### Information Retrieval
* Dataset: `dim_768`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.1851 |
| cosine_accuracy@3 | 0.4808 |
| cosine_accuracy@5 | 0.625 |
| cosine_accuracy@10 | 0.726 |
| cosine_precision@1 | 0.1851 |
| cosine_precision@3 | 0.1603 |
| cosine_precision@5 | 0.125 |
| cosine_precision@10 | 0.0726 |
| cosine_recall@1 | 0.1851 |
| cosine_recall@3 | 0.4808 |
| cosine_recall@5 | 0.625 |
| cosine_recall@10 | 0.726 |
| cosine_ndcg@10 | 0.4479 |
| cosine_mrr@10 | 0.359 |
| **cosine_map@100** | **0.3672** |
#### Information Retrieval
* Dataset: `dim_512`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.1755 |
| cosine_accuracy@3 | 0.4712 |
| cosine_accuracy@5 | 0.613 |
| cosine_accuracy@10 | 0.7019 |
| cosine_precision@1 | 0.1755 |
| cosine_precision@3 | 0.1571 |
| cosine_precision@5 | 0.1226 |
| cosine_precision@10 | 0.0702 |
| cosine_recall@1 | 0.1755 |
| cosine_recall@3 | 0.4712 |
| cosine_recall@5 | 0.613 |
| cosine_recall@10 | 0.7019 |
| cosine_ndcg@10 | 0.4334 |
| cosine_mrr@10 | 0.3474 |
| **cosine_map@100** | **0.3564** |
#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.1562 |
| cosine_accuracy@3 | 0.4543 |
| cosine_accuracy@5 | 0.5649 |
| cosine_accuracy@10 | 0.6731 |
| cosine_precision@1 | 0.1562 |
| cosine_precision@3 | 0.1514 |
| cosine_precision@5 | 0.113 |
| cosine_precision@10 | 0.0673 |
| cosine_recall@1 | 0.1562 |
| cosine_recall@3 | 0.4543 |
| cosine_recall@5 | 0.5649 |
| cosine_recall@10 | 0.6731 |
| cosine_ndcg@10 | 0.4103 |
| cosine_mrr@10 | 0.3261 |
| **cosine_map@100** | **0.3351** |
#### Information Retrieval
* Dataset: `dim_128`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.1635 |
| cosine_accuracy@3 | 0.3918 |
| cosine_accuracy@5 | 0.5072 |
| cosine_accuracy@10 | 0.6058 |
| cosine_precision@1 | 0.1635 |
| cosine_precision@3 | 0.1306 |
| cosine_precision@5 | 0.1014 |
| cosine_precision@10 | 0.0606 |
| cosine_recall@1 | 0.1635 |
| cosine_recall@3 | 0.3918 |
| cosine_recall@5 | 0.5072 |
| cosine_recall@10 | 0.6058 |
| cosine_ndcg@10 | 0.3758 |
| cosine_mrr@10 | 0.3027 |
| **cosine_map@100** | **0.3117** |
#### Information Retrieval
* Dataset: `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.149 |
| cosine_accuracy@3 | 0.3389 |
| cosine_accuracy@5 | 0.4183 |
| cosine_accuracy@10 | 0.4928 |
| cosine_precision@1 | 0.149 |
| cosine_precision@3 | 0.113 |
| cosine_precision@5 | 0.0837 |
| cosine_precision@10 | 0.0493 |
| cosine_recall@1 | 0.149 |
| cosine_recall@3 | 0.3389 |
| cosine_recall@5 | 0.4183 |
| cosine_recall@10 | 0.4928 |
| cosine_ndcg@10 | 0.3178 |
| cosine_mrr@10 | 0.2621 |
| **cosine_map@100** | **0.2704** |
<!--
## 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
#### Unnamed Dataset
* Size: 3,738 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
| | positive | anchor |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 94.61 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 30.71 tokens</li><li>max: 76 tokens</li></ul> |
* Samples:
| positive | anchor |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Marsz Ochotników (chin.</code> | <code>kto jest kompozytorem chińskiego hymnu narodowego Marsz Ochotników?</code> |
| <code>Wybrane przykłady: Święta Rodzina – Maryja z Dzieciątkiem na ręku, niekiedy obok niej stoi św. Józef Rodzina Marii – przedstawienie w którym pojawia się Święta Rodzina oraz postaci spokrewnione z Marią. Maria w połogu (Maria in puerperio) – leżąca na łożu Maria opiekuje się Dzieciątkiem Maria karmiąca (Maria lactans) – Maria karmiąca swą piersią Dzieciątko Orantka – kobieta modląca się z podniesionymi rękami (częsty motyw ikon wschodnich); Sacra Conversazione – Matka Boska tronująca z Dzieciątkiem, otoczona stojącymi postaciami świętych Pietà – opłakująca Jezusa, trzymając na kolanach jego ciało po śmierci na krzyżu; Hodegetria – ujęcie popiersia Maryi, trzymającej na rękach małego Jezusa, częsty motyw w ikonach Eleusa – formalnie podobne do przedstawienia Hodegetrii lecz Maryja policzkiem przytula się do policzka Jezusa Immaculata – Niepokalane Poczęcie Najświętszej Maryi Panny.</code> | <code>kto zamiast Maryi trzyma nowonarodzonego Jezusa w scenie Bożego Narodzenia przedstawionej na poliptyku z Marią i Dzieciątkiem Jezus?</code> |
| <code>Pomnik Josepha von Eichendorffa w Brzeziu Pomnik Josepha von Eichendorffa – odtworzony w 2006 roku pomnik znanego niemieckiego poety epoki romantyzmu związanego z ziemią raciborską, Josepha von Eichendorffa.</code> | <code>po ilu latach odtworzono wysadzony w 1945 roku pomnik Josepha von Eichendorffa w Raciborzu-Brzeziu?</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
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 5
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `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`: 16
- `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`: 5
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `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
- `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`: True
- `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`: True
- `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_fused
- `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_eval_metrics`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.0684 | 1 | 9.3155 | - | - | - | - | - |
| 0.1368 | 2 | 9.1788 | - | - | - | - | - |
| 0.2051 | 3 | 8.8387 | - | - | - | - | - |
| 0.2735 | 4 | 8.2961 | - | - | - | - | - |
| 0.3419 | 5 | 8.0242 | - | - | - | - | - |
| 0.4103 | 6 | 7.2329 | - | - | - | - | - |
| 0.4786 | 7 | 5.4386 | - | - | - | - | - |
| 0.5470 | 8 | 6.1186 | - | - | - | - | - |
| 0.6154 | 9 | 4.9714 | - | - | - | - | - |
| 0.6838 | 10 | 5.1958 | - | - | - | - | - |
| 0.7521 | 11 | 5.1135 | - | - | - | - | - |
| 0.8205 | 12 | 4.6971 | - | - | - | - | - |
| 0.8889 | 13 | 4.5559 | - | - | - | - | - |
| 0.9573 | 14 | 3.9357 | 0.2842 | 0.3098 | 0.3191 | 0.2238 | 0.3209 |
| 1.0256 | 15 | 3.7916 | - | - | - | - | - |
| 1.0940 | 16 | 3.6393 | - | - | - | - | - |
| 1.1624 | 17 | 3.7733 | - | - | - | - | - |
| 1.2308 | 18 | 3.6974 | - | - | - | - | - |
| 1.2991 | 19 | 3.5964 | - | - | - | - | - |
| 1.3675 | 20 | 3.4118 | - | - | - | - | - |
| 1.4359 | 21 | 3.2022 | - | - | - | - | - |
| 1.5043 | 22 | 2.8133 | - | - | - | - | - |
| 1.5726 | 23 | 3.0871 | - | - | - | - | - |
| 1.6410 | 24 | 2.9559 | - | - | - | - | - |
| 1.7094 | 25 | 2.8192 | - | - | - | - | - |
| 1.7778 | 26 | 3.462 | - | - | - | - | - |
| 1.8462 | 27 | 3.1435 | - | - | - | - | - |
| 1.9145 | 28 | 2.8001 | - | - | - | - | - |
| 1.9829 | 29 | 2.5643 | 0.3134 | 0.3359 | 0.3563 | 0.2588 | 0.3671 |
| 2.0513 | 30 | 2.4295 | - | - | - | - | - |
| 2.1197 | 31 | 2.3892 | - | - | - | - | - |
| 2.1880 | 32 | 2.5228 | - | - | - | - | - |
| 2.2564 | 33 | 2.4906 | - | - | - | - | - |
| 2.3248 | 34 | 2.5358 | - | - | - | - | - |
| 2.3932 | 35 | 2.2806 | - | - | - | - | - |
| 2.4615 | 36 | 2.0083 | - | - | - | - | - |
| 2.5299 | 37 | 2.5088 | - | - | - | - | - |
| 2.5983 | 38 | 2.0628 | - | - | - | - | - |
| 2.6667 | 39 | 2.193 | - | - | - | - | - |
| 2.7350 | 40 | 2.4783 | - | - | - | - | - |
| 2.8034 | 41 | 2.382 | - | - | - | - | - |
| 2.8718 | 42 | 2.2017 | - | - | - | - | - |
| 2.9402 | 43 | 1.9739 | 0.3111 | 0.3392 | 0.3572 | 0.2657 | 0.3659 |
| 3.0085 | 44 | 2.0332 | - | - | - | - | - |
| 3.0769 | 45 | 1.9983 | - | - | - | - | - |
| 3.1453 | 46 | 1.8612 | - | - | - | - | - |
| 3.2137 | 47 | 1.9897 | - | - | - | - | - |
| 3.2821 | 48 | 2.2514 | - | - | - | - | - |
| 3.3504 | 49 | 2.0092 | - | - | - | - | - |
| 3.4188 | 50 | 1.7399 | - | - | - | - | - |
| 3.4872 | 51 | 1.5825 | - | - | - | - | - |
| 3.5556 | 52 | 2.1501 | - | - | - | - | - |
| 3.6239 | 53 | 1.4505 | - | - | - | - | - |
| 3.6923 | 54 | 1.8575 | - | - | - | - | - |
| 3.7607 | 55 | 2.3882 | - | - | - | - | - |
| 3.8291 | 56 | 2.1119 | - | - | - | - | - |
| 3.8974 | 57 | 1.8992 | - | - | - | - | - |
| 3.9658 | 58 | 1.8323 | 0.3117 | 0.3365 | 0.3558 | 0.2683 | 0.3670 |
| 4.0342 | 59 | 1.5938 | - | - | - | - | - |
| 4.1026 | 60 | 1.552 | - | - | - | - | - |
| 4.1709 | 61 | 1.907 | - | - | - | - | - |
| 4.2393 | 62 | 1.8304 | - | - | - | - | - |
| 4.3077 | 63 | 1.8775 | - | - | - | - | - |
| 4.3761 | 64 | 1.8654 | - | - | - | - | - |
| 4.4444 | 65 | 1.7944 | - | - | - | - | - |
| 4.5128 | 66 | 1.8335 | - | - | - | - | - |
| 4.5812 | 67 | 1.8823 | - | - | - | - | - |
| 4.6496 | 68 | 1.6479 | - | - | - | - | - |
| 4.7179 | 69 | 1.5771 | - | - | - | - | - |
| **4.7863** | **70** | **2.1911** | **0.3117** | **0.3351** | **0.3564** | **0.2704** | **0.3672** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.12.2
- Sentence Transformers: 3.0.0
- Transformers: 4.41.2
- PyTorch: 2.3.1
- Accelerate: 0.27.2
- Datasets: 2.19.1
- 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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