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sitges2608bai-4ep

Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:4173
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
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---
base_model: BAAI/bge-m3
datasets: []
language: []
library_name: sentence-transformers
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
- generated_from_trainer
- dataset_size:4173
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Si dins el termini que s'hagi atorgat amb aquesta finalitat els
habitatges que en disposen no s'han adaptat, la llicència pot ésser revocada.
sentences:
- Qui pot sol·licitar la pròrroga de la prestació?
- Quin és el resultat de la constatació dels fets denunciats per part de l'Ajuntament?
- Què passa si no s'adapten els habitatges d'ús turístic dins el termini establert?
- source_sentence: En cas que a la sepultura hi hagi despulles, la persona titular
podrà triar entre traslladar-les a una altra sepultura de la què en sigui el/la
titular o bé que l'Ajuntament les traslladi a l'ossera general.
sentences:
- Què passa amb les despulles si la persona titular decideix traslladar-les a una
altra sepultura?
- Quins són els beneficis de la llicència de publicitat dinàmica?
- Quan es va aprovar els models d'aval per part de la Junta de Govern Local?
- source_sentence: La colònia felina té un paper important en la reducció del nombre
d'animals abandonats, ja que proporciona un refugi segur i un entorn adequat per
als animals que es troben en situació de risc o abandonament.
sentences:
- Quin és el termini per justificar la realització del projecte/activitat subvencionada?
- Quins són els tractaments mèdics que beneficien la salut de l'empleat municipal?
- Quin és el paper de la colònia felina en la reducció del nombre d'animals abandonats?
- source_sentence: 'La realització de les obres que s’indiquen a continuació està
subjecta a l’obtenció d’una llicència d’obra major atorgada per l’Ajuntament:
... Compartimentació de naus industrials existents...'
sentences:
- Quin tipus d’obra es refereix a la compartimentació de naus industrials existents?
- Quin és el benefici principal del tràmit de canvi de titular de la llicència de
gual?
- Quin és el tipus de garantia que es pot fer mitjançant una assegurança de caució?
- source_sentence: Els membres de la Corporació tenen dret a obtenir dels òrgans de
l'Ajuntament les dades o informacions...
sentences:
- Quin és el paper dels òrgans de l'Ajuntament en relació amb les sol·licituds dels
membres de la Corporació?
- Quin és el motiu principal perquè un beneficiari pugui perdre el dret a una subvenció?
- Quin és el benefici de la presentació de recursos?
model-index:
- name: SentenceTransformer based on BAAI/bge-m3
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.07543103448275862
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.14439655172413793
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.21336206896551724
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.3900862068965517
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.07543103448275862
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.048132183908045974
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.04267241379310344
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.039008620689655174
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.07543103448275862
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.14439655172413793
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.21336206896551724
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.3900862068965517
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.19775448839983267
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.14087729200875768
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.1670966505747688
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.07543103448275862
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.1400862068965517
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.20905172413793102
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.3922413793103448
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.07543103448275862
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.046695402298850566
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.04181034482758621
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.03922413793103448
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.07543103448275862
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.1400862068965517
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.20905172413793102
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.3922413793103448
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.1973388128367381
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.14006910235358525
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.1660059682423787
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.07112068965517242
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.14439655172413793
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.20905172413793102
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.3793103448275862
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.07112068965517242
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.048132183908045974
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.04181034482758621
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.03793103448275861
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.07112068965517242
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.14439655172413793
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.20905172413793102
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.3793103448275862
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.19451734912520316
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.13957307060755345
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.1658323397622155
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.06465517241379311
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.13793103448275862
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.21336206896551724
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.3577586206896552
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.06465517241379311
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.04597701149425287
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.04267241379310345
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.03577586206896552
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.06465517241379311
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.13793103448275862
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.21336206896551724
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.3577586206896552
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.18381656342161204
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.13181616037219498
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.15919561658705733
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.06896551724137931
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.13577586206896552
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.20905172413793102
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.35344827586206895
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.06896551724137931
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.04525862068965517
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.041810344827586214
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.03534482758620689
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.06896551724137931
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.13577586206896552
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.20905172413793102
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.35344827586206895
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.18256713591724985
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.131704980842912
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.1580121500031178
name: Cosine Map@100
---
# SentenceTransformer based on BAAI/bge-m3
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3). It maps sentences & paragraphs to a 1024-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:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, '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("adriansanz/sitges2608bai-4ep")
# Run inference
sentences = [
"Els membres de la Corporació tenen dret a obtenir dels òrgans de l'Ajuntament les dades o informacions...",
"Quin és el paper dels òrgans de l'Ajuntament en relació amb les sol·licituds dels membres de la Corporació?",
'Quin és el benefici de la presentació de recursos?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# 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 | 0.0754 |
| cosine_accuracy@3 | 0.1444 |
| cosine_accuracy@5 | 0.2134 |
| cosine_accuracy@10 | 0.3901 |
| cosine_precision@1 | 0.0754 |
| cosine_precision@3 | 0.0481 |
| cosine_precision@5 | 0.0427 |
| cosine_precision@10 | 0.039 |
| cosine_recall@1 | 0.0754 |
| cosine_recall@3 | 0.1444 |
| cosine_recall@5 | 0.2134 |
| cosine_recall@10 | 0.3901 |
| cosine_ndcg@10 | 0.1978 |
| cosine_mrr@10 | 0.1409 |
| **cosine_map@100** | **0.1671** |
#### 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 | 0.0754 |
| cosine_accuracy@3 | 0.1401 |
| cosine_accuracy@5 | 0.2091 |
| cosine_accuracy@10 | 0.3922 |
| cosine_precision@1 | 0.0754 |
| cosine_precision@3 | 0.0467 |
| cosine_precision@5 | 0.0418 |
| cosine_precision@10 | 0.0392 |
| cosine_recall@1 | 0.0754 |
| cosine_recall@3 | 0.1401 |
| cosine_recall@5 | 0.2091 |
| cosine_recall@10 | 0.3922 |
| cosine_ndcg@10 | 0.1973 |
| cosine_mrr@10 | 0.1401 |
| **cosine_map@100** | **0.166** |
#### 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 | 0.0711 |
| cosine_accuracy@3 | 0.1444 |
| cosine_accuracy@5 | 0.2091 |
| cosine_accuracy@10 | 0.3793 |
| cosine_precision@1 | 0.0711 |
| cosine_precision@3 | 0.0481 |
| cosine_precision@5 | 0.0418 |
| cosine_precision@10 | 0.0379 |
| cosine_recall@1 | 0.0711 |
| cosine_recall@3 | 0.1444 |
| cosine_recall@5 | 0.2091 |
| cosine_recall@10 | 0.3793 |
| cosine_ndcg@10 | 0.1945 |
| cosine_mrr@10 | 0.1396 |
| **cosine_map@100** | **0.1658** |
#### 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 | 0.0647 |
| cosine_accuracy@3 | 0.1379 |
| cosine_accuracy@5 | 0.2134 |
| cosine_accuracy@10 | 0.3578 |
| cosine_precision@1 | 0.0647 |
| cosine_precision@3 | 0.046 |
| cosine_precision@5 | 0.0427 |
| cosine_precision@10 | 0.0358 |
| cosine_recall@1 | 0.0647 |
| cosine_recall@3 | 0.1379 |
| cosine_recall@5 | 0.2134 |
| cosine_recall@10 | 0.3578 |
| cosine_ndcg@10 | 0.1838 |
| cosine_mrr@10 | 0.1318 |
| **cosine_map@100** | **0.1592** |
#### 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 | 0.069 |
| cosine_accuracy@3 | 0.1358 |
| cosine_accuracy@5 | 0.2091 |
| cosine_accuracy@10 | 0.3534 |
| cosine_precision@1 | 0.069 |
| cosine_precision@3 | 0.0453 |
| cosine_precision@5 | 0.0418 |
| cosine_precision@10 | 0.0353 |
| cosine_recall@1 | 0.069 |
| cosine_recall@3 | 0.1358 |
| cosine_recall@5 | 0.2091 |
| cosine_recall@10 | 0.3534 |
| cosine_ndcg@10 | 0.1826 |
| cosine_mrr@10 | 0.1317 |
| **cosine_map@100** | **0.158** |
<!--
## 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
#### Unnamed Dataset
* Size: 4,173 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: 8 tokens</li><li>mean: 48.65 tokens</li><li>max: 125 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 20.96 tokens</li><li>max: 45 tokens</li></ul> |
* Samples:
| positive | anchor |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------|
| <code>Quan es produeix la caducitat del dret funerari per haver transcorregut el termini de concessió i un cop que l'Ajuntament hagi resolt el procediment legalment establert per a la declaració de caducitat, és imprescindible formalitzar la nova concessió del dret.</code> | <code>Quan es produeix la caducitat del dret funerari?</code> |
| <code>Les persones beneficiàries de l'ajut per a la creació de noves empreses per persones donades d'alta al règim especial de treballadors autònoms.</code> | <code>Quin és el tipus de persones que poden beneficiar-se de l'ajut?</code> |
| <code>Les entitats beneficiàries són les responsables de la gestió dels recursos econòmics i materials assignats per a la realització del projecte o activitat subvencionat.</code> | <code>Quin és el paper de les entitats beneficiàries en la gestió dels recursos?</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`: 2
- `per_device_eval_batch_size`: 2
- `gradient_accumulation_steps`: 2
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: False
- `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`: 2
- `per_device_eval_batch_size`: 2
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 2
- `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`: 4
- `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`: False
- `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
- `eval_on_start`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| 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 |
|:----------:|:--------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.0096 | 10 | 0.4269 | - | - | - | - | - |
| 0.0192 | 20 | 0.2328 | - | - | - | - | - |
| 0.0287 | 30 | 0.2803 | - | - | - | - | - |
| 0.0383 | 40 | 0.312 | - | - | - | - | - |
| 0.0479 | 50 | 0.0631 | - | - | - | - | - |
| 0.0575 | 60 | 0.1824 | - | - | - | - | - |
| 0.0671 | 70 | 0.3102 | - | - | - | - | - |
| 0.0767 | 80 | 0.2966 | - | - | - | - | - |
| 0.0862 | 90 | 0.3715 | - | - | - | - | - |
| 0.0958 | 100 | 0.0719 | - | - | - | - | - |
| 0.1054 | 110 | 0.279 | - | - | - | - | - |
| 0.1150 | 120 | 0.0954 | - | - | - | - | - |
| 0.1246 | 130 | 0.4912 | - | - | - | - | - |
| 0.1342 | 140 | 0.2877 | - | - | - | - | - |
| 0.1437 | 150 | 0.1933 | - | - | - | - | - |
| 0.1533 | 160 | 0.5942 | - | - | - | - | - |
| 0.1629 | 170 | 0.1336 | - | - | - | - | - |
| 0.1725 | 180 | 0.1755 | - | - | - | - | - |
| 0.1821 | 190 | 0.1455 | - | - | - | - | - |
| 0.1917 | 200 | 0.4391 | - | - | - | - | - |
| 0.2012 | 210 | 0.0567 | - | - | - | - | - |
| 0.2108 | 220 | 0.2368 | - | - | - | - | - |
| 0.2204 | 230 | 0.0249 | - | - | - | - | - |
| 0.2300 | 240 | 0.0518 | - | - | - | - | - |
| 0.2396 | 250 | 0.015 | - | - | - | - | - |
| 0.2492 | 260 | 0.4096 | - | - | - | - | - |
| 0.2587 | 270 | 0.115 | - | - | - | - | - |
| 0.2683 | 280 | 0.0532 | - | - | - | - | - |
| 0.2779 | 290 | 0.0407 | - | - | - | - | - |
| 0.2875 | 300 | 0.082 | - | - | - | - | - |
| 0.2971 | 310 | 0.1086 | - | - | - | - | - |
| 0.3067 | 320 | 0.0345 | - | - | - | - | - |
| 0.3162 | 330 | 0.3144 | - | - | - | - | - |
| 0.3258 | 340 | 0.0056 | - | - | - | - | - |
| 0.3354 | 350 | 0.0867 | - | - | - | - | - |
| 0.3450 | 360 | 0.1011 | - | - | - | - | - |
| 0.3546 | 370 | 0.6417 | - | - | - | - | - |
| 0.3642 | 380 | 0.0689 | - | - | - | - | - |
| 0.3737 | 390 | 0.0075 | - | - | - | - | - |
| 0.3833 | 400 | 0.0822 | - | - | - | - | - |
| 0.3929 | 410 | 0.098 | - | - | - | - | - |
| 0.4025 | 420 | 0.0442 | - | - | - | - | - |
| 0.4121 | 430 | 0.1759 | - | - | - | - | - |
| 0.4217 | 440 | 0.2625 | - | - | - | - | - |
| 0.4312 | 450 | 0.1123 | - | - | - | - | - |
| 0.4408 | 460 | 0.1174 | - | - | - | - | - |
| 0.4504 | 470 | 0.0529 | - | - | - | - | - |
| 0.4600 | 480 | 0.5396 | - | - | - | - | - |
| 0.4696 | 490 | 0.1985 | - | - | - | - | - |
| 0.4792 | 500 | 0.0016 | - | - | - | - | - |
| 0.4887 | 510 | 0.0496 | - | - | - | - | - |
| 0.4983 | 520 | 0.3138 | - | - | - | - | - |
| 0.5079 | 530 | 0.1974 | - | - | - | - | - |
| 0.5175 | 540 | 0.3489 | - | - | - | - | - |
| 0.5271 | 550 | 0.3332 | - | - | - | - | - |
| 0.5367 | 560 | 0.7838 | - | - | - | - | - |
| 0.5462 | 570 | 0.8335 | - | - | - | - | - |
| 0.5558 | 580 | 0.5018 | - | - | - | - | - |
| 0.5654 | 590 | 0.3391 | - | - | - | - | - |
| 0.5750 | 600 | 0.0055 | - | - | - | - | - |
| 0.5846 | 610 | 0.0264 | - | - | - | - | - |
| 0.5942 | 620 | 0.1397 | - | - | - | - | - |
| 0.6037 | 630 | 0.1114 | - | - | - | - | - |
| 0.6133 | 640 | 0.337 | - | - | - | - | - |
| 0.6229 | 650 | 0.0027 | - | - | - | - | - |
| 0.6325 | 660 | 0.1454 | - | - | - | - | - |
| 0.6421 | 670 | 0.2212 | - | - | - | - | - |
| 0.6517 | 680 | 0.0472 | - | - | - | - | - |
| 0.6612 | 690 | 0.6882 | - | - | - | - | - |
| 0.6708 | 700 | 0.0266 | - | - | - | - | - |
| 0.6804 | 710 | 1.0057 | - | - | - | - | - |
| 0.6900 | 720 | 0.1456 | - | - | - | - | - |
| 0.6996 | 730 | 0.4195 | - | - | - | - | - |
| 0.7092 | 740 | 0.0732 | - | - | - | - | - |
| 0.7187 | 750 | 0.0588 | - | - | - | - | - |
| 0.7283 | 760 | 0.0033 | - | - | - | - | - |
| 0.7379 | 770 | 0.0156 | - | - | - | - | - |
| 0.7475 | 780 | 0.0997 | - | - | - | - | - |
| 0.7571 | 790 | 0.856 | - | - | - | - | - |
| 0.7667 | 800 | 0.2394 | - | - | - | - | - |
| 0.7762 | 810 | 0.0322 | - | - | - | - | - |
| 0.7858 | 820 | 0.1821 | - | - | - | - | - |
| 0.7954 | 830 | 0.1883 | - | - | - | - | - |
| 0.8050 | 840 | 0.0994 | - | - | - | - | - |
| 0.8146 | 850 | 0.3889 | - | - | - | - | - |
| 0.8241 | 860 | 0.0221 | - | - | - | - | - |
| 0.8337 | 870 | 0.0106 | - | - | - | - | - |
| 0.8433 | 880 | 0.0031 | - | - | - | - | - |
| 0.8529 | 890 | 0.1453 | - | - | - | - | - |
| 0.8625 | 900 | 0.487 | - | - | - | - | - |
| 0.8721 | 910 | 0.2987 | - | - | - | - | - |
| 0.8816 | 920 | 0.0347 | - | - | - | - | - |
| 0.8912 | 930 | 0.2024 | - | - | - | - | - |
| 0.9008 | 940 | 0.0087 | - | - | - | - | - |
| 0.9104 | 950 | 0.3944 | - | - | - | - | - |
| 0.9200 | 960 | 0.0935 | - | - | - | - | - |
| 0.9296 | 970 | 0.2408 | - | - | - | - | - |
| 0.9391 | 980 | 0.1545 | - | - | - | - | - |
| 0.9487 | 990 | 0.1168 | - | - | - | - | - |
| 0.9583 | 1000 | 0.0051 | - | - | - | - | - |
| 0.9679 | 1010 | 0.681 | - | - | - | - | - |
| 0.9775 | 1020 | 0.0198 | - | - | - | - | - |
| 0.9871 | 1030 | 0.7243 | - | - | - | - | - |
| 0.9966 | 1040 | 0.0341 | - | - | - | - | - |
| 0.9995 | 1043 | - | 0.1608 | 0.1639 | 0.1678 | 0.1526 | 0.1610 |
| 1.0062 | 1050 | 0.001 | - | - | - | - | - |
| 1.0158 | 1060 | 0.0864 | - | - | - | - | - |
| 1.0254 | 1070 | 0.0209 | - | - | - | - | - |
| 1.0350 | 1080 | 0.2703 | - | - | - | - | - |
| 1.0446 | 1090 | 0.1857 | - | - | - | - | - |
| 1.0541 | 1100 | 0.0032 | - | - | - | - | - |
| 1.0637 | 1110 | 0.118 | - | - | - | - | - |
| 1.0733 | 1120 | 0.0029 | - | - | - | - | - |
| 1.0829 | 1130 | 0.0393 | - | - | - | - | - |
| 1.0925 | 1140 | 0.3103 | - | - | - | - | - |
| 1.1021 | 1150 | 0.0323 | - | - | - | - | - |
| 1.1116 | 1160 | 0.0925 | - | - | - | - | - |
| 1.1212 | 1170 | 0.0963 | - | - | - | - | - |
| 1.1308 | 1180 | 0.0481 | - | - | - | - | - |
| 1.1404 | 1190 | 0.0396 | - | - | - | - | - |
| 1.1500 | 1200 | 0.0033 | - | - | - | - | - |
| 1.1596 | 1210 | 0.1555 | - | - | - | - | - |
| 1.1691 | 1220 | 0.0938 | - | - | - | - | - |
| 1.1787 | 1230 | 0.1347 | - | - | - | - | - |
| 1.1883 | 1240 | 0.3057 | - | - | - | - | - |
| 1.1979 | 1250 | 0.0005 | - | - | - | - | - |
| 1.2075 | 1260 | 0.0634 | - | - | - | - | - |
| 1.2171 | 1270 | 0.0013 | - | - | - | - | - |
| 1.2266 | 1280 | 0.0012 | - | - | - | - | - |
| 1.2362 | 1290 | 0.0119 | - | - | - | - | - |
| 1.2458 | 1300 | 0.002 | - | - | - | - | - |
| 1.2554 | 1310 | 0.016 | - | - | - | - | - |
| 1.2650 | 1320 | 0.0169 | - | - | - | - | - |
| 1.2746 | 1330 | 0.0332 | - | - | - | - | - |
| 1.2841 | 1340 | 0.0076 | - | - | - | - | - |
| 1.2937 | 1350 | 0.0029 | - | - | - | - | - |
| 1.3033 | 1360 | 0.0011 | - | - | - | - | - |
| 1.3129 | 1370 | 0.0477 | - | - | - | - | - |
| 1.3225 | 1380 | 0.014 | - | - | - | - | - |
| 1.3321 | 1390 | 0.0002 | - | - | - | - | - |
| 1.3416 | 1400 | 0.012 | - | - | - | - | - |
| 1.3512 | 1410 | 0.0175 | - | - | - | - | - |
| 1.3608 | 1420 | 0.0088 | - | - | - | - | - |
| 1.3704 | 1430 | 0.0022 | - | - | - | - | - |
| 1.3800 | 1440 | 0.0007 | - | - | - | - | - |
| 1.3896 | 1450 | 0.0098 | - | - | - | - | - |
| 1.3991 | 1460 | 0.0003 | - | - | - | - | - |
| 1.4087 | 1470 | 0.0804 | - | - | - | - | - |
| 1.4183 | 1480 | 0.0055 | - | - | - | - | - |
| 1.4279 | 1490 | 0.1131 | - | - | - | - | - |
| 1.4375 | 1500 | 0.0018 | - | - | - | - | - |
| 1.4471 | 1510 | 0.0002 | - | - | - | - | - |
| 1.4566 | 1520 | 0.0143 | - | - | - | - | - |
| 1.4662 | 1530 | 0.0876 | - | - | - | - | - |
| 1.4758 | 1540 | 0.003 | - | - | - | - | - |
| 1.4854 | 1550 | 0.0087 | - | - | - | - | - |
| 1.4950 | 1560 | 0.0005 | - | - | - | - | - |
| 1.5046 | 1570 | 0.0002 | - | - | - | - | - |
| 1.5141 | 1580 | 0.1614 | - | - | - | - | - |
| 1.5237 | 1590 | 0.0017 | - | - | - | - | - |
| 1.5333 | 1600 | 0.0013 | - | - | - | - | - |
| 1.5429 | 1610 | 0.0041 | - | - | - | - | - |
| 1.5525 | 1620 | 0.0021 | - | - | - | - | - |
| 1.5621 | 1630 | 0.1113 | - | - | - | - | - |
| 1.5716 | 1640 | 0.0003 | - | - | - | - | - |
| 1.5812 | 1650 | 0.0003 | - | - | - | - | - |
| 1.5908 | 1660 | 0.0018 | - | - | - | - | - |
| 1.6004 | 1670 | 0.0004 | - | - | - | - | - |
| 1.6100 | 1680 | 0.0003 | - | - | - | - | - |
| 1.6195 | 1690 | 0.0017 | - | - | - | - | - |
| 1.6291 | 1700 | 0.0023 | - | - | - | - | - |
| 1.6387 | 1710 | 0.0167 | - | - | - | - | - |
| 1.6483 | 1720 | 0.0023 | - | - | - | - | - |
| 1.6579 | 1730 | 0.0095 | - | - | - | - | - |
| 1.6675 | 1740 | 0.0005 | - | - | - | - | - |
| 1.6770 | 1750 | 0.0014 | - | - | - | - | - |
| 1.6866 | 1760 | 0.0007 | - | - | - | - | - |
| 1.6962 | 1770 | 0.0014 | - | - | - | - | - |
| 1.7058 | 1780 | 0.0 | - | - | - | - | - |
| 1.7154 | 1790 | 0.0016 | - | - | - | - | - |
| 1.7250 | 1800 | 0.0004 | - | - | - | - | - |
| 1.7345 | 1810 | 0.0007 | - | - | - | - | - |
| 1.7441 | 1820 | 0.3356 | - | - | - | - | - |
| 1.7537 | 1830 | 0.001 | - | - | - | - | - |
| 1.7633 | 1840 | 0.0436 | - | - | - | - | - |
| 1.7729 | 1850 | 0.0839 | - | - | - | - | - |
| 1.7825 | 1860 | 0.0019 | - | - | - | - | - |
| 1.7920 | 1870 | 0.0406 | - | - | - | - | - |
| 1.8016 | 1880 | 0.0496 | - | - | - | - | - |
| 1.8112 | 1890 | 0.0164 | - | - | - | - | - |
| 1.8208 | 1900 | 0.0118 | - | - | - | - | - |
| 1.8304 | 1910 | 0.001 | - | - | - | - | - |
| 1.8400 | 1920 | 0.0004 | - | - | - | - | - |
| 1.8495 | 1930 | 0.002 | - | - | - | - | - |
| 1.8591 | 1940 | 0.0051 | - | - | - | - | - |
| 1.8687 | 1950 | 0.0624 | - | - | - | - | - |
| 1.8783 | 1960 | 0.0033 | - | - | - | - | - |
| 1.8879 | 1970 | 0.0001 | - | - | - | - | - |
| 1.8975 | 1980 | 0.1594 | - | - | - | - | - |
| 1.9070 | 1990 | 0.007 | - | - | - | - | - |
| 1.9166 | 2000 | 0.0002 | - | - | - | - | - |
| 1.9262 | 2010 | 0.0012 | - | - | - | - | - |
| 1.9358 | 2020 | 0.0011 | - | - | - | - | - |
| 1.9454 | 2030 | 0.0264 | - | - | - | - | - |
| 1.9550 | 2040 | 0.0004 | - | - | - | - | - |
| 1.9645 | 2050 | 0.008 | - | - | - | - | - |
| 1.9741 | 2060 | 0.1025 | - | - | - | - | - |
| 1.9837 | 2070 | 0.0745 | - | - | - | - | - |
| 1.9933 | 2080 | 0.006 | - | - | - | - | - |
| 2.0 | 2087 | - | 0.1609 | 0.1644 | 0.1708 | 0.1499 | 0.1696 |
| 2.0029 | 2090 | 0.001 | - | - | - | - | - |
| 2.0125 | 2100 | 0.0004 | - | - | - | - | - |
| 2.0220 | 2110 | 0.0003 | - | - | - | - | - |
| 2.0316 | 2120 | 0.0001 | - | - | - | - | - |
| 2.0412 | 2130 | 0.0003 | - | - | - | - | - |
| 2.0508 | 2140 | 0.0002 | - | - | - | - | - |
| 2.0604 | 2150 | 0.0006 | - | - | - | - | - |
| 2.0700 | 2160 | 0.04 | - | - | - | - | - |
| 2.0795 | 2170 | 0.0055 | - | - | - | - | - |
| 2.0891 | 2180 | 0.1454 | - | - | - | - | - |
| 2.0987 | 2190 | 0.0029 | - | - | - | - | - |
| 2.1083 | 2200 | 0.0006 | - | - | - | - | - |
| 2.1179 | 2210 | 0.0001 | - | - | - | - | - |
| 2.1275 | 2220 | 0.0129 | - | - | - | - | - |
| 2.1370 | 2230 | 0.0001 | - | - | - | - | - |
| 2.1466 | 2240 | 0.0003 | - | - | - | - | - |
| 2.1562 | 2250 | 0.4145 | - | - | - | - | - |
| 2.1658 | 2260 | 0.0048 | - | - | - | - | - |
| 2.1754 | 2270 | 0.0706 | - | - | - | - | - |
| 2.1850 | 2280 | 0.0026 | - | - | - | - | - |
| 2.1945 | 2290 | 0.008 | - | - | - | - | - |
| 2.2041 | 2300 | 0.0051 | - | - | - | - | - |
| 2.2137 | 2310 | 0.0307 | - | - | - | - | - |
| 2.2233 | 2320 | 0.0017 | - | - | - | - | - |
| 2.2329 | 2330 | 0.0005 | - | - | - | - | - |
| 2.2425 | 2340 | 0.0001 | - | - | - | - | - |
| 2.2520 | 2350 | 0.0001 | - | - | - | - | - |
| 2.2616 | 2360 | 0.0001 | - | - | - | - | - |
| 2.2712 | 2370 | 0.0461 | - | - | - | - | - |
| 2.2808 | 2380 | 0.0001 | - | - | - | - | - |
| 2.2904 | 2390 | 0.0003 | - | - | - | - | - |
| 2.3000 | 2400 | 0.001 | - | - | - | - | - |
| 2.3095 | 2410 | 0.0002 | - | - | - | - | - |
| 2.3191 | 2420 | 0.1568 | - | - | - | - | - |
| 2.3287 | 2430 | 0.0001 | - | - | - | - | - |
| 2.3383 | 2440 | 0.0005 | - | - | - | - | - |
| 2.3479 | 2450 | 0.0072 | - | - | - | - | - |
| 2.3575 | 2460 | 0.014 | - | - | - | - | - |
| 2.3670 | 2470 | 0.0003 | - | - | - | - | - |
| 2.3766 | 2480 | 0.0 | - | - | - | - | - |
| 2.3862 | 2490 | 0.0001 | - | - | - | - | - |
| 2.3958 | 2500 | 0.0008 | - | - | - | - | - |
| 2.4054 | 2510 | 0.0 | - | - | - | - | - |
| 2.4149 | 2520 | 0.0002 | - | - | - | - | - |
| 2.4245 | 2530 | 0.061 | - | - | - | - | - |
| 2.4341 | 2540 | 0.0005 | - | - | - | - | - |
| 2.4437 | 2550 | 0.0 | - | - | - | - | - |
| 2.4533 | 2560 | 0.0003 | - | - | - | - | - |
| 2.4629 | 2570 | 0.0095 | - | - | - | - | - |
| 2.4724 | 2580 | 0.0002 | - | - | - | - | - |
| 2.4820 | 2590 | 0.0 | - | - | - | - | - |
| 2.4916 | 2600 | 0.0003 | - | - | - | - | - |
| 2.5012 | 2610 | 0.0002 | - | - | - | - | - |
| 2.5108 | 2620 | 0.0035 | - | - | - | - | - |
| 2.5204 | 2630 | 0.0001 | - | - | - | - | - |
| 2.5299 | 2640 | 0.0 | - | - | - | - | - |
| 2.5395 | 2650 | 0.0017 | - | - | - | - | - |
| 2.5491 | 2660 | 0.0 | - | - | - | - | - |
| 2.5587 | 2670 | 0.0066 | - | - | - | - | - |
| 2.5683 | 2680 | 0.0004 | - | - | - | - | - |
| 2.5779 | 2690 | 0.0001 | - | - | - | - | - |
| 2.5874 | 2700 | 0.0 | - | - | - | - | - |
| 2.5970 | 2710 | 0.0 | - | - | - | - | - |
| 2.6066 | 2720 | 0.131 | - | - | - | - | - |
| 2.6162 | 2730 | 0.0001 | - | - | - | - | - |
| 2.6258 | 2740 | 0.0001 | - | - | - | - | - |
| 2.6354 | 2750 | 0.0001 | - | - | - | - | - |
| 2.6449 | 2760 | 0.0 | - | - | - | - | - |
| 2.6545 | 2770 | 0.0003 | - | - | - | - | - |
| 2.6641 | 2780 | 0.0095 | - | - | - | - | - |
| 2.6737 | 2790 | 0.0 | - | - | - | - | - |
| 2.6833 | 2800 | 0.0003 | - | - | - | - | - |
| 2.6929 | 2810 | 0.0001 | - | - | - | - | - |
| 2.7024 | 2820 | 0.0002 | - | - | - | - | - |
| 2.7120 | 2830 | 0.0007 | - | - | - | - | - |
| 2.7216 | 2840 | 0.0008 | - | - | - | - | - |
| 2.7312 | 2850 | 0.0 | - | - | - | - | - |
| 2.7408 | 2860 | 0.0002 | - | - | - | - | - |
| 2.7504 | 2870 | 0.0003 | - | - | - | - | - |
| 2.7599 | 2880 | 0.0062 | - | - | - | - | - |
| 2.7695 | 2890 | 0.0415 | - | - | - | - | - |
| 2.7791 | 2900 | 0.0002 | - | - | - | - | - |
| 2.7887 | 2910 | 0.0024 | - | - | - | - | - |
| 2.7983 | 2920 | 0.0022 | - | - | - | - | - |
| 2.8079 | 2930 | 0.0014 | - | - | - | - | - |
| 2.8174 | 2940 | 0.1301 | - | - | - | - | - |
| 2.8270 | 2950 | 0.0 | - | - | - | - | - |
| 2.8366 | 2960 | 0.0 | - | - | - | - | - |
| 2.8462 | 2970 | 0.0 | - | - | - | - | - |
| 2.8558 | 2980 | 0.0006 | - | - | - | - | - |
| 2.8654 | 2990 | 0.0 | - | - | - | - | - |
| 2.8749 | 3000 | 0.0235 | - | - | - | - | - |
| 2.8845 | 3010 | 0.0001 | - | - | - | - | - |
| 2.8941 | 3020 | 0.0285 | - | - | - | - | - |
| 2.9037 | 3030 | 0.0 | - | - | - | - | - |
| 2.9133 | 3040 | 0.0002 | - | - | - | - | - |
| 2.9229 | 3050 | 0.0 | - | - | - | - | - |
| 2.9324 | 3060 | 0.0005 | - | - | - | - | - |
| 2.9420 | 3070 | 0.0001 | - | - | - | - | - |
| 2.9516 | 3080 | 0.0011 | - | - | - | - | - |
| 2.9612 | 3090 | 0.0 | - | - | - | - | - |
| 2.9708 | 3100 | 0.0001 | - | - | - | - | - |
| 2.9804 | 3110 | 0.0046 | - | - | - | - | - |
| 2.9899 | 3120 | 0.0001 | - | - | - | - | - |
| **2.9995** | **3130** | **0.0005** | **0.1622** | **0.1647** | **0.1635** | **0.1564** | **0.1617** |
| 3.0091 | 3140 | 0.0 | - | - | - | - | - |
| 3.0187 | 3150 | 0.0 | - | - | - | - | - |
| 3.0283 | 3160 | 0.0 | - | - | - | - | - |
| 3.0379 | 3170 | 0.0002 | - | - | - | - | - |
| 3.0474 | 3180 | 0.0004 | - | - | - | - | - |
| 3.0570 | 3190 | 0.1022 | - | - | - | - | - |
| 3.0666 | 3200 | 0.0012 | - | - | - | - | - |
| 3.0762 | 3210 | 0.0001 | - | - | - | - | - |
| 3.0858 | 3220 | 0.0677 | - | - | - | - | - |
| 3.0954 | 3230 | 0.0 | - | - | - | - | - |
| 3.1049 | 3240 | 0.0002 | - | - | - | - | - |
| 3.1145 | 3250 | 0.0001 | - | - | - | - | - |
| 3.1241 | 3260 | 0.0005 | - | - | - | - | - |
| 3.1337 | 3270 | 0.0002 | - | - | - | - | - |
| 3.1433 | 3280 | 0.0 | - | - | - | - | - |
| 3.1529 | 3290 | 0.0021 | - | - | - | - | - |
| 3.1624 | 3300 | 0.0001 | - | - | - | - | - |
| 3.1720 | 3310 | 0.0077 | - | - | - | - | - |
| 3.1816 | 3320 | 0.0001 | - | - | - | - | - |
| 3.1912 | 3330 | 0.1324 | - | - | - | - | - |
| 3.2008 | 3340 | 0.0 | - | - | - | - | - |
| 3.2103 | 3350 | 0.1278 | - | - | - | - | - |
| 3.2199 | 3360 | 0.0001 | - | - | - | - | - |
| 3.2295 | 3370 | 0.0 | - | - | - | - | - |
| 3.2391 | 3380 | 0.0001 | - | - | - | - | - |
| 3.2487 | 3390 | 0.0001 | - | - | - | - | - |
| 3.2583 | 3400 | 0.0 | - | - | - | - | - |
| 3.2678 | 3410 | 0.0001 | - | - | - | - | - |
| 3.2774 | 3420 | 0.0 | - | - | - | - | - |
| 3.2870 | 3430 | 0.0001 | - | - | - | - | - |
| 3.2966 | 3440 | 0.0001 | - | - | - | - | - |
| 3.3062 | 3450 | 0.0001 | - | - | - | - | - |
| 3.3158 | 3460 | 0.0263 | - | - | - | - | - |
| 3.3253 | 3470 | 0.0001 | - | - | - | - | - |
| 3.3349 | 3480 | 0.0002 | - | - | - | - | - |
| 3.3445 | 3490 | 0.0003 | - | - | - | - | - |
| 3.3541 | 3500 | 0.0 | - | - | - | - | - |
| 3.3637 | 3510 | 0.0 | - | - | - | - | - |
| 3.3733 | 3520 | 0.0 | - | - | - | - | - |
| 3.3828 | 3530 | 0.0002 | - | - | - | - | - |
| 3.3924 | 3540 | 0.0001 | - | - | - | - | - |
| 3.4020 | 3550 | 0.0 | - | - | - | - | - |
| 3.4116 | 3560 | 0.0001 | - | - | - | - | - |
| 3.4212 | 3570 | 0.0001 | - | - | - | - | - |
| 3.4308 | 3580 | 0.0122 | - | - | - | - | - |
| 3.4403 | 3590 | 0.0 | - | - | - | - | - |
| 3.4499 | 3600 | 0.0001 | - | - | - | - | - |
| 3.4595 | 3610 | 0.0003 | - | - | - | - | - |
| 3.4691 | 3620 | 0.0 | - | - | - | - | - |
| 3.4787 | 3630 | 0.0 | - | - | - | - | - |
| 3.4883 | 3640 | 0.0001 | - | - | - | - | - |
| 3.4978 | 3650 | 0.0 | - | - | - | - | - |
| 3.5074 | 3660 | 0.0002 | - | - | - | - | - |
| 3.5170 | 3670 | 0.0004 | - | - | - | - | - |
| 3.5266 | 3680 | 0.0003 | - | - | - | - | - |
| 3.5362 | 3690 | 0.0004 | - | - | - | - | - |
| 3.5458 | 3700 | 0.0 | - | - | - | - | - |
| 3.5553 | 3710 | 0.0001 | - | - | - | - | - |
| 3.5649 | 3720 | 0.0001 | - | - | - | - | - |
| 3.5745 | 3730 | 0.0 | - | - | - | - | - |
| 3.5841 | 3740 | 0.0001 | - | - | - | - | - |
| 3.5937 | 3750 | 0.0003 | - | - | - | - | - |
| 3.6033 | 3760 | 0.0 | - | - | - | - | - |
| 3.6128 | 3770 | 0.0002 | - | - | - | - | - |
| 3.6224 | 3780 | 0.0 | - | - | - | - | - |
| 3.6320 | 3790 | 0.0 | - | - | - | - | - |
| 3.6416 | 3800 | 0.0 | - | - | - | - | - |
| 3.6512 | 3810 | 0.0 | - | - | - | - | - |
| 3.6608 | 3820 | 0.0 | - | - | - | - | - |
| 3.6703 | 3830 | 0.0 | - | - | - | - | - |
| 3.6799 | 3840 | 0.0001 | - | - | - | - | - |
| 3.6895 | 3850 | 0.0001 | - | - | - | - | - |
| 3.6991 | 3860 | 0.0002 | - | - | - | - | - |
| 3.7087 | 3870 | 0.0 | - | - | - | - | - |
| 3.7183 | 3880 | 0.0001 | - | - | - | - | - |
| 3.7278 | 3890 | 0.0002 | - | - | - | - | - |
| 3.7374 | 3900 | 0.0001 | - | - | - | - | - |
| 3.7470 | 3910 | 0.0003 | - | - | - | - | - |
| 3.7566 | 3920 | 0.0003 | - | - | - | - | - |
| 3.7662 | 3930 | 0.0021 | - | - | - | - | - |
| 3.7758 | 3940 | 0.0002 | - | - | - | - | - |
| 3.7853 | 3950 | 0.0001 | - | - | - | - | - |
| 3.7949 | 3960 | 0.0001 | - | - | - | - | - |
| 3.8045 | 3970 | 0.0001 | - | - | - | - | - |
| 3.8141 | 3980 | 0.0002 | - | - | - | - | - |
| 3.8237 | 3990 | 0.0001 | - | - | - | - | - |
| 3.8333 | 4000 | 0.0001 | - | - | - | - | - |
| 3.8428 | 4010 | 0.0001 | - | - | - | - | - |
| 3.8524 | 4020 | 0.0001 | - | - | - | - | - |
| 3.8620 | 4030 | 0.0 | - | - | - | - | - |
| 3.8716 | 4040 | 0.0003 | - | - | - | - | - |
| 3.8812 | 4050 | 0.0 | - | - | - | - | - |
| 3.8908 | 4060 | 0.002 | - | - | - | - | - |
| 3.9003 | 4070 | 0.0 | - | - | - | - | - |
| 3.9099 | 4080 | 0.0 | - | - | - | - | - |
| 3.9195 | 4090 | 0.0001 | - | - | - | - | - |
| 3.9291 | 4100 | 0.0 | - | - | - | - | - |
| 3.9387 | 4110 | 0.0 | - | - | - | - | - |
| 3.9483 | 4120 | 0.0 | - | - | - | - | - |
| 3.9578 | 4130 | 0.0 | - | - | - | - | - |
| 3.9674 | 4140 | 0.0 | - | - | - | - | - |
| 3.9770 | 4150 | 0.0 | - | - | - | - | - |
| 3.9866 | 4160 | 0.0004 | - | - | - | - | - |
| 3.9962 | 4170 | 0.0 | - | - | - | - | - |
| 3.9981 | 4172 | - | 0.1592 | 0.1658 | 0.1660 | 0.1580 | 0.1671 |
* The bold row denotes the saved checkpoint.
</details>
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.3.1+cu121
- Accelerate: 0.34.0.dev0
- Datasets: 2.21.0
- 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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