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---
base_model: projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base
datasets: []
language:
- ca
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
- generated_from_trainer
- dataset_size:4173
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 'Queixa: Deixar constància de la vostra disconformitat per un mal
servei (un tracte inapropiat, un temps d''espera excessiu, etc.), sense demanar
cap indemnització.'
sentences:
- Quin és el format de sortida del tràmit de baixa de la llicència de gual?
- Quin és el tipus de venda que es realitza en els mercats setmanals?
- Quin és el paper de la queixa en la resolució de conflictes?
- source_sentence: L'empleat que en l'exercici de les seves tasques tingui assignada
la funció de conducció de vehicles municipals, pot sol·licitar un ajut per les
despeses ocasionades per a la renovació del carnet de conduir (certificat mèdic
i administratiu).
sentences:
- Quin és el resultat esperat de les escoles que reben les subvencions?
- Quin és el requisit per obtenir una autorització d'estacionament?
- Quin és el requisit per a sol·licitar l'ajut social?
- source_sentence: Aportació de documentació. Subvencions per finançar despeses d'hipoteca,
subministrament i altres serveis i la manca d'ingressos de lloguer de les entitats
culturals
sentences:
- Quin és el propòsit de la documentació?
- Quin és el paper del públic assistent en el Ple Municipal?
- Quin és el paper de l'ajuntament en la renovació del carnet de persona cuidadora?
- source_sentence: la Fira de la Vila del Llibre de Sitges consistent en un conjunt
de parades instal·lades al Passeig Marítim
sentences:
- Quin és el paper de la llicència de parcel·lació en la construcció d'edificacions?
- Quin és l'objectiu del tràmit de participació en processos de selecció de personal
de l'Ajuntament?
- Quin és el lloc on es desenvolupa la Fira de la Vila del Llibre de Sitges?
- source_sentence: Mitjançant aquest tràmit la persona interessada posa en coneixement
de l'Ajuntament de Sitges l'inici d'un espectacle públic o activitat recreativa
de caràcter extraordinari...
sentences:
- Quin és el paper de la persona interessada en la llicència per a espectacles públics
o activitats recreatives de caràcter extraordinari?
- Quin és el paper del Registre de Sol·licitants d'Habitatge amb Protecció Oficial
en la gestió d'habitatges?
- Quin és el tipus de familiars que es tenen en compte per l'ajut especial?
model-index:
- name: BGE SITGES CAT
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.07327586206896551
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.15732758620689655
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.21767241379310345
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.39439655172413796
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.07327586206896551
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.05244252873563218
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.043534482758620686
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.03943965517241379
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.07327586206896551
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.15732758620689655
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.21767241379310345
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.39439655172413796
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.20125893142070614
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.14385604816639316
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.17098930660026063
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.07327586206896551
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.15086206896551724
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.21767241379310345
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.39439655172413796
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.07327586206896551
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.050287356321839075
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.04353448275862069
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.03943965517241379
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.07327586206896551
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.15086206896551724
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.21767241379310345
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.39439655172413796
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2016207682773376
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.14438799945265474
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.1715919733142084
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.07327586206896551
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.14870689655172414
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.21120689655172414
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.40086206896551724
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.07327586206896551
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.04956896551724138
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.04224137931034483
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.04008620689655173
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.07327586206896551
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.14870689655172414
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.21120689655172414
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.40086206896551724
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2021149795452301
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.1433856732348113
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.16973847535400444
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.06896551724137931
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.14655172413793102
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.21767241379310345
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.38146551724137934
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.06896551724137931
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.048850574712643674
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.04353448275862069
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.03814655172413793
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.06896551724137931
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.14655172413793102
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.21767241379310345
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.38146551724137934
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.19535554125135882
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.1398416119321293
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.16597320243564267
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.05603448275862069
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.13793103448275862
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.1939655172413793
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.36853448275862066
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.05603448275862069
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.04597701149425287
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.03879310344827586
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.03685344827586207
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.05603448275862069
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.13793103448275862
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.1939655172413793
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.36853448275862066
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.18225870966588442
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.12688492063492074
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.15425908300208627
name: Cosine Map@100
---
# BGE SITGES CAT
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base](https://huggingface.co/projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base). 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:** [projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base](https://huggingface.co/projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base) <!-- at revision 3354aea2cb9d91091495e9f1e1241b488f32e47c -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** ca
- **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': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("adriansanz/SITGES-aina4_moreseq")
# Run inference
sentences = [
"Mitjançant aquest tràmit la persona interessada posa en coneixement de l'Ajuntament de Sitges l'inici d'un espectacle públic o activitat recreativa de caràcter extraordinari...",
'Quin és el paper de la persona interessada en la llicència per a espectacles públics o activitats recreatives de caràcter extraordinari?',
"Quin és el paper del Registre de Sol·licitants d'Habitatge amb Protecció Oficial en la gestió d'habitatges?",
]
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 | 0.0733 |
| cosine_accuracy@3 | 0.1573 |
| cosine_accuracy@5 | 0.2177 |
| cosine_accuracy@10 | 0.3944 |
| cosine_precision@1 | 0.0733 |
| cosine_precision@3 | 0.0524 |
| cosine_precision@5 | 0.0435 |
| cosine_precision@10 | 0.0394 |
| cosine_recall@1 | 0.0733 |
| cosine_recall@3 | 0.1573 |
| cosine_recall@5 | 0.2177 |
| cosine_recall@10 | 0.3944 |
| cosine_ndcg@10 | 0.2013 |
| cosine_mrr@10 | 0.1439 |
| **cosine_map@100** | **0.171** |
#### 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 | 0.0733 |
| cosine_accuracy@3 | 0.1509 |
| cosine_accuracy@5 | 0.2177 |
| cosine_accuracy@10 | 0.3944 |
| cosine_precision@1 | 0.0733 |
| cosine_precision@3 | 0.0503 |
| cosine_precision@5 | 0.0435 |
| cosine_precision@10 | 0.0394 |
| cosine_recall@1 | 0.0733 |
| cosine_recall@3 | 0.1509 |
| cosine_recall@5 | 0.2177 |
| cosine_recall@10 | 0.3944 |
| cosine_ndcg@10 | 0.2016 |
| cosine_mrr@10 | 0.1444 |
| **cosine_map@100** | **0.1716** |
#### 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 | 0.0733 |
| cosine_accuracy@3 | 0.1487 |
| cosine_accuracy@5 | 0.2112 |
| cosine_accuracy@10 | 0.4009 |
| cosine_precision@1 | 0.0733 |
| cosine_precision@3 | 0.0496 |
| cosine_precision@5 | 0.0422 |
| cosine_precision@10 | 0.0401 |
| cosine_recall@1 | 0.0733 |
| cosine_recall@3 | 0.1487 |
| cosine_recall@5 | 0.2112 |
| cosine_recall@10 | 0.4009 |
| cosine_ndcg@10 | 0.2021 |
| cosine_mrr@10 | 0.1434 |
| **cosine_map@100** | **0.1697** |
#### 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 | 0.069 |
| cosine_accuracy@3 | 0.1466 |
| cosine_accuracy@5 | 0.2177 |
| cosine_accuracy@10 | 0.3815 |
| cosine_precision@1 | 0.069 |
| cosine_precision@3 | 0.0489 |
| cosine_precision@5 | 0.0435 |
| cosine_precision@10 | 0.0381 |
| cosine_recall@1 | 0.069 |
| cosine_recall@3 | 0.1466 |
| cosine_recall@5 | 0.2177 |
| cosine_recall@10 | 0.3815 |
| cosine_ndcg@10 | 0.1954 |
| cosine_mrr@10 | 0.1398 |
| **cosine_map@100** | **0.166** |
#### 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 | 0.056 |
| cosine_accuracy@3 | 0.1379 |
| cosine_accuracy@5 | 0.194 |
| cosine_accuracy@10 | 0.3685 |
| cosine_precision@1 | 0.056 |
| cosine_precision@3 | 0.046 |
| cosine_precision@5 | 0.0388 |
| cosine_precision@10 | 0.0369 |
| cosine_recall@1 | 0.056 |
| cosine_recall@3 | 0.1379 |
| cosine_recall@5 | 0.194 |
| cosine_recall@10 | 0.3685 |
| cosine_ndcg@10 | 0.1823 |
| cosine_mrr@10 | 0.1269 |
| **cosine_map@100** | **0.1543** |
<!--
## 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 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`: 6
- `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`: 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`: 6
- `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
| Epoch | Step | Training Loss | 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 |
|:----------:|:------:|:-------------:|:----------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.3065 | 5 | 3.3947 | - | - | - | - | - | - |
| 0.6130 | 10 | 2.6401 | - | - | - | - | - | - |
| 0.9195 | 15 | 2.0152 | - | - | - | - | - | - |
| 0.9808 | 16 | - | 1.3404 | 0.1639 | 0.1577 | 0.1694 | 0.1503 | 0.1638 |
| 1.2261 | 20 | 1.4542 | - | - | - | - | - | - |
| 1.5326 | 25 | 1.0135 | - | - | - | - | - | - |
| 1.8391 | 30 | 0.8437 | - | - | - | - | - | - |
| 1.9617 | 32 | - | 0.9436 | 0.1556 | 0.1596 | 0.1600 | 0.1467 | 0.1701 |
| 2.1456 | 35 | 0.7676 | - | - | - | - | - | - |
| 2.4521 | 40 | 0.5126 | - | - | - | - | - | - |
| 2.7586 | 45 | 0.4358 | - | - | - | - | - | - |
| 2.9425 | 48 | - | 0.7852 | 0.1650 | 0.1693 | 0.1720 | 0.1511 | 0.1686 |
| 3.0651 | 50 | 0.4192 | - | - | - | - | - | - |
| 3.3716 | 55 | 0.3429 | - | - | - | - | - | - |
| 3.6782 | 60 | 0.3025 | - | - | - | - | - | - |
| 3.9847 | 65 | 0.2863 | 0.7401 | 0.1646 | 0.1706 | 0.1759 | 0.1480 | 0.1694 |
| 4.2912 | 70 | 0.2474 | - | - | - | - | - | - |
| 4.5977 | 75 | 0.2324 | - | - | - | - | - | - |
| 4.9042 | 80 | 0.2344 | - | - | - | - | - | - |
| 4.9655 | 81 | - | 0.7217 | 0.1663 | 0.1699 | 0.1767 | 0.1512 | 0.1696 |
| 5.2107 | 85 | 0.2181 | - | - | - | - | - | - |
| 5.5172 | 90 | 0.2116 | - | - | - | - | - | - |
| 5.8238 | 95 | 0.1926 | - | - | - | - | - | - |
| **5.8851** | **96** | **-** | **0.7154** | **0.166** | **0.1697** | **0.1716** | **0.1543** | **0.171** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.3
- PyTorch: 2.3.1+cu121
- Accelerate: 0.32.1
- Datasets: 2.20.0
- 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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