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---
base_model: cross-encoder/ms-marco-MiniLM-L-4-v2
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: 'Aquelles persones (físiques o jurídiques) que es disposin a exercir
una de les següents activitats: ... Han de comunicar-ho a l''Ajuntament prèviament
a la data prevista de la seva obertura.'
sentences:
- Quin és el benefici que es pretén obtenir amb aquests ajuts econòmics per a les
empreses d'hostaleria i restauració?
- Quin és el benefici del sistema de teleassistència per a les persones que viuen
amb altres persones amb discapacitat?
- Quin és el propòsit de la comunicació prèvia d'una activitat recreativa o un espectacle
públic?
- source_sentence: Les persones titulars d’activitats que generin residus comercials
o industrials assimilables als municipals, vindran obligats a acreditar davant
l’Ajuntament que tenen contractat un gestor autoritzat per la recollida, tractament
i eliminació dels residus que produeixi l’activitat corresponent.
sentences:
- Quin és el paper de l'Ajuntament en l'acreditació de recollida de residus?
- Quin és el benefici de les activitats d'animació socio-cultural?
- Quin és el benefici de l'ajut per a la creació de noves empreses?
- source_sentence: Modificació de sol·licitud de permís d'ocupació de la via pública
per filmacions, rodatges o sessions fotogràfiques.
sentences:
- Quin és el grau de discapacitat mínim per a rebre l'ajut de 300€ anuals?
- Quin és el requisit per a la constitució o modificació del règim de propietat
horitzontal?
- Quin és el tipus de permís que es modifica?
- source_sentence: El beneficiari és l'encarregat de complir les condicions de la
subvenció i de presentar els informes de seguiment del projecte.
sentences:
- Quin és el paper del beneficiari en el procés de subvencions?
- Quin és el càlcul dels interessos de demora en el fraccionament i l'ajornament?
- Quin és el període de temps en què es poden efectuar les despeses mèdiques per
a rebre l'ajuda?
- source_sentence: Aquest tràmit permet sol·licitar la llicència per a realitzar obres
d'excavació a la via pública per a la instal·lació o reparació d'infraestructures
de serveis i subministraments.
sentences:
- Quin és el paper de la via pública en aquest tràmit?
- Quin és el requisit principal per obtenir el certificat?
- Quin és l'objectiu de presentar una denúncia per presumpta infracció urbanística?
model-index:
- name: SentenceTransformer based on cross-encoder/ms-marco-MiniLM-L-4-v2
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.03879310344827586
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.08836206896551724
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.12284482758620689
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.1875
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.03879310344827586
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.029454022988505746
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.024568965517241383
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.01875
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.03879310344827586
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.08836206896551724
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.12284482758620689
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.1875
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.10242929123982092
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.0765787493158183
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.09057915519577017
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.03879310344827586
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.08836206896551724
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.12284482758620689
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.1875
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.03879310344827586
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.029454022988505746
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.024568965517241383
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.01875
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.03879310344827586
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.08836206896551724
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.12284482758620689
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.1875
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.10242929123982092
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.0765787493158183
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.09057915519577017
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.03879310344827586
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.08405172413793104
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.12931034482758622
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.1853448275862069
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.03879310344827586
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.028017241379310345
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.02586206896551724
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.01853448275862069
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.03879310344827586
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.08405172413793104
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.12931034482758622
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.1853448275862069
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.10210354316803219
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.07668821839080461
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.08986051867751395
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.034482758620689655
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.09482758620689655
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.1271551724137931
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.1853448275862069
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.034482758620689655
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.03160919540229885
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.025431034482758623
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.018534482758620692
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.034482758620689655
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.09482758620689655
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.1271551724137931
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.1853448275862069
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.10100148719550092
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.07532755199781063
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.08992740582596234
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.034482758620689655
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.08405172413793104
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.10344827586206896
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.17025862068965517
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.034482758620689655
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.028017241379310345
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.020689655172413796
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.017025862068965517
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.034482758620689655
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.08405172413793104
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.10344827586206896
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.17025862068965517
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.09332320019708457
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.0700302750410509
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.08369264546406543
name: Cosine Map@100
---
# SentenceTransformer based on cross-encoder/ms-marco-MiniLM-L-4-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [cross-encoder/ms-marco-MiniLM-L-4-v2](https://huggingface.co./cross-encoder/ms-marco-MiniLM-L-4-v2). It maps sentences & paragraphs to a 384-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:** [cross-encoder/ms-marco-MiniLM-L-4-v2](https://huggingface.co./cross-encoder/ms-marco-MiniLM-L-4-v2) <!-- at revision 1f1ab0943a42a52afd702e7e8337bec985c189ea -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 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': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, '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/sitges10242608-4ep-rerankv2")
# Run inference
sentences = [
"Aquest tràmit permet sol·licitar la llicència per a realitzar obres d'excavació a la via pública per a la instal·lació o reparació d'infraestructures de serveis i subministraments.",
'Quin és el paper de la via pública en aquest tràmit?',
"Quin és l'objectiu de presentar una denúncia per presumpta infracció urbanística?",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# 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 | 0.0388 |
| cosine_accuracy@3 | 0.0884 |
| cosine_accuracy@5 | 0.1228 |
| cosine_accuracy@10 | 0.1875 |
| cosine_precision@1 | 0.0388 |
| cosine_precision@3 | 0.0295 |
| cosine_precision@5 | 0.0246 |
| cosine_precision@10 | 0.0187 |
| cosine_recall@1 | 0.0388 |
| cosine_recall@3 | 0.0884 |
| cosine_recall@5 | 0.1228 |
| cosine_recall@10 | 0.1875 |
| cosine_ndcg@10 | 0.1024 |
| cosine_mrr@10 | 0.0766 |
| **cosine_map@100** | **0.0906** |
#### 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 | 0.0388 |
| cosine_accuracy@3 | 0.0884 |
| cosine_accuracy@5 | 0.1228 |
| cosine_accuracy@10 | 0.1875 |
| cosine_precision@1 | 0.0388 |
| cosine_precision@3 | 0.0295 |
| cosine_precision@5 | 0.0246 |
| cosine_precision@10 | 0.0187 |
| cosine_recall@1 | 0.0388 |
| cosine_recall@3 | 0.0884 |
| cosine_recall@5 | 0.1228 |
| cosine_recall@10 | 0.1875 |
| cosine_ndcg@10 | 0.1024 |
| cosine_mrr@10 | 0.0766 |
| **cosine_map@100** | **0.0906** |
#### 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 | 0.0388 |
| cosine_accuracy@3 | 0.0841 |
| cosine_accuracy@5 | 0.1293 |
| cosine_accuracy@10 | 0.1853 |
| cosine_precision@1 | 0.0388 |
| cosine_precision@3 | 0.028 |
| cosine_precision@5 | 0.0259 |
| cosine_precision@10 | 0.0185 |
| cosine_recall@1 | 0.0388 |
| cosine_recall@3 | 0.0841 |
| cosine_recall@5 | 0.1293 |
| cosine_recall@10 | 0.1853 |
| cosine_ndcg@10 | 0.1021 |
| cosine_mrr@10 | 0.0767 |
| **cosine_map@100** | **0.0899** |
#### 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 | 0.0345 |
| cosine_accuracy@3 | 0.0948 |
| cosine_accuracy@5 | 0.1272 |
| cosine_accuracy@10 | 0.1853 |
| cosine_precision@1 | 0.0345 |
| cosine_precision@3 | 0.0316 |
| cosine_precision@5 | 0.0254 |
| cosine_precision@10 | 0.0185 |
| cosine_recall@1 | 0.0345 |
| cosine_recall@3 | 0.0948 |
| cosine_recall@5 | 0.1272 |
| cosine_recall@10 | 0.1853 |
| cosine_ndcg@10 | 0.101 |
| cosine_mrr@10 | 0.0753 |
| **cosine_map@100** | **0.0899** |
#### 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 | 0.0345 |
| cosine_accuracy@3 | 0.0841 |
| cosine_accuracy@5 | 0.1034 |
| cosine_accuracy@10 | 0.1703 |
| cosine_precision@1 | 0.0345 |
| cosine_precision@3 | 0.028 |
| cosine_precision@5 | 0.0207 |
| cosine_precision@10 | 0.017 |
| cosine_recall@1 | 0.0345 |
| cosine_recall@3 | 0.0841 |
| cosine_recall@5 | 0.1034 |
| cosine_recall@10 | 0.1703 |
| cosine_ndcg@10 | 0.0933 |
| cosine_mrr@10 | 0.07 |
| **cosine_map@100** | **0.0837** |
<!--
## 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.*
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### 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: 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: 10 tokens</li><li>mean: 67.49 tokens</li><li>max: 214 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 28.0 tokens</li><li>max: 61 tokens</li></ul> |
* Samples:
| positive | anchor |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|
| <code>Havent-se d'acreditar la matriculació i inscripció en el respectiu centre públic o concertat, així com el cost de les llars d'infants, de l'educació especialitzada per les discapacitats físiques, psíquiques i sensorials en centres públics, concertats o privats.</code> | <code>Quin és el requisit per acreditar la llar d'infants?</code> |
| <code>El volant històric de convivència és el document que informa de la residencia en el municipi de Sitges, així com altres fets relatius a l'empadronament d'una persona, i detalla tots els domicilis, la data inicial i final en els que ha estat empadronada en cadascun d'ells, i les persones amb les què constava inscrites, segons les dades que consten al Padró Municipal d'Habitants fins a la data d'expedició.</code> | <code>Quin és el propòsit del volant històric de convivència?</code> |
| <code>Instal·lació de tanques sense obra.</code> | <code>Quins són els exemples d'instal·lacions que es poden comunicar amb aquest tràmit?</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
384,
256,
128,
64
],
"matryoshka_weights": [
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
- `num_train_epochs`: 5
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.2
- `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`: 5e-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`: 0.2
- `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 | 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 |
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.6130 | 10 | 11.3695 | - | - | - | - | - |
| 0.9808 | 16 | - | 0.0214 | 0.0243 | 0.0234 | 0.0199 | 0.0234 |
| 1.2261 | 20 | 10.653 | - | - | - | - | - |
| 1.8391 | 30 | 9.0745 | - | - | - | - | - |
| 1.9617 | 32 | - | 0.0495 | 0.0517 | 0.0589 | 0.0481 | 0.0589 |
| 2.4521 | 40 | 7.3468 | - | - | - | - | - |
| 2.9425 | 48 | - | 0.0764 | 0.0734 | 0.0811 | 0.0709 | 0.0811 |
| 3.0651 | 50 | 5.887 | - | - | - | - | - |
| 3.6782 | 60 | 5.3568 | - | - | - | - | - |
| **3.9847** | **65** | **-** | **0.0922** | **0.0857** | **0.0896** | **0.0808** | **0.0896** |
| 4.2912 | 70 | 4.8338 | - | - | - | - | - |
| 4.9042 | 80 | 4.9251 | 0.0899 | 0.0899 | 0.0906 | 0.0837 | 0.0906 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.4.0+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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