|
--- |
|
base_model: BAAI/bge-m3 |
|
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:6749 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: La presentació de la comunicació prèvia, acompanyada de la documentació |
|
exigida, habilita a la persona interessada a executar els actes que s'hi descriuen, |
|
des del dia de la seva presentació, sens perjudici de les facultats de comprovació, |
|
control i inspecció de l'Ajuntament. |
|
sentences: |
|
- Quin és el resultat de la llicència d'usos i obres provisionals en relació amb |
|
altres autoritzacions administratives? |
|
- Quin és el paper de la persona interessada en aquest tràmit? |
|
- Quin és el tipus d'impost que es beneficia d'aquest tràmit? |
|
- source_sentence: L'aportació de residus a la Deixalleria municipal us permet obtenir |
|
una bonificació de la taxa de residus del 15%. |
|
sentences: |
|
- Quin és el benefici de la Deixalleria municipal? |
|
- Quin és el benefici de tenir un volant de convivència? |
|
- Quin és el benefici de tenir el certificat del nombre d’habitants i habitatges |
|
del Padró d’Habitants? |
|
- source_sentence: La presentació de la comunicació prèvia, acompanyada de la documentació |
|
exigida, habilita a la persona interessada a executar els actes que s'hi descriuen, |
|
des del dia de la seva presentació, sens perjudici de les facultats de comprovació, |
|
control i inspecció de l’Ajuntament. |
|
sentences: |
|
- Quin és el resultat de la presentació de la documentació exigida? |
|
- Quina és la condició per a la concessió de la bonificació? |
|
- On es troben els drets funeraris que es volen canviar? |
|
- source_sentence: Renovació de concessió de drets funeraris a llarg termini (cementiri) |
|
sentences: |
|
- Quin és el requisit per aturar o estacionar el vehicle amb la targeta d'aparcament |
|
de transport col·lectiu? |
|
- Quin és el benefici de la concessió de drets funeraris a llarg termini? |
|
- Quin és el tipus de residus que es requereixen per a la bonificació? |
|
- source_sentence: La presentació de la sol·licitud no dona dret al muntatge de la |
|
parada. |
|
sentences: |
|
- Quin és el motiu per canviar la persona titular dels drets funeraris? |
|
- Quin és el propòsit de la reunió informativa i de coordinació? |
|
- Quin és el requisit per a la presentació de la sol·licitud d’autorització? |
|
model-index: |
|
- name: SentenceTransformer based on BAAI/bge-m3 |
|
results: |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 1024 |
|
type: dim_1024 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.044 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.116 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.18 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.3506666666666667 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.044 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.03866666666666667 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.036 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.03506666666666667 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.044 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.116 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.18 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.3506666666666667 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.16592235166459846 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.11099682539682543 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.13414156200645738 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.04133333333333333 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.116 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.17866666666666667 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.3626666666666667 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.04133333333333333 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.03866666666666666 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.03573333333333333 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.03626666666666667 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.04133333333333333 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.116 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.17866666666666667 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.3626666666666667 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.16902152680215465 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.11157989417989429 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.13412743689937764 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.04666666666666667 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.116 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.17866666666666667 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.356 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.04666666666666667 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.03866666666666667 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.03573333333333333 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.03560000000000001 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.04666666666666667 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.116 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.17866666666666667 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.356 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.16772455344289713 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.11209576719576728 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.13459804045251053 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.03866666666666667 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.10666666666666667 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.17066666666666666 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.3413333333333333 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.03866666666666667 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.035555555555555556 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.034133333333333335 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.034133333333333335 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.03866666666666667 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.10666666666666667 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.17066666666666666 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.3413333333333333 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.15868936356762114 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.10455608465608475 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.12901246498692368 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.04933333333333333 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.12266666666666666 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.19866666666666666 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.36666666666666664 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.04933333333333333 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.040888888888888884 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.039733333333333336 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.03666666666666667 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.04933333333333333 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.12266666666666666 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.19866666666666666 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.36666666666666664 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.17594327999948436 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.11901798941798955 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.14198426639116846 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.037333333333333336 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.09466666666666666 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.15733333333333333 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.34 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.037333333333333336 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.03155555555555555 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.03146666666666667 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.034 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.037333333333333336 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.09466666666666666 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.15733333333333333 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.34 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.1535334048621682 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.09865185185185205 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.12262604132052936 |
|
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) on the json dataset. 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:** |
|
- json |
|
<!-- - **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/sqv2") |
|
# Run inference |
|
sentences = [ |
|
'La presentació de la sol·licitud no dona dret al muntatge de la parada.', |
|
'Quin és el requisit per a la presentació de la sol·licitud d’autorització?', |
|
'Quin és el motiu per canviar la persona titular dels drets funeraris?', |
|
] |
|
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_1024` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.044 | |
|
| cosine_accuracy@3 | 0.116 | |
|
| cosine_accuracy@5 | 0.18 | |
|
| cosine_accuracy@10 | 0.3507 | |
|
| cosine_precision@1 | 0.044 | |
|
| cosine_precision@3 | 0.0387 | |
|
| cosine_precision@5 | 0.036 | |
|
| cosine_precision@10 | 0.0351 | |
|
| cosine_recall@1 | 0.044 | |
|
| cosine_recall@3 | 0.116 | |
|
| cosine_recall@5 | 0.18 | |
|
| cosine_recall@10 | 0.3507 | |
|
| cosine_ndcg@10 | 0.1659 | |
|
| cosine_mrr@10 | 0.111 | |
|
| **cosine_map@100** | **0.1341** | |
|
|
|
#### 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 | 0.0413 | |
|
| cosine_accuracy@3 | 0.116 | |
|
| cosine_accuracy@5 | 0.1787 | |
|
| cosine_accuracy@10 | 0.3627 | |
|
| cosine_precision@1 | 0.0413 | |
|
| cosine_precision@3 | 0.0387 | |
|
| cosine_precision@5 | 0.0357 | |
|
| cosine_precision@10 | 0.0363 | |
|
| cosine_recall@1 | 0.0413 | |
|
| cosine_recall@3 | 0.116 | |
|
| cosine_recall@5 | 0.1787 | |
|
| cosine_recall@10 | 0.3627 | |
|
| cosine_ndcg@10 | 0.169 | |
|
| cosine_mrr@10 | 0.1116 | |
|
| **cosine_map@100** | **0.1341** | |
|
|
|
#### 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 | 0.0467 | |
|
| cosine_accuracy@3 | 0.116 | |
|
| cosine_accuracy@5 | 0.1787 | |
|
| cosine_accuracy@10 | 0.356 | |
|
| cosine_precision@1 | 0.0467 | |
|
| cosine_precision@3 | 0.0387 | |
|
| cosine_precision@5 | 0.0357 | |
|
| cosine_precision@10 | 0.0356 | |
|
| cosine_recall@1 | 0.0467 | |
|
| cosine_recall@3 | 0.116 | |
|
| cosine_recall@5 | 0.1787 | |
|
| cosine_recall@10 | 0.356 | |
|
| cosine_ndcg@10 | 0.1677 | |
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| cosine_mrr@10 | 0.1121 | |
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| **cosine_map@100** | **0.1346** | |
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#### Information Retrieval |
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* Dataset: `dim_256` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:----------| |
|
| cosine_accuracy@1 | 0.0387 | |
|
| cosine_accuracy@3 | 0.1067 | |
|
| cosine_accuracy@5 | 0.1707 | |
|
| cosine_accuracy@10 | 0.3413 | |
|
| cosine_precision@1 | 0.0387 | |
|
| cosine_precision@3 | 0.0356 | |
|
| cosine_precision@5 | 0.0341 | |
|
| cosine_precision@10 | 0.0341 | |
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| cosine_recall@1 | 0.0387 | |
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| cosine_recall@3 | 0.1067 | |
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| cosine_recall@5 | 0.1707 | |
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| cosine_recall@10 | 0.3413 | |
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| cosine_ndcg@10 | 0.1587 | |
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| cosine_mrr@10 | 0.1046 | |
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| **cosine_map@100** | **0.129** | |
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|
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#### Information Retrieval |
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* 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 | 0.0493 | |
|
| cosine_accuracy@3 | 0.1227 | |
|
| cosine_accuracy@5 | 0.1987 | |
|
| cosine_accuracy@10 | 0.3667 | |
|
| cosine_precision@1 | 0.0493 | |
|
| cosine_precision@3 | 0.0409 | |
|
| cosine_precision@5 | 0.0397 | |
|
| cosine_precision@10 | 0.0367 | |
|
| cosine_recall@1 | 0.0493 | |
|
| cosine_recall@3 | 0.1227 | |
|
| cosine_recall@5 | 0.1987 | |
|
| cosine_recall@10 | 0.3667 | |
|
| cosine_ndcg@10 | 0.1759 | |
|
| cosine_mrr@10 | 0.119 | |
|
| **cosine_map@100** | **0.142** | |
|
|
|
#### 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 | 0.0373 | |
|
| cosine_accuracy@3 | 0.0947 | |
|
| cosine_accuracy@5 | 0.1573 | |
|
| cosine_accuracy@10 | 0.34 | |
|
| cosine_precision@1 | 0.0373 | |
|
| cosine_precision@3 | 0.0316 | |
|
| cosine_precision@5 | 0.0315 | |
|
| cosine_precision@10 | 0.034 | |
|
| cosine_recall@1 | 0.0373 | |
|
| cosine_recall@3 | 0.0947 | |
|
| cosine_recall@5 | 0.1573 | |
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| cosine_recall@10 | 0.34 | |
|
| cosine_ndcg@10 | 0.1535 | |
|
| cosine_mrr@10 | 0.0987 | |
|
| **cosine_map@100** | **0.1226** | |
|
|
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<!-- |
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## Bias, Risks and Limitations |
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*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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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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|
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## Training Details |
|
|
|
### Training Dataset |
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|
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#### json |
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|
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* Dataset: json |
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* Size: 6,749 training samples |
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* Columns: <code>positive</code> and <code>anchor</code> |
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* Approximate statistics based on the first 1000 samples: |
|
| | positive | anchor | |
|
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 6 tokens</li><li>mean: 42.03 tokens</li><li>max: 106 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 20.32 tokens</li><li>max: 54 tokens</li></ul> | |
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* Samples: |
|
| positive | anchor | |
|
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------| |
|
| <code>Aquest tràmit us permet compensar deutes de naturalesa pública a favor de l'Ajuntament, sigui quin sigui el seu estat (voluntari/executiu), amb crèdits reconeguts per aquest a favor del mateix deutor, i que el seu estat sigui pendent de pagament.</code> | <code>Quin és el benefici de la compensació de deutes amb crèdits?</code> | |
|
| <code>El seu objecte és que -prèviament a la seva execució material- l'Ajuntament comprovi l'adequació de l’actuació a la normativa i planejament, així com a les ordenances municipals sobre l’ús del sòl i edificació.</code> | <code>Quin és el paper de les ordenances municipals en aquest tràmit?</code> | |
|
| <code>Comunicació prèvia del manteniment en espais, zones o instal·lacions comunitàries interiors dels edificis (reparació i/o millora de materials).</code> | <code>Quin és el límit del manteniment en espais comunitaris interiors dels edificis?</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
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```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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"matryoshka_dims": [ |
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1024, |
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768, |
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512, |
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256, |
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128, |
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64 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1, |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": -1 |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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|
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- `eval_strategy`: epoch |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `gradient_accumulation_steps`: 16 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 5 |
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- `lr_scheduler_type`: cosine |
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- `warmup_ratio`: 0.2 |
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- `bf16`: True |
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- `tf32`: True |
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- `load_best_model_at_end`: True |
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- `optim`: adamw_torch_fused |
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- `batch_sampler`: no_duplicates |
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|
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#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: epoch |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
|
- `per_gpu_train_batch_size`: None |
|
- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 16 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 2e-05 |
|
- `weight_decay`: 0.0 |
|
- `adam_beta1`: 0.9 |
|
- `adam_beta2`: 0.999 |
|
- `adam_epsilon`: 1e-08 |
|
- `max_grad_norm`: 1.0 |
|
- `num_train_epochs`: 5 |
|
- `max_steps`: -1 |
|
- `lr_scheduler_type`: cosine |
|
- `lr_scheduler_kwargs`: {} |
|
- `warmup_ratio`: 0.2 |
|
- `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 |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
|
- `use_ipex`: False |
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- `bf16`: True |
|
- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: True |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: True |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `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 |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
|
- `ddp_bucket_cap_mb`: None |
|
- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: False |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `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 |
|
- `eval_use_gather_object`: False |
|
- `batch_sampler`: no_duplicates |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | Training Loss | dim_1024_cosine_map@100 | 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 | |
|
|:----------:|:-------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 0.3791 | 10 | 3.0867 | - | - | - | - | - | - | |
|
| 0.7583 | 20 | 2.4414 | - | - | - | - | - | - | |
|
| 0.9858 | 26 | - | 0.1266 | 0.1255 | 0.1232 | 0.1257 | 0.1091 | 0.1345 | |
|
| 1.1351 | 30 | 1.7091 | - | - | - | - | - | - | |
|
| 1.5142 | 40 | 1.2495 | - | - | - | - | - | - | |
|
| 1.8934 | 50 | 0.9813 | - | - | - | - | - | - | |
|
| 1.9692 | 52 | - | 0.1315 | 0.1325 | 0.1285 | 0.1328 | 0.1218 | 0.1309 | |
|
| 2.2701 | 60 | 0.6918 | - | - | - | - | - | - | |
|
| 2.6493 | 70 | 0.7146 | - | - | - | - | - | - | |
|
| 2.9905 | 79 | - | 0.1370 | 0.1344 | 0.1355 | 0.1338 | 0.1269 | 0.1363 | |
|
| 3.0261 | 80 | 0.6002 | - | - | - | - | - | - | |
|
| 3.4052 | 90 | 0.4816 | - | - | - | - | - | - | |
|
| 3.7844 | 100 | 0.4949 | - | - | - | - | - | - | |
|
| 3.9739 | 105 | - | 0.1357 | 0.1393 | 0.1302 | 0.1347 | 0.1204 | 0.1354 | |
|
| 4.1611 | 110 | 0.474 | - | - | - | - | - | - | |
|
| 4.5403 | 120 | 0.4692 | - | - | - | - | - | - | |
|
| **4.9194** | **130** | **0.4484** | **0.1341** | **0.142** | **0.129** | **0.1346** | **0.1226** | **0.1341** | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.1.0 |
|
- Transformers: 4.44.2 |
|
- PyTorch: 2.4.0+cu121 |
|
- Accelerate: 0.35.0.dev0 |
|
- Datasets: 3.0.0 |
|
- 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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*Clearly define terms in order to be accessible across audiences.* |
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