|
--- |
|
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:9717 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: Per accedir a un habitatge amb protecció oficial al municipi de |
|
Sitges s'ha d'estar inscrit en el Registre municipal de sol·licitants. |
|
sentences: |
|
- Quin és el motiu perquè la renovació de la inscripció en el Registre municipal |
|
de sol·licitants d'habitatge amb protecció oficial de Sitges és necessària? |
|
- Quin és el sector que es veu afectat per la disminució d'ingressos? |
|
- Quin és el propòsit de la descripció de l'activitat? |
|
- source_sentence: Aquest tràmit permet presentar ofertes i/o pressupostos sol·licitats |
|
per l'Ajuntament de Sitges en procediments de contractes menors. |
|
sentences: |
|
- Quin és el requisit per a sol·licitar l'ajut econòmic a l'Ajuntament de Sitges? |
|
- Què passa amb la llicència de gual quan es vol reduir les característiques físiques? |
|
- Quin és el propòsit del tràmit de presentació d'ofertes? |
|
- source_sentence: Estudis universitaris fins al grau de llicenciatura |
|
sentences: |
|
- Quin és el propòsit de la subvenció per a les persones autònomes? |
|
- Quin és el requisit per als establiments oberts al públic destinats a espectacles |
|
públics i activitats recreatives musicals? |
|
- Quins estudis universitaris es poden fer amb aquesta ajuda? |
|
- source_sentence: Les entitats especialitzades i acreditades com a proveïdores de |
|
la Xarxa de Serveis Socials d'Atenció Pública interesades en la la gestió delegada |
|
dels serveis públics de l'Ajuntament de Sitges així determinats, poden presentar-se |
|
a les respectives convocatòries per a l'adjudicació. |
|
sentences: |
|
- On comencen i acaben les activitats de l'Estiu Jove? |
|
- Quin és el benefici per a l'Ajuntament de Sitges de la gestió delegada? |
|
- Quin és el paper de l’organització en la valoració d'una proposta? |
|
- source_sentence: Publicada la llista d'infants admesos i exclosos a les estades |
|
esportives, s'obre un termini perquè les persones admeses puguin demanar qualsevol |
|
canvi a la sol·licitud inicial. |
|
sentences: |
|
- Quin és el contingut del volant històric de convivència? |
|
- Quin és el període en què es pot demanar un canvi a la sol·licitud inicial? |
|
- Quin és el paper de les escoles de Sitges en les activitats de foment de l'esport |
|
escolar |
|
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: 0.10126582278481013 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.18565400843881857 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.24472573839662448 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.34177215189873417 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.10126582278481013 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.061884669479606184 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.0489451476793249 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.03417721518987342 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.10126582278481013 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.18565400843881857 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.24472573839662448 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.34177215189873417 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.20497940546236365 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.1631588641082312 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.18190274772574827 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.0970464135021097 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.18143459915611815 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.2616033755274262 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.34177215189873417 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.0970464135021097 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.06047819971870604 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.052320675105485236 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.034177215189873406 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.0970464135021097 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.18143459915611815 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.2616033755274262 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.34177215189873417 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.20447797235629017 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.16207219878105952 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.18114215201809386 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.08438818565400844 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.17721518987341772 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.23628691983122363 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.34177215189873417 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.08438818565400844 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.05907172995780591 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.04725738396624472 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.03417721518987342 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.08438818565400844 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.17721518987341772 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.23628691983122363 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.34177215189873417 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.19477348596574798 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.15014232134485297 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.16826302734813764 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.0759493670886076 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.16877637130801687 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.23628691983122363 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.34177215189873417 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.0759493670886076 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.05625879043600562 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.04725738396624473 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.034177215189873406 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.0759493670886076 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.16877637130801687 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.23628691983122363 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.34177215189873417 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.18887676996048183 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.14248208425423614 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.15960797563687307 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.08016877637130802 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.16455696202531644 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.2320675105485232 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.32489451476793246 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.08016877637130802 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.05485232067510549 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.046413502109704644 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.032489451476793246 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.08016877637130802 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.16455696202531644 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.2320675105485232 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.32489451476793246 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.18920967116655296 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.14736454356707523 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.1622413863660417 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.046413502109704644 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.1518987341772152 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.21940928270042195 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.270042194092827 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.046413502109704644 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.050632911392405056 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.04388185654008439 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.0270042194092827 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.046413502109704644 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.1518987341772152 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.21940928270042195 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.270042194092827 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.15109586098353134 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.11371308016877635 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.12600329900444687 |
|
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/sitges-v2-5ep") |
|
# Run inference |
|
sentences = [ |
|
"Publicada la llista d'infants admesos i exclosos a les estades esportives, s'obre un termini perquè les persones admeses puguin demanar qualsevol canvi a la sol·licitud inicial.", |
|
'Quin és el període en què es pot demanar un canvi a la sol·licitud inicial?', |
|
'Quin és el contingut del volant històric de convivència?', |
|
] |
|
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 | 0.1013 | |
|
| cosine_accuracy@3 | 0.1857 | |
|
| cosine_accuracy@5 | 0.2447 | |
|
| cosine_accuracy@10 | 0.3418 | |
|
| cosine_precision@1 | 0.1013 | |
|
| cosine_precision@3 | 0.0619 | |
|
| cosine_precision@5 | 0.0489 | |
|
| cosine_precision@10 | 0.0342 | |
|
| cosine_recall@1 | 0.1013 | |
|
| cosine_recall@3 | 0.1857 | |
|
| cosine_recall@5 | 0.2447 | |
|
| cosine_recall@10 | 0.3418 | |
|
| cosine_ndcg@10 | 0.205 | |
|
| cosine_mrr@10 | 0.1632 | |
|
| **cosine_map@100** | **0.1819** | |
|
|
|
#### 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 | 0.097 | |
|
| cosine_accuracy@3 | 0.1814 | |
|
| cosine_accuracy@5 | 0.2616 | |
|
| cosine_accuracy@10 | 0.3418 | |
|
| cosine_precision@1 | 0.097 | |
|
| cosine_precision@3 | 0.0605 | |
|
| cosine_precision@5 | 0.0523 | |
|
| cosine_precision@10 | 0.0342 | |
|
| cosine_recall@1 | 0.097 | |
|
| cosine_recall@3 | 0.1814 | |
|
| cosine_recall@5 | 0.2616 | |
|
| cosine_recall@10 | 0.3418 | |
|
| cosine_ndcg@10 | 0.2045 | |
|
| cosine_mrr@10 | 0.1621 | |
|
| **cosine_map@100** | **0.1811** | |
|
|
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#### 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 | 0.0844 | |
|
| cosine_accuracy@3 | 0.1772 | |
|
| cosine_accuracy@5 | 0.2363 | |
|
| cosine_accuracy@10 | 0.3418 | |
|
| cosine_precision@1 | 0.0844 | |
|
| cosine_precision@3 | 0.0591 | |
|
| cosine_precision@5 | 0.0473 | |
|
| cosine_precision@10 | 0.0342 | |
|
| cosine_recall@1 | 0.0844 | |
|
| cosine_recall@3 | 0.1772 | |
|
| cosine_recall@5 | 0.2363 | |
|
| cosine_recall@10 | 0.3418 | |
|
| cosine_ndcg@10 | 0.1948 | |
|
| cosine_mrr@10 | 0.1501 | |
|
| **cosine_map@100** | **0.1683** | |
|
|
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#### 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 | 0.0759 | |
|
| cosine_accuracy@3 | 0.1688 | |
|
| cosine_accuracy@5 | 0.2363 | |
|
| cosine_accuracy@10 | 0.3418 | |
|
| cosine_precision@1 | 0.0759 | |
|
| cosine_precision@3 | 0.0563 | |
|
| cosine_precision@5 | 0.0473 | |
|
| cosine_precision@10 | 0.0342 | |
|
| cosine_recall@1 | 0.0759 | |
|
| cosine_recall@3 | 0.1688 | |
|
| cosine_recall@5 | 0.2363 | |
|
| cosine_recall@10 | 0.3418 | |
|
| cosine_ndcg@10 | 0.1889 | |
|
| cosine_mrr@10 | 0.1425 | |
|
| **cosine_map@100** | **0.1596** | |
|
|
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#### 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 | 0.0802 | |
|
| cosine_accuracy@3 | 0.1646 | |
|
| cosine_accuracy@5 | 0.2321 | |
|
| cosine_accuracy@10 | 0.3249 | |
|
| cosine_precision@1 | 0.0802 | |
|
| cosine_precision@3 | 0.0549 | |
|
| cosine_precision@5 | 0.0464 | |
|
| cosine_precision@10 | 0.0325 | |
|
| cosine_recall@1 | 0.0802 | |
|
| cosine_recall@3 | 0.1646 | |
|
| cosine_recall@5 | 0.2321 | |
|
| cosine_recall@10 | 0.3249 | |
|
| cosine_ndcg@10 | 0.1892 | |
|
| cosine_mrr@10 | 0.1474 | |
|
| **cosine_map@100** | **0.1622** | |
|
|
|
#### 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 | 0.0464 | |
|
| cosine_accuracy@3 | 0.1519 | |
|
| cosine_accuracy@5 | 0.2194 | |
|
| cosine_accuracy@10 | 0.27 | |
|
| cosine_precision@1 | 0.0464 | |
|
| cosine_precision@3 | 0.0506 | |
|
| cosine_precision@5 | 0.0439 | |
|
| cosine_precision@10 | 0.027 | |
|
| cosine_recall@1 | 0.0464 | |
|
| cosine_recall@3 | 0.1519 | |
|
| cosine_recall@5 | 0.2194 | |
|
| cosine_recall@10 | 0.27 | |
|
| cosine_ndcg@10 | 0.1511 | |
|
| cosine_mrr@10 | 0.1137 | |
|
| **cosine_map@100** | **0.126** | |
|
|
|
<!-- |
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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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|
|
## Training Details |
|
|
|
### Training Dataset |
|
|
|
#### json |
|
|
|
* Dataset: json |
|
* Size: 9,717 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: 49.79 tokens</li><li>max: 190 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 20.83 tokens</li><li>max: 43 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------| |
|
| <code>L'Ajuntament de Sitges atorga subvencions per a projectes i activitats d'interès públic o social que tinguin per finalitat les activitats esportives federades, escolars o populars desenvolupades per les entitats esportives i esportistes del municipi de Sitges al llarg de l'exercici per la qual es sol·licita la subvenció, i reuneixin les condicions assenyalades a les bases.</code> | <code>Quin és el requisit per a obtenir les subvencions per a projectes i activitats esportives?</code> | |
|
| <code>L'Ajuntament de Sitges atorga subvencions per a projectes i activitats d'interès públic o social que tinguin per finalitat les activitats esportives federades, escolars o populars desenvolupades per les entitats esportives i esportistes del municipi de Sitges al llarg de l'exercici per la qual es sol·licita la subvenció, i reuneixin les condicions assenyalades a les bases.</code> | <code>Quin és el requisit per a obtenir les subvencions per a projectes i activitats esportives?</code> | |
|
| <code>No es proporciona informació sobre el requisit principal per obtenir el certificat.</code> | <code>Quin és el requisit principal per obtenir el certificat?</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
```json |
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{ |
|
"loss": "MultipleNegativesRankingLoss", |
|
"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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"n_dims_per_step": -1 |
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} |
|
``` |
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### Training Hyperparameters |
|
#### Non-Default Hyperparameters |
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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 |
|
- `learning_rate`: 2e-05 |
|
- `num_train_epochs`: 5 |
|
- `lr_scheduler_type`: cosine |
|
- `warmup_ratio`: 0.2 |
|
- `bf16`: True |
|
- `tf32`: True |
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- `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 |
|
- `torch_empty_cache_steps`: None |
|
- `learning_rate`: 2e-05 |
|
- `weight_decay`: 0.0 |
|
- `adam_beta1`: 0.9 |
|
- `adam_beta2`: 0.999 |
|
- `adam_epsilon`: 1e-08 |
|
- `max_grad_norm`: 1.0 |
|
- `num_train_epochs`: 5 |
|
- `max_steps`: -1 |
|
- `lr_scheduler_type`: cosine |
|
- `lr_scheduler_kwargs`: {} |
|
- `warmup_ratio`: 0.2 |
|
- `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`: True |
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- `local_rank`: 0 |
|
- `ddp_backend`: None |
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- `tpu_num_cores`: None |
|
- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `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 |
|
- `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 | |
|
|:-------:|:------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 0.2632 | 10 | 3.2527 | - | - | - | - | - | - | |
|
| 0.5263 | 20 | 1.9679 | - | - | - | - | - | - | |
|
| 0.7895 | 30 | 1.8319 | - | - | - | - | - | - | |
|
| **1.0** | **38** | **-** | **0.1819** | **0.1622** | **0.1596** | **0.1683** | **0.126** | **0.1811** | |
|
| 1.0526 | 40 | 1.3358 | - | - | - | - | - | - | |
|
| 1.3158 | 50 | 1.1166 | - | - | - | - | - | - | |
|
| 1.5789 | 60 | 0.8715 | - | - | - | - | - | - | |
|
| 1.8421 | 70 | 0.8801 | - | - | - | - | - | - | |
|
| 2.0 | 76 | - | 0.1819 | 0.1622 | 0.1596 | 0.1683 | 0.1260 | 0.1811 | |
|
| 2.1053 | 80 | 0.6515 | - | - | - | - | - | - | |
|
| 2.3684 | 90 | 0.536 | - | - | - | - | - | - | |
|
| 2.6316 | 100 | 0.4682 | - | - | - | - | - | - | |
|
| 2.8947 | 110 | 0.4686 | - | - | - | - | - | - | |
|
| 3.0 | 114 | - | 0.1819 | 0.1622 | 0.1596 | 0.1683 | 0.1260 | 0.1811 | |
|
| 3.1579 | 120 | 0.3161 | - | - | - | - | - | - | |
|
| 3.4211 | 130 | 0.3554 | - | - | - | - | - | - | |
|
| 3.6842 | 140 | 0.2886 | - | - | - | - | - | - | |
|
| 3.9474 | 150 | 0.2616 | - | - | - | - | - | - | |
|
| 4.0 | 152 | - | 0.1819 | 0.1622 | 0.1596 | 0.1683 | 0.1260 | 0.1811 | |
|
| 4.2105 | 160 | 0.1902 | - | - | - | - | - | - | |
|
| 4.4737 | 170 | 0.1894 | - | - | - | - | - | - | |
|
| 4.7368 | 180 | 0.1858 | - | - | - | - | - | - | |
|
| 5.0 | 190 | 0.1939 | 0.1819 | 0.1622 | 0.1596 | 0.1683 | 0.1260 | 0.1811 | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.1.1 |
|
- Transformers: 4.44.2 |
|
- PyTorch: 2.4.1+cu121 |
|
- Accelerate: 0.35.0.dev0 |
|
- Datasets: 3.0.1 |
|
- Tokenizers: 0.19.1 |
|
|
|
## Citation |
|
|
|
### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
month = "11", |
|
year = "2019", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/1908.10084", |
|
} |
|
``` |
|
|
|
#### MatryoshkaLoss |
|
```bibtex |
|
@misc{kusupati2024matryoshka, |
|
title={Matryoshka Representation Learning}, |
|
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
|
year={2024}, |
|
eprint={2205.13147}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.LG} |
|
} |
|
``` |
|
|
|
#### MultipleNegativesRankingLoss |
|
```bibtex |
|
@misc{henderson2017efficient, |
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
year={2017}, |
|
eprint={1705.00652}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
|
|
|
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## Glossary |
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*Clearly define terms in order to be accessible across audiences.* |
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## Model Card Authors |
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*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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