|
--- |
|
base_model: BAAI/bge-m3 |
|
datasets: [] |
|
language: [] |
|
library_name: sentence-transformers |
|
metrics: |
|
- cosine_accuracy@1 |
|
- cosine_accuracy@3 |
|
- cosine_accuracy@5 |
|
- cosine_accuracy@10 |
|
- cosine_precision@1 |
|
- cosine_precision@3 |
|
- cosine_precision@5 |
|
- cosine_precision@10 |
|
- cosine_recall@1 |
|
- cosine_recall@3 |
|
- cosine_recall@5 |
|
- cosine_recall@10 |
|
- cosine_ndcg@10 |
|
- cosine_mrr@10 |
|
- cosine_map@100 |
|
pipeline_tag: sentence-similarity |
|
tags: |
|
- sentence-transformers |
|
- sentence-similarity |
|
- feature-extraction |
|
- generated_from_trainer |
|
- dataset_size:9593 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: Aquest tràmit permet a la nova persona titular sol·licitar el canvi |
|
de nom d'una llicència de gual, sempre que no variïn la utilització ni les característiques |
|
de la llicència concedida prèviament, i s’acompleixen les ordenances vigents. |
|
sentences: |
|
- Quin és el resultat de la presentació del tràmit de comunicació d'inici i modificació |
|
substancial d'activitat en un establiment? |
|
- Quin és el benefici per a les entitats especialitzades de la gestió delegada? |
|
- Necessito canviar el titular de la meva llicència de gual |
|
- source_sentence: Mitjançant aquest tràmit la persona interessada posa en coneixement |
|
de l'Ajuntament l'inici o modificació substancial d'una activitat econòmica, de |
|
les incloses en l'annex de la Llei de facilitació de l'activitat econòmica, i |
|
hi adjunta el projecte i el certificat tècnic acreditatiu del compliment dels |
|
requisits necessaris que estableix la normativa vigent per a l'exercici de l'activitat. |
|
sentences: |
|
- Quins canvis es poden fer en els tanques? |
|
- Què és necessari per gaudir d'exempció de les taxes per recollida d'escombraries? |
|
- Quin és el resultat de la presentació del certificat tècnic acreditatiu? |
|
- source_sentence: La instal·lació i utilització d’una grua torre està subjecta a |
|
l’obtenció d’una llicència municipal. |
|
sentences: |
|
- Quin és el propòsit de la Declaració de baixa de la Taxa pel servei municipal |
|
complementari de recollida, tractament i eliminació de residus comercials? |
|
- Quin és el paper de la persona beneficiària en el pagament de l'ajut de lloguer |
|
just? |
|
- Què és necessari per a la instal·lació i utilització d'una grua torre? |
|
- source_sentence: El procediment d'adjudicació serà mitjançant concurs públic, amb |
|
la presentació de la sol·licitud dins del termini establert per cada convocatòria, |
|
amb la priorització de casos amb seguiment social i educatiu a persones i famílies |
|
en situació de vulnerabilitat social i econòmica. |
|
sentences: |
|
- Quins són els casos que tenen prioritat en l'adjudicació dels habitatges del Fons |
|
d'Habitatges d'Inclusió Social? |
|
- Quin és el paper del certificat del nombre d'habitatges en el tràmit d'obertura |
|
d'una oficina de farmàcia? |
|
- Quin és el paper de la Junta de Govern Local en relació amb les garanties? |
|
- source_sentence: Els comerciants locals han de sol·licitar els ajuts per al projecte |
|
de la targeta de fidelització dins del termini establert per l'Ajuntament de Sitges. |
|
sentences: |
|
- Quin és el paper de la persona cuidadora en la gestió de les emergències en la |
|
colònia felina? |
|
- Quin és el termini perquè els comerciants locals puguin sol·licitar els ajuts |
|
per al projecte de la targeta de fidelització? |
|
- Quin és el règim especial al qual han d'estar inscrites les persones per rebre |
|
els ajuts? |
|
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: 0.0600375234521576 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.1303939962476548 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.1801125703564728 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.32833020637898686 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.0600375234521576 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.04346466541588492 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.036022514071294566 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.03283302063789869 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.0600375234521576 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.1303939962476548 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.1801125703564728 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.32833020637898686 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.16801025559505256 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.12051319276929036 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.14641981337897508 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.05909943714821764 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.12195121951219512 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.18105065666041276 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.3302063789868668 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.05909943714821764 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.04065040650406503 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.03621013133208256 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.03302063789868668 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.05909943714821764 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.12195121951219512 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.18105065666041276 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.3302063789868668 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.1674921436005172 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.119329044938801 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.14541664461952028 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.058161350844277676 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.12851782363977485 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.1904315196998124 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.32645403377110693 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.058161350844277676 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.04283927454659161 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.03808630393996248 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.03264540337711069 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.058161350844277676 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.12851782363977485 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.1904315196998124 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.32645403377110693 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.16736509943357222 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.11985169302242468 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.14638786229645445 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.054409005628517824 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.11913696060037524 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.18198874296435272 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.3170731707317073 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.054409005628517824 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.03971232020012507 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.036397748592870545 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.03170731707317073 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.054409005628517824 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.11913696060037524 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.18198874296435272 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.3170731707317073 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.16104635688777047 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.11454927186634503 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.14146334434951485 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.054409005628517824 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.12195121951219512 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.18198874296435272 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.31144465290806755 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.054409005628517824 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.04065040650406503 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.03639774859287054 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.031144465290806757 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.054409005628517824 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.12195121951219512 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.18198874296435272 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.31144465290806755 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.15963450508596505 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.11438361773727633 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.14164175280264735 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.05065666041275797 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.11444652908067542 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.18292682926829268 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.3076923076923077 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.05065666041275797 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.0381488430268918 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.036585365853658534 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.030769230769230767 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.05065666041275797 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.11444652908067542 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.18292682926829268 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.3076923076923077 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.1558660768539628 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.11049949373120106 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.13758639006498824 |
|
name: Cosine Map@100 |
|
--- |
|
|
|
# SentenceTransformer based on BAAI/bge-m3 |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co./BAAI/bge-m3). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
|
|
|
## Model Details |
|
|
|
### Model Description |
|
- **Model Type:** Sentence Transformer |
|
- **Base model:** [BAAI/bge-m3](https://huggingface.co./BAAI/bge-m3) <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 --> |
|
- **Maximum Sequence Length:** 8192 tokens |
|
- **Output Dimensionality:** 1024 tokens |
|
- **Similarity Function:** Cosine Similarity |
|
<!-- - **Training Dataset:** Unknown --> |
|
<!-- - **Language:** Unknown --> |
|
<!-- - **License:** Unknown --> |
|
|
|
### Model Sources |
|
|
|
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
|
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
|
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co./models?library=sentence-transformers) |
|
|
|
### Full Model Architecture |
|
|
|
``` |
|
SentenceTransformer( |
|
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel |
|
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
|
(2): Normalize() |
|
) |
|
``` |
|
|
|
## Usage |
|
|
|
### Direct Usage (Sentence Transformers) |
|
|
|
First install the Sentence Transformers library: |
|
|
|
```bash |
|
pip install -U sentence-transformers |
|
``` |
|
|
|
Then you can load this model and run inference. |
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
|
|
# Download from the 🤗 Hub |
|
model = SentenceTransformer("adriansanz/sitgrsBAAIbge-m3-300824v2") |
|
# Run inference |
|
sentences = [ |
|
"Els comerciants locals han de sol·licitar els ajuts per al projecte de la targeta de fidelització dins del termini establert per l'Ajuntament de Sitges.", |
|
'Quin és el termini perquè els comerciants locals puguin sol·licitar els ajuts per al projecte de la targeta de fidelització?', |
|
'Quin és el paper de la persona cuidadora en la gestió de les emergències en la colònia felina?', |
|
] |
|
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 | 0.06 | |
|
| cosine_accuracy@3 | 0.1304 | |
|
| cosine_accuracy@5 | 0.1801 | |
|
| cosine_accuracy@10 | 0.3283 | |
|
| cosine_precision@1 | 0.06 | |
|
| cosine_precision@3 | 0.0435 | |
|
| cosine_precision@5 | 0.036 | |
|
| cosine_precision@10 | 0.0328 | |
|
| cosine_recall@1 | 0.06 | |
|
| cosine_recall@3 | 0.1304 | |
|
| cosine_recall@5 | 0.1801 | |
|
| cosine_recall@10 | 0.3283 | |
|
| cosine_ndcg@10 | 0.168 | |
|
| cosine_mrr@10 | 0.1205 | |
|
| **cosine_map@100** | **0.1464** | |
|
|
|
#### 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 | 0.0591 | |
|
| cosine_accuracy@3 | 0.122 | |
|
| cosine_accuracy@5 | 0.1811 | |
|
| cosine_accuracy@10 | 0.3302 | |
|
| cosine_precision@1 | 0.0591 | |
|
| cosine_precision@3 | 0.0407 | |
|
| cosine_precision@5 | 0.0362 | |
|
| cosine_precision@10 | 0.033 | |
|
| cosine_recall@1 | 0.0591 | |
|
| cosine_recall@3 | 0.122 | |
|
| cosine_recall@5 | 0.1811 | |
|
| cosine_recall@10 | 0.3302 | |
|
| cosine_ndcg@10 | 0.1675 | |
|
| cosine_mrr@10 | 0.1193 | |
|
| **cosine_map@100** | **0.1454** | |
|
|
|
#### 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 | 0.0582 | |
|
| cosine_accuracy@3 | 0.1285 | |
|
| cosine_accuracy@5 | 0.1904 | |
|
| cosine_accuracy@10 | 0.3265 | |
|
| cosine_precision@1 | 0.0582 | |
|
| cosine_precision@3 | 0.0428 | |
|
| cosine_precision@5 | 0.0381 | |
|
| cosine_precision@10 | 0.0326 | |
|
| cosine_recall@1 | 0.0582 | |
|
| cosine_recall@3 | 0.1285 | |
|
| cosine_recall@5 | 0.1904 | |
|
| cosine_recall@10 | 0.3265 | |
|
| cosine_ndcg@10 | 0.1674 | |
|
| cosine_mrr@10 | 0.1199 | |
|
| **cosine_map@100** | **0.1464** | |
|
|
|
#### 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 | 0.0544 | |
|
| cosine_accuracy@3 | 0.1191 | |
|
| cosine_accuracy@5 | 0.182 | |
|
| cosine_accuracy@10 | 0.3171 | |
|
| cosine_precision@1 | 0.0544 | |
|
| cosine_precision@3 | 0.0397 | |
|
| cosine_precision@5 | 0.0364 | |
|
| cosine_precision@10 | 0.0317 | |
|
| cosine_recall@1 | 0.0544 | |
|
| cosine_recall@3 | 0.1191 | |
|
| cosine_recall@5 | 0.182 | |
|
| cosine_recall@10 | 0.3171 | |
|
| cosine_ndcg@10 | 0.161 | |
|
| cosine_mrr@10 | 0.1145 | |
|
| **cosine_map@100** | **0.1415** | |
|
|
|
#### 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 | 0.0544 | |
|
| cosine_accuracy@3 | 0.122 | |
|
| cosine_accuracy@5 | 0.182 | |
|
| cosine_accuracy@10 | 0.3114 | |
|
| cosine_precision@1 | 0.0544 | |
|
| cosine_precision@3 | 0.0407 | |
|
| cosine_precision@5 | 0.0364 | |
|
| cosine_precision@10 | 0.0311 | |
|
| cosine_recall@1 | 0.0544 | |
|
| cosine_recall@3 | 0.122 | |
|
| cosine_recall@5 | 0.182 | |
|
| cosine_recall@10 | 0.3114 | |
|
| cosine_ndcg@10 | 0.1596 | |
|
| cosine_mrr@10 | 0.1144 | |
|
| **cosine_map@100** | **0.1416** | |
|
|
|
#### 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 | 0.0507 | |
|
| cosine_accuracy@3 | 0.1144 | |
|
| cosine_accuracy@5 | 0.1829 | |
|
| cosine_accuracy@10 | 0.3077 | |
|
| cosine_precision@1 | 0.0507 | |
|
| cosine_precision@3 | 0.0381 | |
|
| cosine_precision@5 | 0.0366 | |
|
| cosine_precision@10 | 0.0308 | |
|
| cosine_recall@1 | 0.0507 | |
|
| cosine_recall@3 | 0.1144 | |
|
| cosine_recall@5 | 0.1829 | |
|
| cosine_recall@10 | 0.3077 | |
|
| cosine_ndcg@10 | 0.1559 | |
|
| cosine_mrr@10 | 0.1105 | |
|
| **cosine_map@100** | **0.1376** | |
|
|
|
<!-- |
|
## Bias, Risks and Limitations |
|
|
|
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
|
--> |
|
|
|
<!-- |
|
### Recommendations |
|
|
|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
|
--> |
|
|
|
## Training Details |
|
|
|
### Training Dataset |
|
|
|
#### Unnamed Dataset |
|
|
|
|
|
* Size: 9,593 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: 3 tokens</li><li>mean: 49.72 tokens</li><li>max: 190 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 21.22 tokens</li><li>max: 45 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------| |
|
| <code>Mitjançant aquest tràmit la persona interessada posa en coneixement de l'Ajuntament l’inici o modificació substancial d’una activitat econòmica.</code> | <code>Quin és el paper de l'Ajuntament en la comunicació de modificació d'activitat?</code> | |
|
| <code>El Carnet Blau és un carnet personal i intransferible que acredita el compliment dels requisits per a gaudir d'un conjunt de descomptes i avantatges.</code> | <code>Quin és el propòsit del Carnet Blau en relació amb els descomptes?</code> | |
|
| <code>Bonificació del 25% de l'import corresponent al consum d'aigua, la conservació d'escomeses, aforaments i comptadors així com els drets de connexió.</code> | <code>Quin és l'objectiu de la bonificació de la taxa per distribució i subministrament d'aigua?</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
```json |
|
{ |
|
"loss": "MultipleNegativesRankingLoss", |
|
"matryoshka_dims": [ |
|
1024, |
|
768, |
|
512, |
|
256, |
|
128, |
|
64 |
|
], |
|
"matryoshka_weights": [ |
|
1, |
|
1, |
|
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 |
|
- `learning_rate`: 2e-05 |
|
- `num_train_epochs`: 10 |
|
- `lr_scheduler_type`: cosine |
|
- `warmup_ratio`: 0.2 |
|
- `bf16`: True |
|
- `tf32`: True |
|
- `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`: 10 |
|
- `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 |
|
- `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 |
|
- `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 | |
|
|:----------:|:-------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 0.2667 | 10 | 3.4587 | - | - | - | - | - | - | |
|
| 0.5333 | 20 | 2.8693 | - | - | - | - | - | - | |
|
| 0.8 | 30 | 2.3094 | - | - | - | - | - | - | |
|
| 0.9867 | 37 | - | 0.1331 | 0.1252 | 0.1322 | 0.1337 | 0.1128 | 0.1347 | |
|
| 1.0667 | 40 | 1.6196 | - | - | - | - | - | - | |
|
| 1.3333 | 50 | 1.1926 | - | - | - | - | - | - | |
|
| 1.6 | 60 | 0.9497 | - | - | - | - | - | - | |
|
| 1.8667 | 70 | 0.882 | - | - | - | - | - | - | |
|
| 2.0 | 75 | - | 0.1372 | 0.1272 | 0.1298 | 0.1365 | 0.1212 | 0.1369 | |
|
| 2.1333 | 80 | 0.5621 | - | - | - | - | - | - | |
|
| 2.4 | 90 | 0.4454 | - | - | - | - | - | - | |
|
| 2.6667 | 100 | 0.4143 | - | - | - | - | - | - | |
|
| 2.9333 | 110 | 0.4014 | - | - | - | - | - | - | |
|
| 2.9867 | 112 | - | 0.1365 | 0.1282 | 0.1329 | 0.1437 | 0.1259 | 0.1390 | |
|
| 3.2 | 120 | 0.2863 | - | - | - | - | - | - | |
|
| 3.4667 | 130 | 0.1977 | - | - | - | - | - | - | |
|
| 3.7333 | 140 | 0.2411 | - | - | - | - | - | - | |
|
| 4.0 | 150 | 0.222 | 0.1355 | 0.1308 | 0.1378 | 0.1346 | 0.1239 | 0.1362 | |
|
| 4.2667 | 160 | 0.1705 | - | - | - | - | - | - | |
|
| 4.5333 | 170 | 0.1522 | - | - | - | - | - | - | |
|
| 4.8 | 180 | 0.1606 | - | - | - | - | - | - | |
|
| 4.9867 | 187 | - | 0.1441 | 0.1305 | 0.1344 | 0.1373 | 0.1356 | 0.1409 | |
|
| 5.0667 | 190 | 0.1281 | - | - | - | - | - | - | |
|
| 5.3333 | 200 | 0.1099 | - | - | - | - | - | - | |
|
| 5.6 | 210 | 0.0921 | - | - | - | - | - | - | |
|
| 5.8667 | 220 | 0.114 | - | - | - | - | - | - | |
|
| 6.0 | 225 | - | 0.1371 | 0.1361 | 0.1331 | 0.1371 | 0.1351 | 0.1421 | |
|
| 6.1333 | 230 | 0.0703 | - | - | - | - | - | - | |
|
| 6.4 | 240 | 0.0746 | - | - | - | - | - | - | |
|
| 6.6667 | 250 | 0.0734 | - | - | - | - | - | - | |
|
| 6.9333 | 260 | 0.0803 | - | - | - | - | - | - | |
|
| 6.9867 | 262 | - | 0.1447 | 0.1400 | 0.1422 | 0.1397 | 0.1376 | 0.1395 | |
|
| 7.2 | 270 | 0.0684 | - | - | - | - | - | - | |
|
| 7.4667 | 280 | 0.0493 | - | - | - | - | - | - | |
|
| 7.7333 | 290 | 0.0531 | - | - | - | - | - | - | |
|
| 8.0 | 300 | 0.0705 | 0.1410 | 0.1411 | 0.1379 | 0.1372 | 0.1372 | 0.1380 | |
|
| 8.2667 | 310 | 0.0495 | - | - | - | - | - | - | |
|
| 8.5333 | 320 | 0.0478 | - | - | - | - | - | - | |
|
| 8.8 | 330 | 0.0455 | - | - | - | - | - | - | |
|
| **8.9867** | **337** | **-** | **0.1463** | **0.1456** | **0.1416** | **0.1445** | **0.1408** | **0.1427** | |
|
| 9.0667 | 340 | 0.0495 | - | - | - | - | - | - | |
|
| 9.3333 | 350 | 0.0457 | - | - | - | - | - | - | |
|
| 9.6 | 360 | 0.0487 | - | - | - | - | - | - | |
|
| 9.8667 | 370 | 0.0568 | 0.1464 | 0.1416 | 0.1415 | 0.1464 | 0.1376 | 0.1454 | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.0.1 |
|
- Transformers: 4.44.2 |
|
- 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} |
|
} |
|
``` |
|
|
|
<!-- |
|
## Glossary |
|
|
|
*Clearly define terms in order to be accessible across audiences.* |
|
--> |
|
|
|
<!-- |
|
## Model Card Authors |
|
|
|
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
|
--> |
|
|
|
<!-- |
|
## Model Card Contact |
|
|
|
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
|
--> |
