|
--- |
|
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:5520 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: Queda exclosa de la prohibició, dintre de les àrees recreatives |
|
i d'acampada i en parcel·les de les urbanitzacions, la utilització dels fogons |
|
de gas i de barbacoes d'obra amb mataguspires. |
|
sentences: |
|
- Què està prohibit fer en àrees d'acampada? |
|
- Quin és el benefici de la reserva d'un equipament municipal? |
|
- Quin és el benefici de la targeta d'aparcament individual per a l'autonomia personal? |
|
- source_sentence: Aquest tràmit permet participar en processos oberts de selecció |
|
i provisió de personal de l'Ajuntament, i fer el pagament de la taxa per drets |
|
d'examen establerta en la convocatòria. |
|
sentences: |
|
- Quin és el requisit per participar en un procés de selecció de personal de l'Ajuntament? |
|
- On es pot trobar la relació de requeriments de documentació per a l'ajut de menjador |
|
escolar? |
|
- Quin és el tipus d'activitats que es poden practicar amb les armes de 4a categoria? |
|
- source_sentence: Sol·licitar la cessió temporal d’un compostador domèstic. |
|
sentences: |
|
- Quin és el requisit per a la tala d'arbres aïllats en sòl urbà? |
|
- Quin és el paper de la persona interessada en aquest tràmit? |
|
- Quin és el paper del compostador domèstic en la reducció de les emissions de gasos |
|
d'efecte hivernacle? |
|
- source_sentence: Matriculació a l'Escola Bressol Municipal El Patufet. |
|
sentences: |
|
- Quin és el termini màxim per a deutes de 1.500,01 fins a 6.000,00 euros en el |
|
criteri excepcional? |
|
- Quin és el lloc on es realitza el tràmit de matrícula? |
|
- Quin és el lloc on es realitza el taller 'Informàtica nivell bàsic'? |
|
- source_sentence: Aquest tipus de transmissió entre cedent i cessionari només podrà |
|
ser de caràcter gratuït i no condicionada. |
|
sentences: |
|
- Quin és el caràcter de la transmissió de drets funeraris entre cedent i cessionari? |
|
- Quin és el propòsit de la comunicació prèvia en relació amb la intervenció definitiva? |
|
- Quin és el propòsit de la Deixalleria municipal? |
|
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: 0.04782608695652174 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.20869565217391303 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.30869565217391304 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5565217391304348 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.04782608695652174 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.06956521739130433 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.061739130434782616 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.055652173913043466 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.04782608695652174 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.20869565217391303 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.30869565217391304 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5565217391304348 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.25888429095047366 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.16955314009661854 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.18763324173665294 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.06086956521739131 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.21304347826086956 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.30434782608695654 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5565217391304348 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.06086956521739131 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.07101449275362319 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.06086956521739131 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.055652173913043466 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.06086956521739131 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.21304347826086956 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.30434782608695654 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5565217391304348 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.2637812435357463 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.17599723947550047 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.19341889075062485 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.0782608695652174 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.21739130434782608 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.34347826086956523 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5695652173913044 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.0782608695652174 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.07246376811594202 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.06869565217391305 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.05695652173913043 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.0782608695652174 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.21739130434782608 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.34347826086956523 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5695652173913044 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.28117776588045035 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.1947342995169084 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.21224466664057137 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.05217391304347826 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.20869565217391303 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3173913043478261 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5130434782608696 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.05217391304347826 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.06956521739130433 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.06347826086956522 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.05130434782608694 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.05217391304347826 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.20869565217391303 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3173913043478261 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5130434782608696 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.24833360148474737 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.16793305728088342 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.1892957688791951 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.05652173913043478 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.22608695652173913 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.32608695652173914 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5434782608695652 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.05652173913043478 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.0753623188405797 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.06521739130434782 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.05434782608695651 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.05652173913043478 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.22608695652173913 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.32608695652173914 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5434782608695652 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.2660596038952714 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.18197895100069028 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.20038255187663148 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.05652173913043478 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.21739130434782608 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3173913043478261 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5434782608695652 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.05652173913043478 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.07246376811594202 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.06347826086956522 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.054347826086956506 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.05652173913043478 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.21739130434782608 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3173913043478261 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5434782608695652 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.2641081743881476 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.17965838509316792 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.19707496290303578 |
|
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/sqv-v5-10ep") |
|
# Run inference |
|
sentences = [ |
|
'Aquest tipus de transmissió entre cedent i cessionari només podrà ser de caràcter gratuït i no condicionada.', |
|
'Quin és el caràcter de la transmissió de drets funeraris entre cedent i cessionari?', |
|
'Quin és el propòsit de la Deixalleria municipal?', |
|
] |
|
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 | 0.0478 | |
|
| cosine_accuracy@3 | 0.2087 | |
|
| cosine_accuracy@5 | 0.3087 | |
|
| cosine_accuracy@10 | 0.5565 | |
|
| cosine_precision@1 | 0.0478 | |
|
| cosine_precision@3 | 0.0696 | |
|
| cosine_precision@5 | 0.0617 | |
|
| cosine_precision@10 | 0.0557 | |
|
| cosine_recall@1 | 0.0478 | |
|
| cosine_recall@3 | 0.2087 | |
|
| cosine_recall@5 | 0.3087 | |
|
| cosine_recall@10 | 0.5565 | |
|
| cosine_ndcg@10 | 0.2589 | |
|
| cosine_mrr@10 | 0.1696 | |
|
| **cosine_map@100** | **0.1876** | |
|
|
|
#### 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 | 0.0609 | |
|
| cosine_accuracy@3 | 0.213 | |
|
| cosine_accuracy@5 | 0.3043 | |
|
| cosine_accuracy@10 | 0.5565 | |
|
| cosine_precision@1 | 0.0609 | |
|
| cosine_precision@3 | 0.071 | |
|
| cosine_precision@5 | 0.0609 | |
|
| cosine_precision@10 | 0.0557 | |
|
| cosine_recall@1 | 0.0609 | |
|
| cosine_recall@3 | 0.213 | |
|
| cosine_recall@5 | 0.3043 | |
|
| cosine_recall@10 | 0.5565 | |
|
| cosine_ndcg@10 | 0.2638 | |
|
| cosine_mrr@10 | 0.176 | |
|
| **cosine_map@100** | **0.1934** | |
|
|
|
#### 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 | 0.0783 | |
|
| cosine_accuracy@3 | 0.2174 | |
|
| cosine_accuracy@5 | 0.3435 | |
|
| cosine_accuracy@10 | 0.5696 | |
|
| cosine_precision@1 | 0.0783 | |
|
| cosine_precision@3 | 0.0725 | |
|
| cosine_precision@5 | 0.0687 | |
|
| cosine_precision@10 | 0.057 | |
|
| cosine_recall@1 | 0.0783 | |
|
| cosine_recall@3 | 0.2174 | |
|
| cosine_recall@5 | 0.3435 | |
|
| cosine_recall@10 | 0.5696 | |
|
| cosine_ndcg@10 | 0.2812 | |
|
| cosine_mrr@10 | 0.1947 | |
|
| **cosine_map@100** | **0.2122** | |
|
|
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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 | 0.0522 | |
|
| cosine_accuracy@3 | 0.2087 | |
|
| cosine_accuracy@5 | 0.3174 | |
|
| cosine_accuracy@10 | 0.513 | |
|
| cosine_precision@1 | 0.0522 | |
|
| cosine_precision@3 | 0.0696 | |
|
| cosine_precision@5 | 0.0635 | |
|
| cosine_precision@10 | 0.0513 | |
|
| cosine_recall@1 | 0.0522 | |
|
| cosine_recall@3 | 0.2087 | |
|
| cosine_recall@5 | 0.3174 | |
|
| cosine_recall@10 | 0.513 | |
|
| cosine_ndcg@10 | 0.2483 | |
|
| cosine_mrr@10 | 0.1679 | |
|
| **cosine_map@100** | **0.1893** | |
|
|
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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 | 0.0565 | |
|
| cosine_accuracy@3 | 0.2261 | |
|
| cosine_accuracy@5 | 0.3261 | |
|
| cosine_accuracy@10 | 0.5435 | |
|
| cosine_precision@1 | 0.0565 | |
|
| cosine_precision@3 | 0.0754 | |
|
| cosine_precision@5 | 0.0652 | |
|
| cosine_precision@10 | 0.0543 | |
|
| cosine_recall@1 | 0.0565 | |
|
| cosine_recall@3 | 0.2261 | |
|
| cosine_recall@5 | 0.3261 | |
|
| cosine_recall@10 | 0.5435 | |
|
| cosine_ndcg@10 | 0.2661 | |
|
| cosine_mrr@10 | 0.182 | |
|
| **cosine_map@100** | **0.2004** | |
|
|
|
#### 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 | 0.0565 | |
|
| cosine_accuracy@3 | 0.2174 | |
|
| cosine_accuracy@5 | 0.3174 | |
|
| cosine_accuracy@10 | 0.5435 | |
|
| cosine_precision@1 | 0.0565 | |
|
| cosine_precision@3 | 0.0725 | |
|
| cosine_precision@5 | 0.0635 | |
|
| cosine_precision@10 | 0.0543 | |
|
| cosine_recall@1 | 0.0565 | |
|
| cosine_recall@3 | 0.2174 | |
|
| cosine_recall@5 | 0.3174 | |
|
| cosine_recall@10 | 0.5435 | |
|
| cosine_ndcg@10 | 0.2641 | |
|
| cosine_mrr@10 | 0.1797 | |
|
| **cosine_map@100** | **0.1971** | |
|
|
|
<!-- |
|
## 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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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
|
|
|
## Training Details |
|
|
|
### Training Dataset |
|
|
|
#### json |
|
|
|
* Dataset: json |
|
* Size: 5,520 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: 5 tokens</li><li>mean: 43.78 tokens</li><li>max: 117 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 20.5 tokens</li><li>max: 51 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>L’Ajuntament vol crear un banc de recursos on recollir tots els oferiments de la població i que servirà per atendre les necessitats de les famílies refugiades acollides al poble.</code> | <code>Quin és el paper de l’Ajuntament en la integració de les persones refugiades acollides?</code> | |
|
| <code>Aquest tipus d'actuació requereix la intervenció d'una persona tècnica competent que subscrigui el projecte o la documentació tècnica corresponent i que assumeixi la direcció facultativa de l'execució de les obres.</code> | <code>Quin és el requisit per a la intervenció d'una persona tècnica competent en les obres d'intervenció parcial interior en edificis amb elements catalogats?</code> | |
|
| <code>Aquest títol, adreçat a persones empadronades a Sant Quirze del Vallès, es concedirà segons el nivell d’ingressos, la condició d’edat o de discapacitat, en base als criteris específics que recull l’ordenança reguladora del sistema de tarifació social del transport públic municipal en autobús a Sant Quirze del Vallès.</code> | <code>Quin és el benefici de la TBUS GRATUÏTA per a les persones majors?</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
```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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|
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### Training Hyperparameters |
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#### 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 |
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- `load_best_model_at_end`: True |
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- `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 |
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- `fp16_full_eval`: False |
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- `tf32`: True |
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- `local_rank`: 0 |
|
- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `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 |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `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 |
|
- `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 | |
|
|:----------:|:-------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 0.4638 | 10 | 4.0375 | - | - | - | - | - | - | |
|
| 0.9275 | 20 | 3.2095 | - | - | - | - | - | - | |
|
| 0.9739 | 21 | - | 0.1772 | 0.1818 | 0.1967 | 0.1911 | 0.1417 | 0.1750 | |
|
| 1.3913 | 30 | 2.1843 | - | - | - | - | - | - | |
|
| 1.8551 | 40 | 1.6095 | - | - | - | - | - | - | |
|
| 1.9942 | 43 | - | 0.1889 | 0.1676 | 0.1961 | 0.1969 | 0.1834 | 0.1899 | |
|
| 2.3188 | 50 | 1.2099 | - | - | - | - | - | - | |
|
| 2.7826 | 60 | 0.909 | - | - | - | - | - | - | |
|
| 2.9681 | 64 | - | 0.1998 | 0.1977 | 0.2164 | 0.2030 | 0.1972 | 0.2156 | |
|
| 3.2464 | 70 | 0.7534 | - | - | - | - | - | - | |
|
| 3.7101 | 80 | 0.6339 | - | - | - | - | - | - | |
|
| 3.9884 | 86 | - | 0.2049 | 0.2024 | 0.1989 | 0.1935 | 0.2046 | 0.1949 | |
|
| 4.1739 | 90 | 0.5423 | - | - | - | - | - | - | |
|
| 4.6377 | 100 | 0.5135 | - | - | - | - | - | - | |
|
| 4.9623 | 107 | - | 0.1967 | 0.2199 | 0.1892 | 0.2113 | 0.1957 | 0.2037 | |
|
| 5.1014 | 110 | 0.4563 | - | - | - | - | - | - | |
|
| 5.5652 | 120 | 0.3837 | - | - | - | - | - | - | |
|
| 5.9826 | 129 | - | 0.2026 | 0.1898 | 0.1903 | 0.2035 | 0.2034 | 0.2187 | |
|
| 6.0290 | 130 | 0.3991 | - | - | - | - | - | - | |
|
| 6.4928 | 140 | 0.3996 | - | - | - | - | - | - | |
|
| 6.9565 | 150 | 0.3225 | 0.2053 | 0.1866 | 0.2046 | 0.2083 | 0.1822 | 0.2086 | |
|
| 7.4203 | 160 | 0.3407 | - | - | - | - | - | - | |
|
| 7.8841 | 170 | 0.2982 | - | - | - | - | - | - | |
|
| **7.9768** | **172** | **-** | **0.2092** | **0.2197** | **0.2005** | **0.2178** | **0.2063** | **0.2042** | |
|
| 8.3478 | 180 | 0.3169 | - | - | - | - | - | - | |
|
| 8.8116 | 190 | 0.2799 | - | - | - | - | - | - | |
|
| 8.9971 | 194 | - | 0.2053 | 0.2215 | 0.1929 | 0.2191 | 0.2106 | 0.2170 | |
|
| 9.2754 | 200 | 0.312 | - | - | - | - | - | - | |
|
| 9.7391 | 210 | 0.2684 | 0.1876 | 0.2004 | 0.1893 | 0.2122 | 0.1971 | 0.1934 | |
|
|
|
* 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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