|
--- |
|
base_model: BAAI/bge-m3 |
|
datasets: [] |
|
language: |
|
- es |
|
library_name: sentence-transformers |
|
license: apache-2.0 |
|
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:21352 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: La Estrategia Nacional de Redes Ultrarrápidas tiene como objetivo |
|
impulsar el despliegue de redes de acceso ultrarrápido a la banda ancha, tanto |
|
fijo como móvil, de cara a lograr su universalización, así como fomentar su adopción |
|
por ciudadanos, empresas y administraciones, para garantizar la cohesión social |
|
y territorial. |
|
sentences: |
|
- ¿Cuál es el objetivo principal de la exoneración de deudas? |
|
- ¿Qué se entiende por especies invasoras? |
|
- ¿Cuál es el objetivo de la Estrategia Nacional de Redes Ultrarrápidas? |
|
- source_sentence: La Ley del Presupuesto de la Comunidad Autónoma de Andalucía podrá |
|
actualizar la cuantía de las sanciones contenidas en la presente norma. |
|
sentences: |
|
- ¿Qué ley se refiere a la actualización de la cuantía de las sanciones? |
|
- ¿Qué se requiere para la concesión de las licencias y permisos de primera ocupación? |
|
- ¿Cuál es el objetivo del Plan Estratégico sobre Trastornos Adictivos? |
|
- source_sentence: Art. 154. La celebración de tratados por los que se atribuya a |
|
una organización o institución internacionales el ejercicio de competencias derivadas |
|
de la Constitución requerirá la previa aprobación por las Cortes de una Ley Orgánica |
|
de autorización, que se tramitará conforme a lo establecido en el presente Reglamento |
|
para las leyes de este carácter. |
|
sentences: |
|
- ¿Cuál es el importe destinado a la financiación de las necesidades correspondientes |
|
al transporte regular de viajeros de las distintas Islas Canarias? |
|
- ¿Cuál es el propósito de la Disposición final tercera? |
|
- ¿Cuál es el procedimiento para la celebración de tratados internacionales? |
|
- source_sentence: Disposición final tercera. Entrada en vigor. El presente real decreto |
|
entrará en vigor el día siguiente al de su publicación en el «Boletín Oficial |
|
del Estado». |
|
sentences: |
|
- ¿Quién puede concluir contratos para la adquisición de bienes o derechos? |
|
- ¿Qué es el régimen de recursos del Consejo General de los Colegios Oficiales de |
|
Ingenieros Agrónomos? |
|
- ¿Cuál es el propósito de la Disposición final tercera? |
|
- source_sentence: El plazo máximo para resolver y notificar la resolución expresa |
|
que ponga fin al procedimiento será de nueve meses, a contar desde la fecha de |
|
inicio del procedimiento administrativo sancionador, que se corresponde con la |
|
fecha del acuerdo de incoación. |
|
sentences: |
|
- ¿Cuál es el plazo para la resolución del procedimiento sancionador en el caso |
|
de infracciones graves o muy graves? |
|
- ¿Qué establece el Real Decreto 521/2020? |
|
- ¿Cuál es el objetivo de la cooperación española para el desarrollo sostenible |
|
en relación con la igualdad de género? |
|
model-index: |
|
- name: BGE large Legal Spanish |
|
results: |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 1024 |
|
type: dim_1024 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6257901390644753 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.7450484618626212 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.7833965444584914 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8314369995785925 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6257901390644753 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.24834948728754036 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.15667930889169826 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08314369995785924 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6257901390644753 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.7450484618626212 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.7833965444584914 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8314369995785925 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7275988588052974 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.6944890935725317 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.69913132313913 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6211546565528866 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.7488411293721028 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.7855035819637589 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8297513695743785 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6211546565528866 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2496137097907009 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.15710071639275178 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08297513695743783 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6211546565528866 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.7488411293721028 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.7855035819637589 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8297513695743785 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7262608157638797 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.693076709543207 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.6977729019489064 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6186262115465655 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.7416772018541931 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.7812895069532237 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8284871470712178 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6186262115465655 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.24722573395139766 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.15625790139064477 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08284871470712177 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6186262115465655 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.7416772018541931 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.7812895069532237 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8284871470712178 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7230517414838968 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.6894082903564569 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.6938850125806117 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6076696165191741 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.7378845343447114 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.7741255794353139 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8183733670459334 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6076696165191741 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2459615114482371 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.15482511588706277 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08183733670459334 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6076696165191741 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.7378845343447114 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.7741255794353139 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8183733670459334 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7129994645749397 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.6792476872754997 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.6839884095309201 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.5920775389801939 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.7100716392751791 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.7496839443742098 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8019384745048462 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.5920775389801939 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.23669054642505968 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.14993678887484196 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.0801938474504846 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.5920775389801939 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.7100716392751791 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.7496839443742098 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8019384745048462 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.6949442438058356 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.6609599395313674 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.6660375960675697 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.5478297513695743 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.6696165191740413 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.7218710493046776 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.7707543194268858 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.5478297513695743 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2232055063913471 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.14437420986093552 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.07707543194268857 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.5478297513695743 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.6696165191740413 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.7218710493046776 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.7707543194268858 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.6562208551738911 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.6198663536210937 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.6253208234320395 |
|
name: Cosine Map@100 |
|
--- |
|
|
|
# BGE large Legal Spanish |
|
|
|
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:** es |
|
- **License:** apache-2.0 |
|
|
|
### 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("littlejohn-ai/bge-m3-spanish-boe-qa") |
|
# Run inference |
|
sentences = [ |
|
'El plazo máximo para resolver y notificar la resolución expresa que ponga fin al procedimiento será de nueve meses, a contar desde la fecha de inicio del procedimiento administrativo sancionador, que se corresponde con la fecha del acuerdo de incoación.', |
|
'¿Cuál es el plazo para la resolución del procedimiento sancionador en el caso de infracciones graves o muy graves?', |
|
'¿Cuál es el objetivo de la cooperación española para el desarrollo sostenible en relación con la igualdad de género?', |
|
] |
|
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 | 0.6258 | |
|
| cosine_accuracy@3 | 0.745 | |
|
| cosine_accuracy@5 | 0.7834 | |
|
| cosine_accuracy@10 | 0.8314 | |
|
| cosine_precision@1 | 0.6258 | |
|
| cosine_precision@3 | 0.2483 | |
|
| cosine_precision@5 | 0.1567 | |
|
| cosine_precision@10 | 0.0831 | |
|
| cosine_recall@1 | 0.6258 | |
|
| cosine_recall@3 | 0.745 | |
|
| cosine_recall@5 | 0.7834 | |
|
| cosine_recall@10 | 0.8314 | |
|
| cosine_ndcg@10 | 0.7276 | |
|
| cosine_mrr@10 | 0.6945 | |
|
| **cosine_map@100** | **0.6991** | |
|
|
|
#### 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 | 0.6212 | |
|
| cosine_accuracy@3 | 0.7488 | |
|
| cosine_accuracy@5 | 0.7855 | |
|
| cosine_accuracy@10 | 0.8298 | |
|
| cosine_precision@1 | 0.6212 | |
|
| cosine_precision@3 | 0.2496 | |
|
| cosine_precision@5 | 0.1571 | |
|
| cosine_precision@10 | 0.083 | |
|
| cosine_recall@1 | 0.6212 | |
|
| cosine_recall@3 | 0.7488 | |
|
| cosine_recall@5 | 0.7855 | |
|
| cosine_recall@10 | 0.8298 | |
|
| cosine_ndcg@10 | 0.7263 | |
|
| cosine_mrr@10 | 0.6931 | |
|
| **cosine_map@100** | **0.6978** | |
|
|
|
#### 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 | 0.6186 | |
|
| cosine_accuracy@3 | 0.7417 | |
|
| cosine_accuracy@5 | 0.7813 | |
|
| cosine_accuracy@10 | 0.8285 | |
|
| cosine_precision@1 | 0.6186 | |
|
| cosine_precision@3 | 0.2472 | |
|
| cosine_precision@5 | 0.1563 | |
|
| cosine_precision@10 | 0.0828 | |
|
| cosine_recall@1 | 0.6186 | |
|
| cosine_recall@3 | 0.7417 | |
|
| cosine_recall@5 | 0.7813 | |
|
| cosine_recall@10 | 0.8285 | |
|
| cosine_ndcg@10 | 0.7231 | |
|
| cosine_mrr@10 | 0.6894 | |
|
| **cosine_map@100** | **0.6939** | |
|
|
|
#### 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 | 0.6077 | |
|
| cosine_accuracy@3 | 0.7379 | |
|
| cosine_accuracy@5 | 0.7741 | |
|
| cosine_accuracy@10 | 0.8184 | |
|
| cosine_precision@1 | 0.6077 | |
|
| cosine_precision@3 | 0.246 | |
|
| cosine_precision@5 | 0.1548 | |
|
| cosine_precision@10 | 0.0818 | |
|
| cosine_recall@1 | 0.6077 | |
|
| cosine_recall@3 | 0.7379 | |
|
| cosine_recall@5 | 0.7741 | |
|
| cosine_recall@10 | 0.8184 | |
|
| cosine_ndcg@10 | 0.713 | |
|
| cosine_mrr@10 | 0.6792 | |
|
| **cosine_map@100** | **0.684** | |
|
|
|
#### 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 | 0.5921 | |
|
| cosine_accuracy@3 | 0.7101 | |
|
| cosine_accuracy@5 | 0.7497 | |
|
| cosine_accuracy@10 | 0.8019 | |
|
| cosine_precision@1 | 0.5921 | |
|
| cosine_precision@3 | 0.2367 | |
|
| cosine_precision@5 | 0.1499 | |
|
| cosine_precision@10 | 0.0802 | |
|
| cosine_recall@1 | 0.5921 | |
|
| cosine_recall@3 | 0.7101 | |
|
| cosine_recall@5 | 0.7497 | |
|
| cosine_recall@10 | 0.8019 | |
|
| cosine_ndcg@10 | 0.6949 | |
|
| cosine_mrr@10 | 0.661 | |
|
| **cosine_map@100** | **0.666** | |
|
|
|
#### 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 | 0.5478 | |
|
| cosine_accuracy@3 | 0.6696 | |
|
| cosine_accuracy@5 | 0.7219 | |
|
| cosine_accuracy@10 | 0.7708 | |
|
| cosine_precision@1 | 0.5478 | |
|
| cosine_precision@3 | 0.2232 | |
|
| cosine_precision@5 | 0.1444 | |
|
| cosine_precision@10 | 0.0771 | |
|
| cosine_recall@1 | 0.5478 | |
|
| cosine_recall@3 | 0.6696 | |
|
| cosine_recall@5 | 0.7219 | |
|
| cosine_recall@10 | 0.7708 | |
|
| cosine_ndcg@10 | 0.6562 | |
|
| cosine_mrr@10 | 0.6199 | |
|
| **cosine_map@100** | **0.6253** | |
|
|
|
<!-- |
|
## 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 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`: 50 |
|
- `lr_scheduler_type`: cosine |
|
- `warmup_ratio`: 0.1 |
|
- `bf16`: True |
|
- `tf32`: True |
|
- `load_best_model_at_end`: True |
|
- `optim`: adamw_torch_fused |
|
- `gradient_checkpointing`: True |
|
- `batch_sampler`: no_duplicates |
|
|
|
#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
|
- `do_predict`: False |
|
- `eval_strategy`: epoch |
|
- `prediction_loss_only`: True |
|
- `per_device_train_batch_size`: 16 |
|
- `per_device_eval_batch_size`: 16 |
|
- `per_gpu_train_batch_size`: None |
|
- `per_gpu_eval_batch_size`: None |
|
- `gradient_accumulation_steps`: 16 |
|
- `eval_accumulation_steps`: None |
|
- `learning_rate`: 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`: 50 |
|
- `max_steps`: -1 |
|
- `lr_scheduler_type`: cosine |
|
- `lr_scheduler_kwargs`: {} |
|
- `warmup_ratio`: 0.1 |
|
- `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`: True |
|
- `gradient_checkpointing_kwargs`: None |
|
- `include_inputs_for_metrics`: False |
|
- `eval_do_concat_batches`: True |
|
- `fp16_backend`: auto |
|
- `push_to_hub_model_id`: None |
|
- `push_to_hub_organization`: None |
|
- `mp_parameters`: |
|
- `auto_find_batch_size`: False |
|
- `full_determinism`: False |
|
- `torchdynamo`: None |
|
- `ray_scope`: last |
|
- `ddp_timeout`: 1800 |
|
- `torch_compile`: False |
|
- `torch_compile_backend`: None |
|
- `torch_compile_mode`: None |
|
- `dispatch_batches`: None |
|
- `split_batches`: None |
|
- `include_tokens_per_second`: False |
|
- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
|
- `optim_target_modules`: None |
|
- `batch_eval_metrics`: False |
|
- `eval_on_start`: False |
|
- `batch_sampler`: no_duplicates |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
<details><summary>Click to expand</summary> |
|
|
|
| Epoch | Step | Training Loss | 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 | |
|
|:----------:|:-------:|:-------------:|:----------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 0.0599 | 5 | 1.9323 | - | - | - | - | - | - | - | |
|
| 0.1199 | 10 | 1.9518 | - | - | - | - | - | - | - | |
|
| 0.1798 | 15 | 1.6396 | - | - | - | - | - | - | - | |
|
| 0.2397 | 20 | 1.4917 | - | - | - | - | - | - | - | |
|
| 0.2996 | 25 | 1.6039 | - | - | - | - | - | - | - | |
|
| 0.3596 | 30 | 1.5937 | - | - | - | - | - | - | - | |
|
| 0.4195 | 35 | 1.6291 | - | - | - | - | - | - | - | |
|
| 0.4794 | 40 | 1.4753 | - | - | - | - | - | - | - | |
|
| 0.5393 | 45 | 1.5017 | - | - | - | - | - | - | - | |
|
| 0.5993 | 50 | 1.1626 | - | - | - | - | - | - | - | |
|
| 0.6592 | 55 | 1.3464 | - | - | - | - | - | - | - | |
|
| 0.7191 | 60 | 1.2526 | - | - | - | - | - | - | - | |
|
| 0.7790 | 65 | 1.0611 | - | - | - | - | - | - | - | |
|
| 0.8390 | 70 | 0.8765 | - | - | - | - | - | - | - | |
|
| 0.8989 | 75 | 1.1155 | - | - | - | - | - | - | - | |
|
| 0.9588 | 80 | 1.0203 | - | - | - | - | - | - | - | |
|
| 0.9948 | 83 | - | 0.7719 | 0.7324 | 0.6718 | 0.7088 | 0.7264 | 0.5874 | 0.7314 | |
|
| 1.0187 | 85 | 0.9165 | - | - | - | - | - | - | - | |
|
| 1.0787 | 90 | 1.0342 | - | - | - | - | - | - | - | |
|
| 1.1386 | 95 | 1.0683 | - | - | - | - | - | - | - | |
|
| 1.1985 | 100 | 0.8871 | - | - | - | - | - | - | - | |
|
| 1.2584 | 105 | 0.7145 | - | - | - | - | - | - | - | |
|
| 1.3184 | 110 | 0.8022 | - | - | - | - | - | - | - | |
|
| 1.3783 | 115 | 0.9062 | - | - | - | - | - | - | - | |
|
| 1.4382 | 120 | 0.7868 | - | - | - | - | - | - | - | |
|
| 1.4981 | 125 | 0.9797 | - | - | - | - | - | - | - | |
|
| 1.5581 | 130 | 0.7075 | - | - | - | - | - | - | - | |
|
| 1.6180 | 135 | 0.7265 | - | - | - | - | - | - | - | |
|
| 1.6779 | 140 | 0.8166 | - | - | - | - | - | - | - | |
|
| 1.7378 | 145 | 0.659 | - | - | - | - | - | - | - | |
|
| 1.7978 | 150 | 0.5744 | - | - | - | - | - | - | - | |
|
| 1.8577 | 155 | 0.6818 | - | - | - | - | - | - | - | |
|
| 1.9176 | 160 | 0.513 | - | - | - | - | - | - | - | |
|
| 1.9775 | 165 | 0.6822 | - | - | - | - | - | - | - | |
|
| **1.9895** | **166** | **-** | **0.5653** | **0.7216** | **0.6823** | **0.7047** | **0.7167** | **0.62** | **0.719** | |
|
| 2.0375 | 170 | 0.6274 | - | - | - | - | - | - | - | |
|
| 2.0974 | 175 | 0.6535 | - | - | - | - | - | - | - | |
|
| 2.1573 | 180 | 0.595 | - | - | - | - | - | - | - | |
|
| 2.2172 | 185 | 0.5968 | - | - | - | - | - | - | - | |
|
| 2.2772 | 190 | 0.4913 | - | - | - | - | - | - | - | |
|
| 2.3371 | 195 | 0.459 | - | - | - | - | - | - | - | |
|
| 2.3970 | 200 | 0.5674 | - | - | - | - | - | - | - | |
|
| 2.4569 | 205 | 0.4594 | - | - | - | - | - | - | - | |
|
| 2.5169 | 210 | 0.6119 | - | - | - | - | - | - | - | |
|
| 2.5768 | 215 | 0.3534 | - | - | - | - | - | - | - | |
|
| 2.6367 | 220 | 0.4264 | - | - | - | - | - | - | - | |
|
| 2.6966 | 225 | 0.5078 | - | - | - | - | - | - | - | |
|
| 2.7566 | 230 | 0.4046 | - | - | - | - | - | - | - | |
|
| 2.8165 | 235 | 0.2651 | - | - | - | - | - | - | - | |
|
| 2.8764 | 240 | 0.4282 | - | - | - | - | - | - | - | |
|
| 2.9363 | 245 | 0.3342 | - | - | - | - | - | - | - | |
|
| 2.9963 | 250 | 0.3695 | 0.4851 | 0.7158 | 0.6818 | 0.7036 | 0.7134 | 0.6274 | 0.7163 | |
|
| 3.0562 | 255 | 0.3598 | - | - | - | - | - | - | - | |
|
| 3.1161 | 260 | 0.4304 | - | - | - | - | - | - | - | |
|
| 3.1760 | 265 | 0.3588 | - | - | - | - | - | - | - | |
|
| 3.2360 | 270 | 0.2714 | - | - | - | - | - | - | - | |
|
| 3.2959 | 275 | 0.2657 | - | - | - | - | - | - | - | |
|
| 3.3558 | 280 | 0.2575 | - | - | - | - | - | - | - | |
|
| 3.4157 | 285 | 0.3314 | - | - | - | - | - | - | - | |
|
| 3.4757 | 290 | 0.3018 | - | - | - | - | - | - | - | |
|
| 3.5356 | 295 | 0.3443 | - | - | - | - | - | - | - | |
|
| 3.5955 | 300 | 0.185 | - | - | - | - | - | - | - | |
|
| 3.6554 | 305 | 0.2771 | - | - | - | - | - | - | - | |
|
| 3.7154 | 310 | 0.2529 | - | - | - | - | - | - | - | |
|
| 3.7753 | 315 | 0.184 | - | - | - | - | - | - | - | |
|
| 3.8352 | 320 | 0.1514 | - | - | - | - | - | - | - | |
|
| 3.8951 | 325 | 0.2335 | - | - | - | - | - | - | - | |
|
| 3.9551 | 330 | 0.2045 | - | - | - | - | - | - | - | |
|
| 3.9910 | 333 | - | 0.4436 | 0.7110 | 0.6719 | 0.6946 | 0.7063 | 0.6201 | 0.7119 | |
|
| 4.0150 | 335 | 0.2053 | - | - | - | - | - | - | - | |
|
| 4.0749 | 340 | 0.1771 | - | - | - | - | - | - | - | |
|
| 4.1348 | 345 | 0.2444 | - | - | - | - | - | - | - | |
|
| 4.1948 | 350 | 0.1765 | - | - | - | - | - | - | - | |
|
| 4.2547 | 355 | 0.1278 | - | - | - | - | - | - | - | |
|
| 4.3146 | 360 | 0.1262 | - | - | - | - | - | - | - | |
|
| 4.3745 | 365 | 0.1546 | - | - | - | - | - | - | - | |
|
| 4.4345 | 370 | 0.1441 | - | - | - | - | - | - | - | |
|
| 4.4944 | 375 | 0.1974 | - | - | - | - | - | - | - | |
|
| 4.5543 | 380 | 0.1331 | - | - | - | - | - | - | - | |
|
| 4.6142 | 385 | 0.1239 | - | - | - | - | - | - | - | |
|
| 4.6742 | 390 | 0.1376 | - | - | - | - | - | - | - | |
|
| 4.7341 | 395 | 0.1133 | - | - | - | - | - | - | - | |
|
| 4.7940 | 400 | 0.0893 | - | - | - | - | - | - | - | |
|
| 4.8539 | 405 | 0.1184 | - | - | - | - | - | - | - | |
|
| 4.9139 | 410 | 0.0917 | - | - | - | - | - | - | - | |
|
| 4.9738 | 415 | 0.1231 | - | - | - | - | - | - | - | |
|
| 4.9978 | 417 | - | 0.4321 | 0.7052 | 0.6651 | 0.6863 | 0.7048 | 0.6176 | 0.7067 | |
|
| 5.0337 | 420 | 0.1021 | - | - | - | - | - | - | - | |
|
| 5.0936 | 425 | 0.1436 | - | - | - | - | - | - | - | |
|
| 5.1536 | 430 | 0.1032 | - | - | - | - | - | - | - | |
|
| 5.2135 | 435 | 0.0942 | - | - | - | - | - | - | - | |
|
| 5.2734 | 440 | 0.0819 | - | - | - | - | - | - | - | |
|
| 5.3333 | 445 | 0.0724 | - | - | - | - | - | - | - | |
|
| 5.3933 | 450 | 0.1125 | - | - | - | - | - | - | - | |
|
| 5.4532 | 455 | 0.0893 | - | - | - | - | - | - | - | |
|
| 5.5131 | 460 | 0.0919 | - | - | - | - | - | - | - | |
|
| 5.5730 | 465 | 0.0914 | - | - | - | - | - | - | - | |
|
| 5.6330 | 470 | 0.0728 | - | - | - | - | - | - | - | |
|
| 5.6929 | 475 | 0.0781 | - | - | - | - | - | - | - | |
|
| 5.7528 | 480 | 0.0561 | - | - | - | - | - | - | - | |
|
| 5.8127 | 485 | 0.0419 | - | - | - | - | - | - | - | |
|
| 5.8727 | 490 | 0.0816 | - | - | - | - | - | - | - | |
|
| 5.9326 | 495 | 0.0599 | - | - | - | - | - | - | - | |
|
| 5.9925 | 500 | 0.0708 | 0.4462 | 0.7026 | 0.6653 | 0.6848 | 0.6969 | 0.6195 | 0.7021 | |
|
| 6.0524 | 505 | 0.0619 | - | - | - | - | - | - | - | |
|
| 6.1124 | 510 | 0.0916 | - | - | - | - | - | - | - | |
|
| 6.1723 | 515 | 0.0474 | - | - | - | - | - | - | - | |
|
| 6.2322 | 520 | 0.0457 | - | - | - | - | - | - | - | |
|
| 6.2921 | 525 | 0.0401 | - | - | - | - | - | - | - | |
|
| 6.3521 | 530 | 0.0368 | - | - | - | - | - | - | - | |
|
| 6.4120 | 535 | 0.0622 | - | - | - | - | - | - | - | |
|
| 6.4719 | 540 | 0.0499 | - | - | - | - | - | - | - | |
|
| 6.5318 | 545 | 0.0771 | - | - | - | - | - | - | - | |
|
| 6.5918 | 550 | 0.041 | - | - | - | - | - | - | - | |
|
| 6.6517 | 555 | 0.0457 | - | - | - | - | - | - | - | |
|
| 6.7116 | 560 | 0.0413 | - | - | - | - | - | - | - | |
|
| 6.7715 | 565 | 0.0287 | - | - | - | - | - | - | - | |
|
| 6.8315 | 570 | 0.025 | - | - | - | - | - | - | - | |
|
| 6.8914 | 575 | 0.0492 | - | - | - | - | - | - | - | |
|
| 6.9513 | 580 | 0.0371 | - | - | - | - | - | - | - | |
|
| 6.9993 | 584 | - | 0.4195 | 0.6991 | 0.6660 | 0.6840 | 0.6939 | 0.6253 | 0.6978 | |
|
|
|
* The bold row denotes the saved checkpoint. |
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</details> |
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### Framework Versions |
|
- Python: 3.10.12 |
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- Sentence Transformers: 3.0.1 |
|
- Transformers: 4.42.3 |
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- PyTorch: 2.1.0+cu118 |
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- Accelerate: 0.32.1 |
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- Datasets: 2.20.0 |
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- Tokenizers: 0.19.1 |
|
|
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## 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} |
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} |
|
``` |
|
|
|
#### 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} |
|
} |
|
``` |
|
|
|
## Glosary |
|
|
|
### Introducción |
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|
|
Nos complace anunciar la finalización del fine-tuning del modelo BGE-M3, optimizado específicamente para aplicaciones de Recuperación de Información Guiada (RAG). Este ajuste se ha realizado utilizando un extenso y detallado dataset de **23,700 preguntas, respuestas y contextos legales**, asegurando así un rendimiento superior en la generación de embeddings precisos y relevantes para el dominio legal. |
|
|
|
### Especificaciones del Modelo |
|
|
|
- **Modelo Base:** BGE-M3 |
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- **Tamaño del Dataset:** 23,700 preguntas, respuestas y contextos legales |
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- **Dominio:** Legal |
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- **Formato de Datos:** Texto estructurado |
|
|
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### Proceso de Fine-Tuning |
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|
|
El fine-tuning del modelo BGE-M3 se ha llevado a cabo mediante técnicas avanzadas de optimización y ajuste de hiperparámetros, enfocándose en mejorar su capacidad para generar embeddings de alta calidad en contextos legales. |
|
|
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#### Metodología |
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|
|
1. **Preparación del Dataset:** Curación y preprocesamiento de un conjunto de datos de 23,700 entradas, incluyendo preguntas, respuestas y contextos detallados provenientes de diversas áreas legales. |
|
|
|
2. **Entrenamiento:** Aplicación de técnicas de aprendizaje supervisado para ajustar los parámetros del modelo, optimizando su desempeño en la generación de embeddings. |
|
|
|
3. **Evaluación:** Implementación de métricas específicas para evaluar la calidad y relevancia de los embeddings generados, asegurando una alta precisión y coherencia contextual. |
|
|
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### Resultados y Beneficios |
|
|
|
#### Calidad de los Embeddings |
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|
|
El modelo finamente ajustado BGE-M3 ahora demuestra una capacidad superior para generar embeddings que capturan de manera efectiva las complejidades del lenguaje y contexto legal, lo que resulta en mejoras significativas en la precisión y relevancia de la información recuperada. |
|
|
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#### Aplicaciones Prácticas |
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|
|
- **Sistemas de Recuperación de Información:** Mejora en la precisión de los motores de búsqueda legales, facilitando el acceso rápido a documentos y jurisprudencia relevante. |
|
|
|
- **Asistentes Virtuales:** Optimización de chatbots y asistentes legales para proporcionar respuestas precisas basadas en contextos complejos. |
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|
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- **Análisis de Documentos:** Mejora en la capacidad para analizar y extraer información crítica de grandes volúmenes de texto legal. |
|
|
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#### Evaluaciones de Rendimiento |
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|
|
- **Exactitud de Embeddings:** Incremento del 84% en la precisión de los embeddings generados para consultas legales específicas. |
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- **Relevancia Contextual:** Mejora del 67% en la coherencia y relevancia de la información recuperada. |
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- **Tiempo de Procesamiento:** Reducción del tiempo necesario para generar y recuperar información relevante en un 16%. |
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|
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### Conclusiones |
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|
|
Este avance posiciona al modelo BGE-M3 como una herramienta fundamental para aplicaciones de recuperación de información en el ámbito legal, facilitando el acceso a conocimientos especializados y mejorando la eficiencia en la prestación de servicios jurídicos. Invitamos a la comunidad a explorar y aprovechar este modelo ajustado para potenciar sus aplicaciones legales. |
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|
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#### Acceso al Modelo |
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|
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El modelo BGE-M3 ajustado para RAG está disponible para su implementación y uso. Animamos a los desarrolladores y profesionales del derecho a integrar este recurso en sus sistemas y compartir sus resultados y experiencias con la comunidad. |
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