Add new SentenceTransformer model.

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

README.md

@@ -0,0 +1,898 @@
+---
+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:5175
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: Caldrà executar l'obra comunicada prèviament d'acord amb les condicions
+    específiques que es contenen en el model normalitzat CT02.
+  sentences:
+  - Quin és el propòsit de la instal·lació d'un circ sense animals a la via pública?
+  - Quin és el destinatari de les dades bloquejades?
+  - Quin és el format de presentació de la comunicació prèvia?
+- source_sentence: Armes utilitzables en activitats lúdico-esportives d’airsoft i
+    paintball...
+  sentences:
+  - Quin és el paper de l'AFA en la venda de llibres?
+  - Quin és el benefici de tenir dades personals correctes?
+  - Quin és el tipus d'activitats que es poden practicar amb les armes de 4a categoria?
+- source_sentence: En les activitats sotmeses al règim d’autorització ambiental o
+    llicència municipal d’activitat (Annex I o Annex II de la Llei 20/2009) cal demanar
+    aquest certificat previ a la presentació de la sol·licitud d’autorització ambiental
+    o llicència municipal.
+  sentences:
+  - Quin és el benefici de tenir el certificat de compatibilitat urbanística en les
+    activitats sotmeses a llicència municipal d’activitat?
+  - Com puc controlar la recepció de propaganda electoral per correu?
+  - Quin és el benefici de la cessió d'un compostador domèstic per a l'entorn?
+- source_sentence: La persona interessada posa en coneixement de l’Administració,
+    les actuacions urbanístiques que pretén dur a terme consistents en l'apuntalament
+    o reforç provisional d'estructures existents fins a la intervenció definitiva.
+  sentences:
+  - Qui pot participar en el Consell d'Adolescents?
+  - Quin és el resultat de la presentació de la comunicació prèvia?
+  - Quin és el paper de la persona interessada en relació amb la presentació de la
+    comunicació prèvia?
+- source_sentence: La persona consumidora presenti la reclamació davant de l'entitat
+    acreditada en un termini superior a un any des de la data en què va presentar
+    la reclamació a l'empresa.
+  sentences:
+  - Quin és el tràmit per inscriure'm al Padró d'Habitants sense tenir constància
+    de la meva anterior residència?
+  - Quin és el resultat de la modificació substancial de la llicència d'obres en relació
+    a les autoritzacions administratives?
+  - Quin és el paper de l'entitat acreditada en la tramitació d'una reclamació?
+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: 0.057391304347826085
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.15304347826086956
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.23478260869565218
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.41739130434782606
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.057391304347826085
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.051014492753623186
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.04695652173913043
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.04173913043478261
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.057391304347826085
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.15304347826086956
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.23478260869565218
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.41739130434782606
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.20551130934080394
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.14188060731539
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.16516795239083046
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 768
+      type: dim_768
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.05565217391304348
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.16
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.24
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.40695652173913044
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.05565217391304348
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.05333333333333333
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.048
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.04069565217391305
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.05565217391304348
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.16
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.24
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.40695652173913044
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.20158774447839253
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.13959282263630102
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.16377775492511307
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 512
+      type: dim_512
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.06956521739130435
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.16695652173913045
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.24869565217391304
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.4260869565217391
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.06956521739130435
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.05565217391304348
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.04973913043478261
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.042608695652173914
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.06956521739130435
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.16695652173913045
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.24869565217391304
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.4260869565217391
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.21580306349457917
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.1526128364389235
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.1754746652296583
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.05565217391304348
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.16695652173913045
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.25217391304347825
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.42434782608695654
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.05565217391304348
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.05565217391304348
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.05043478260869566
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.042434782608695654
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.05565217391304348
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.16695652173913045
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.25217391304347825
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.42434782608695654
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.2100045076980214
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.14526432022084196
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.1684764968624273
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.06086956521739131
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.1617391304347826
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.2608695652173913
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.4434782608695652
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.06086956521739131
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.05391304347826087
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.05217391304347826
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.04434782608695652
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.06086956521739131
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.1617391304347826
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.2608695652173913
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.4434782608695652
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.21805066438366894
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.15018150448585244
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.17220421856187046
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.06086956521739131
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.15478260869565216
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.24521739130434783
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.42782608695652175
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.06086956521739131
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.05159420289855072
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.04904347826086957
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.042782608695652175
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.06086956521739131
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.15478260869565216
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.24521739130434783
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.42782608695652175
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.21079002748958972
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.14568875086266406
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.16756200348857653
+      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-v4-10ep")
+# Run inference
+sentences = [
+    "La persona consumidora presenti la reclamació davant de l'entitat acreditada en un termini superior a un any des de la data en què va presentar la reclamació a l'empresa.",
+    "Quin és el paper de l'entitat acreditada en la tramitació d'una reclamació?",
+    "Quin és el resultat de la modificació substancial de la llicència d'obres en relació a les autoritzacions administratives?",
+]
+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   | 0.0574     |
+| cosine_accuracy@3   | 0.153      |
+| cosine_accuracy@5   | 0.2348     |
+| cosine_accuracy@10  | 0.4174     |
+| cosine_precision@1  | 0.0574     |
+| cosine_precision@3  | 0.051      |
+| cosine_precision@5  | 0.047      |
+| cosine_precision@10 | 0.0417     |
+| cosine_recall@1     | 0.0574     |
+| cosine_recall@3     | 0.153      |
+| cosine_recall@5     | 0.2348     |
+| cosine_recall@10    | 0.4174     |
+| cosine_ndcg@10      | 0.2055     |
+| cosine_mrr@10       | 0.1419     |
+| **cosine_map@100**  | **0.1652** |
+
+#### 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   | 0.0557     |
+| cosine_accuracy@3   | 0.16       |
+| cosine_accuracy@5   | 0.24       |
+| cosine_accuracy@10  | 0.407      |
+| cosine_precision@1  | 0.0557     |
+| cosine_precision@3  | 0.0533     |
+| cosine_precision@5  | 0.048      |
+| cosine_precision@10 | 0.0407     |
+| cosine_recall@1     | 0.0557     |
+| cosine_recall@3     | 0.16       |
+| cosine_recall@5     | 0.24       |
+| cosine_recall@10    | 0.407      |
+| cosine_ndcg@10      | 0.2016     |
+| cosine_mrr@10       | 0.1396     |
+| **cosine_map@100**  | **0.1638** |
+
+#### 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   | 0.0696     |
+| cosine_accuracy@3   | 0.167      |
+| cosine_accuracy@5   | 0.2487     |
+| cosine_accuracy@10  | 0.4261     |
+| cosine_precision@1  | 0.0696     |
+| cosine_precision@3  | 0.0557     |
+| cosine_precision@5  | 0.0497     |
+| cosine_precision@10 | 0.0426     |
+| cosine_recall@1     | 0.0696     |
+| cosine_recall@3     | 0.167      |
+| cosine_recall@5     | 0.2487     |
+| cosine_recall@10    | 0.4261     |
+| cosine_ndcg@10      | 0.2158     |
+| cosine_mrr@10       | 0.1526     |
+| **cosine_map@100**  | **0.1755** |
+
+#### 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   | 0.0557     |
+| cosine_accuracy@3   | 0.167      |
+| cosine_accuracy@5   | 0.2522     |
+| cosine_accuracy@10  | 0.4243     |
+| cosine_precision@1  | 0.0557     |
+| cosine_precision@3  | 0.0557     |
+| cosine_precision@5  | 0.0504     |
+| cosine_precision@10 | 0.0424     |
+| cosine_recall@1     | 0.0557     |
+| cosine_recall@3     | 0.167      |
+| cosine_recall@5     | 0.2522     |
+| cosine_recall@10    | 0.4243     |
+| cosine_ndcg@10      | 0.21       |
+| cosine_mrr@10       | 0.1453     |
+| **cosine_map@100**  | **0.1685** |
+
+#### 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   | 0.0609     |
+| cosine_accuracy@3   | 0.1617     |
+| cosine_accuracy@5   | 0.2609     |
+| cosine_accuracy@10  | 0.4435     |
+| cosine_precision@1  | 0.0609     |
+| cosine_precision@3  | 0.0539     |
+| cosine_precision@5  | 0.0522     |
+| cosine_precision@10 | 0.0443     |
+| cosine_recall@1     | 0.0609     |
+| cosine_recall@3     | 0.1617     |
+| cosine_recall@5     | 0.2609     |
+| cosine_recall@10    | 0.4435     |
+| cosine_ndcg@10      | 0.2181     |
+| cosine_mrr@10       | 0.1502     |
+| **cosine_map@100**  | **0.1722** |
+
+#### 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   | 0.0609     |
+| cosine_accuracy@3   | 0.1548     |
+| cosine_accuracy@5   | 0.2452     |
+| cosine_accuracy@10  | 0.4278     |
+| cosine_precision@1  | 0.0609     |
+| cosine_precision@3  | 0.0516     |
+| cosine_precision@5  | 0.049      |
+| cosine_precision@10 | 0.0428     |
+| cosine_recall@1     | 0.0609     |
+| cosine_recall@3     | 0.1548     |
+| cosine_recall@5     | 0.2452     |
+| cosine_recall@10    | 0.4278     |
+| cosine_ndcg@10      | 0.2108     |
+| cosine_mrr@10       | 0.1457     |
+| **cosine_map@100**  | **0.1676** |
+
+<!--
+## 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
+
+#### json
+
+* Dataset: json
+* Size: 5,175 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: 43.23 tokens</li><li>max: 117 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 20.25 tokens</li><li>max: 46 tokens</li></ul> |
+* Samples:
+  | positive                                                                                                                                                              | anchor                                                                                           |
+  |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|
+  | <code>Aquest tràmit us permet consultar informació de les anotacions d'entrada i sortida que hi consten al registre de l'Ajuntament de Sant Quirze del Vallès.</code> | <code>Quin és el format de les dades de sortida del tràmit?</code>                               |
+  | <code>Tràmit a través del qual la persona interessada posa en coneixement de l’Ajuntament la voluntat de: ... Renunciar a una llicència prèviament atorgada.</code>   | <code>Quin és el resultat de la renúncia a una llicència urbanística prèviament atorgada?</code> |
+  | <code>D’acord amb el plànol d'ubicació de parades: Mercat de diumenges a Les Fonts</code>                                                                             | <code>Quin és el plànol d'ubicació de parades del mercat de diumenges a Les Fonts?</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 |
+|:---------:|:-------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
+| 0.4938    | 10      | 4.1082        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 0.9877    | 20      | 3.2445        | 0.1490                  | 0.1440                 | 0.1466                 | 0.1546                 | 0.1249                | 0.1521                 |
+| 1.4815    | 30      | 1.9296        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 1.9753    | 40      | 1.7067        | 0.1607                  | 0.1548                 | 0.1567                 | 0.1648                 | 0.1448                | 0.1593                 |
+| 2.4691    | 50      | 0.9578        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 2.9630    | 60      | 1.003         | 0.1640                  | 0.1699                 | 0.1660                 | 0.1695                 | 0.1568                | 0.1592                 |
+| 3.4568    | 70      | 0.6298        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 3.9506    | 80      | 0.7035        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 4.0       | 81      | -             | 0.1707                  | 0.1657                 | 0.1769                 | 0.1690                 | 0.1610                | 0.1719                 |
+| 4.4444    | 90      | 0.4606        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 4.9383    | 100     | 0.5131        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 4.9877    | 101     | -             | 0.1645                  | 0.1686                 | 0.1669                 | 0.1620                 | 0.1580                | 0.1722                 |
+| 5.4321    | 110     | 0.3748        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 5.9259    | 120     | 0.4799        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 5.9753    | 121     | -             | 0.1670                  | 0.1670                 | 0.1725                 | 0.1711                 | 0.1628                | 0.1715                 |
+| 6.4198    | 130     | 0.3237        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 6.9136    | 140     | 0.4132        | -                       | -                      | -                      | -                      | -                     | -                      |
+| **6.963** | **141** | **-**         | **0.1746**              | **0.1757**             | **0.1697**             | **0.1746**             | **0.1655**            | **0.1746**             |
+| 7.4074    | 150     | 0.3169        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 7.9012    | 160     | 0.3438        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 8.0       | 162     | -             | 0.1692                  | 0.1698                 | 0.1718                 | 0.1735                 | 0.1707                | 0.1656                 |
+| 8.3951    | 170     | 0.2987        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 8.8889    | 180     | 0.3193        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 8.9877    | 182     | -             | 0.1703                  | 0.1703                 | 0.1695                 | 0.1710                 | 0.1619                | 0.1666                 |
+| 9.3827    | 190     | 0.2883        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 9.8765    | 200     | 0.3098        | 0.1652                  | 0.1722                 | 0.1685                 | 0.1755                 | 0.1676                | 0.1638                 |
+
+* 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}
+}
+```
+
+<!--
+## 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.*
+-->

config.json

@@ -0,0 +1,28 @@
+{
+  "_name_or_path": "BAAI/bge-m3",
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 8194,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.1.1",
+    "transformers": "4.44.2",
+    "pytorch": "2.4.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

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+size 2271064456

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
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+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 8192,
+  "do_lower_case": false
+}

sentencepiece.bpe.model

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+size 5069051

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
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+    "lstrip": true,
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+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
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+    "single_word": false
+  },
+  "sep_token": {
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+    "lstrip": false,
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+    "single_word": false
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+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

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+size 17083053

tokenizer_config.json

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+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+  "model_max_length": 8192,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}