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 +1,881 @@
+---
+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:2844
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: La unió d’aquests dos documents conforma l’Informe d’Avaluació
+    de l’Edifici (IAE).
+  sentences:
+  - Quin és el requisit per a rebre els ajuts econòmics per a les empreses?
+  - Quin és el resultat de la unió de la Inspecció Tècnica de l’Edifici (ITE) i dels
+    certificats energètics?
+  - Quin és el termini per sol·licitar la renovació del carnet de persona cuidadora?
+- source_sentence: La Inspecció Tècnica dels Edificis (ITE) permet identificar les
+    oportunitats de millora de l'eficiència energètica i implementar mesures de rehabilitació.
+  sentences:
+  - Quin és el benefici de l'activitat del Viver dels Avis de Sitges per a la qualitat
+    de vida?
+  - Com puc saber si puc ser cuidador?
+  - Quin és el paper de la Inspecció Tècnica dels Edificis (ITE) en la millora de
+    l'eficiència energètica?
+- source_sentence: A les zones blaves els parquímetres i serveis de pagament reconeixen
+    les matricules dels vehicles acreditats.
+  sentences:
+  - Quin és el paper de la mediació en una denúncia?
+  - Quin és el paper de les persones físiques?
+  - Quin és el procediment per estacionar a les zones blaves amb l'acreditació de
+    resident?
+- source_sentence: Els establiments oberts al públic destinats a espectacles cinematogràfics.
+    Els establiments oberts al públic destinats a espectacles públics i activitats
+    recreatives musicals amb un aforament autoritzat fins a 150 persones.
+  sentences:
+  - Quin és el resultat esperat després de la intervenció de l'Ajuntament en les denúncies
+    sanitàries?
+  - Quin és el requisit de superfície construïda per als restaurants musicals?
+  - Quins establiments oberts al públic han de comunicar la seva obertura a l'Ajuntament?
+- source_sentence: El Decret 97/2002, de 5 de març, regula la concessió de la targeta
+    d’aparcament per a persones amb disminució i altres mesures adreçades a facilitar
+    el desplaçament de les persones amb mobilitat reduïda.
+  sentences:
+  - Quin és el benefici de la targeta d'aparcament per a les persones amb disminució?
+  - Quin és el paper de la Junta de Govern Local?
+  - Quin és l'organisme que emet el certificat de serveis prestats?
+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: 0.11814345991561181
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.23277074542897327
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.3129395218002813
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.4644163150492264
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.11814345991561181
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.07759024847632442
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.06258790436005626
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.046441631504922636
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.11814345991561181
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.23277074542897327
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.3129395218002813
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.4644163150492264
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.26553370933458276
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.20527392672962277
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.22599508422976106
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 768
+      type: dim_768
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.11575246132208157
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.2289732770745429
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.3112517580872011
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.46568213783403656
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.11575246132208157
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.07632442569151429
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.062250351617440226
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.04656821378340366
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.11575246132208157
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.2289732770745429
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.3112517580872011
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.46568213783403656
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.26414039995115557
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.20311873507021158
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.22355973027797246
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 512
+      type: dim_512
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.11912798874824192
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.23277074542897327
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.31758087201125174
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.46582278481012657
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.11912798874824192
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.07759024847632444
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.06351617440225035
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.04658227848101265
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.11912798874824192
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.23277074542897327
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.31758087201125174
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.46582278481012657
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.26671990925029193
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.20635646194717913
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.22673055490318922
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.11533052039381153
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.22658227848101264
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.30857946554149085
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.45668073136427567
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.11533052039381153
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.07552742616033756
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.06171589310829817
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.04566807313642757
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.11533052039381153
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.22658227848101264
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.30857946554149085
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.45668073136427567
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.26044811042246035
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.20098218471636187
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.22169039893772347
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.11181434599156118
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.22334739803094233
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.30253164556962026
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.45288326300984527
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.11181434599156118
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.07444913267698076
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.06050632911392405
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.045288326300984526
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.11181434599156118
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.22334739803094233
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.30253164556962026
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.45288326300984527
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.2566428043422134
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.19724806331346384
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.21784479785600805
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.10689170182841069
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.21251758087201125
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.28846694796061884
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.42967651195499296
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.10689170182841069
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.07083919362400375
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.05769338959212378
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.0429676511954993
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.10689170182841069
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.21251758087201125
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.28846694796061884
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.42967651195499296
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.2438421466584992
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.1875642957604982
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.2080904354707231
+      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/ST-tramits-sitges-006-5ep")
+# Run inference
+sentences = [
+    'El Decret 97/2002, de 5 de març, regula la concessió de la targeta d’aparcament per a persones amb disminució i altres mesures adreçades a facilitar el desplaçament de les persones amb mobilitat reduïda.',
+    "Quin és el benefici de la targeta d'aparcament per a les persones amb disminució?",
+    'Quin és el paper de la Junta de Govern Local?',
+]
+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   | 0.1181    |
+| cosine_accuracy@3   | 0.2328    |
+| cosine_accuracy@5   | 0.3129    |
+| cosine_accuracy@10  | 0.4644    |
+| cosine_precision@1  | 0.1181    |
+| cosine_precision@3  | 0.0776    |
+| cosine_precision@5  | 0.0626    |
+| cosine_precision@10 | 0.0464    |
+| cosine_recall@1     | 0.1181    |
+| cosine_recall@3     | 0.2328    |
+| cosine_recall@5     | 0.3129    |
+| cosine_recall@10    | 0.4644    |
+| cosine_ndcg@10      | 0.2655    |
+| cosine_mrr@10       | 0.2053    |
+| **cosine_map@100**  | **0.226** |
+
+#### 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   | 0.1158     |
+| cosine_accuracy@3   | 0.229      |
+| cosine_accuracy@5   | 0.3113     |
+| cosine_accuracy@10  | 0.4657     |
+| cosine_precision@1  | 0.1158     |
+| cosine_precision@3  | 0.0763     |
+| cosine_precision@5  | 0.0623     |
+| cosine_precision@10 | 0.0466     |
+| cosine_recall@1     | 0.1158     |
+| cosine_recall@3     | 0.229      |
+| cosine_recall@5     | 0.3113     |
+| cosine_recall@10    | 0.4657     |
+| cosine_ndcg@10      | 0.2641     |
+| cosine_mrr@10       | 0.2031     |
+| **cosine_map@100**  | **0.2236** |
+
+#### 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   | 0.1191     |
+| cosine_accuracy@3   | 0.2328     |
+| cosine_accuracy@5   | 0.3176     |
+| cosine_accuracy@10  | 0.4658     |
+| cosine_precision@1  | 0.1191     |
+| cosine_precision@3  | 0.0776     |
+| cosine_precision@5  | 0.0635     |
+| cosine_precision@10 | 0.0466     |
+| cosine_recall@1     | 0.1191     |
+| cosine_recall@3     | 0.2328     |
+| cosine_recall@5     | 0.3176     |
+| cosine_recall@10    | 0.4658     |
+| cosine_ndcg@10      | 0.2667     |
+| cosine_mrr@10       | 0.2064     |
+| **cosine_map@100**  | **0.2267** |
+
+#### 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   | 0.1153     |
+| cosine_accuracy@3   | 0.2266     |
+| cosine_accuracy@5   | 0.3086     |
+| cosine_accuracy@10  | 0.4567     |
+| cosine_precision@1  | 0.1153     |
+| cosine_precision@3  | 0.0755     |
+| cosine_precision@5  | 0.0617     |
+| cosine_precision@10 | 0.0457     |
+| cosine_recall@1     | 0.1153     |
+| cosine_recall@3     | 0.2266     |
+| cosine_recall@5     | 0.3086     |
+| cosine_recall@10    | 0.4567     |
+| cosine_ndcg@10      | 0.2604     |
+| cosine_mrr@10       | 0.201      |
+| **cosine_map@100**  | **0.2217** |
+
+#### 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   | 0.1118     |
+| cosine_accuracy@3   | 0.2233     |
+| cosine_accuracy@5   | 0.3025     |
+| cosine_accuracy@10  | 0.4529     |
+| cosine_precision@1  | 0.1118     |
+| cosine_precision@3  | 0.0744     |
+| cosine_precision@5  | 0.0605     |
+| cosine_precision@10 | 0.0453     |
+| cosine_recall@1     | 0.1118     |
+| cosine_recall@3     | 0.2233     |
+| cosine_recall@5     | 0.3025     |
+| cosine_recall@10    | 0.4529     |
+| cosine_ndcg@10      | 0.2566     |
+| cosine_mrr@10       | 0.1972     |
+| **cosine_map@100**  | **0.2178** |
+
+#### 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   | 0.1069     |
+| cosine_accuracy@3   | 0.2125     |
+| cosine_accuracy@5   | 0.2885     |
+| cosine_accuracy@10  | 0.4297     |
+| cosine_precision@1  | 0.1069     |
+| cosine_precision@3  | 0.0708     |
+| cosine_precision@5  | 0.0577     |
+| cosine_precision@10 | 0.043      |
+| cosine_recall@1     | 0.1069     |
+| cosine_recall@3     | 0.2125     |
+| cosine_recall@5     | 0.2885     |
+| cosine_recall@10    | 0.4297     |
+| cosine_ndcg@10      | 0.2438     |
+| cosine_mrr@10       | 0.1876     |
+| **cosine_map@100**  | **0.2081** |
+
+<!--
+## 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: 2,844 training samples
+* Columns: <code>positive</code> and <code>anchor</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | positive                                                                           | anchor                                                                             |
+  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                             |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 49.45 tokens</li><li>max: 148 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 20.94 tokens</li><li>max: 45 tokens</li></ul> |
+* Samples:
+  | positive                                                                                                                                                                                                                                                                          | anchor                                                                                                               |
+  |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------|
+  | <code>L'Ajuntament de Sitges atorga subvencions per a projectes i activitats d'interès públic o social que tinguin per finalitat les activitats esportives federades, escolars o populars desenvolupades per les entitats esportives i esportistes del municipi de Sitges.</code> | <code>Quin és el benefici de les subvencions per a les entitats esportives?</code>                                   |
+  | <code>Per a poder ser beneficiari d'una subvenció per a un projecte o activitat cultural, les entitats o associacions culturals de Sitges han de tenir una seu social a la ciutat de Sitges i estar inscrites en el Registre d'Entitats de la Generalitat de Catalunya.</code>    | <code>Quin és el requisit per a poder ser beneficiari d'una subvenció per a un projecte o activitat cultural?</code> |
+  | <code>La cessió entre tercers, només es contempla en el cas de sepultures de construcció particular que hagin estat donades d'alta amb una anterioritat de 10 anys a la data de sol·licitud de la cessió.</code>                                                                  | <code>Quin és el paper de la persona que, legalment hi tingui dret, en la cessió entre tercers?</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`: 5
+- `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`: 5
+- `max_steps`: -1
+- `lr_scheduler_type`: cosine
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.2
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: True
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: True
+- `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 |
+|:----------:|:------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
+| 0.8989     | 10     | 3.2114        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 0.9888     | 11     | -             | 0.2144                  | 0.2008                 | 0.2070                 | 0.2126                 | 0.1842                | 0.2126                 |
+| 1.7978     | 20     | 1.5622        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 1.9775     | 22     | -             | 0.2179                  | 0.2101                 | 0.2169                 | 0.2180                 | 0.2012                | 0.2193                 |
+| 2.6966     | 30     | 0.7882        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 2.9663     | 33     | -             | 0.2239                  | 0.2162                 | 0.2220                 | 0.2238                 | 0.2070                | 0.2222                 |
+| 3.5955     | 40     | 0.4956        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 3.9551     | 44     | -             | 0.2270                  | 0.2177                 | 0.2231                 | 0.2278                 | 0.2084                | 0.2255                 |
+| 4.4944     | 50     | 0.392         | -                       | -                      | -                      | -                      | -                     | -                      |
+| **4.9438** | **55** | **-**         | **0.226**               | **0.2178**             | **0.2217**             | **0.2267**             | **0.2081**            | **0.2236**             |
+
+* 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"
+  ],
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+  "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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+    "type": "sentence_transformers.models.Pooling"
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+  {
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+    "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
+}

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+size 5069051

special_tokens_map.json

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+    "rstrip": false,
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+  "unk_token": "<unk>"
+}