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 +1,896 @@
+---
+base_model: BAAI/bge-m3
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:5520
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: Queda exclosa de la prohibició, dintre de les àrees recreatives
+    i d'acampada i en parcel·les de les urbanitzacions, la utilització dels fogons
+    de gas i de barbacoes d'obra amb mataguspires.
+  sentences:
+  - Què està prohibit fer en àrees d'acampada?
+  - Quin és el benefici de la reserva d'un equipament municipal?
+  - Quin és el benefici de la targeta d'aparcament individual per a l'autonomia personal?
+- source_sentence: Aquest tràmit permet participar en processos oberts de selecció
+    i provisió de personal de l'Ajuntament, i fer el pagament de la taxa per drets
+    d'examen establerta en la convocatòria.
+  sentences:
+  - Quin és el requisit per participar en un procés de selecció de personal de l'Ajuntament?
+  - On es pot trobar la relació de requeriments de documentació per a l'ajut de menjador
+    escolar?
+  - Quin és el tipus d'activitats que es poden practicar amb les armes de 4a categoria?
+- source_sentence: Sol·licitar la cessió temporal d’un compostador domèstic.
+  sentences:
+  - Quin és el requisit per a la tala d'arbres aïllats en sòl urbà?
+  - Quin és el paper de la persona interessada en aquest tràmit?
+  - Quin és el paper del compostador domèstic en la reducció de les emissions de gasos
+    d'efecte hivernacle?
+- source_sentence: Matriculació a l'Escola Bressol Municipal El Patufet.
+  sentences:
+  - Quin és el termini màxim per a deutes de 1.500,01 fins a 6.000,00 euros en el
+    criteri excepcional?
+  - Quin és el lloc on es realitza el tràmit de matrícula?
+  - Quin és el lloc on es realitza el taller 'Informàtica nivell bàsic'?
+- source_sentence: Aquest tipus de transmissió entre cedent i cessionari només podrà
+    ser de caràcter gratuït i no condicionada.
+  sentences:
+  - Quin és el caràcter de la transmissió de drets funeraris entre cedent i cessionari?
+  - Quin és el propòsit de la comunicació prèvia en relació amb la intervenció definitiva?
+  - Quin és el propòsit de la Deixalleria municipal?
+model-index:
+- name: SentenceTransformer based on BAAI/bge-m3
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 1024
+      type: dim_1024
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.04782608695652174
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.20869565217391303
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.30869565217391304
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.5565217391304348
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.04782608695652174
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.06956521739130433
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.061739130434782616
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.055652173913043466
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.04782608695652174
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.20869565217391303
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.30869565217391304
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.5565217391304348
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.25888429095047366
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.16955314009661854
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.18763324173665294
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 768
+      type: dim_768
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.06086956521739131
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.21304347826086956
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.30434782608695654
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.5565217391304348
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.06086956521739131
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.07101449275362319
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.06086956521739131
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.055652173913043466
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.06086956521739131
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.21304347826086956
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.30434782608695654
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.5565217391304348
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.2637812435357463
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.17599723947550047
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.19341889075062485
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 512
+      type: dim_512
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.0782608695652174
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.21739130434782608
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.34347826086956523
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.5695652173913044
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.0782608695652174
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.07246376811594202
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.06869565217391305
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.05695652173913043
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.0782608695652174
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.21739130434782608
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.34347826086956523
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.5695652173913044
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.28117776588045035
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.1947342995169084
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.21224466664057137
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.05217391304347826
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.20869565217391303
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.3173913043478261
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.5130434782608696
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.05217391304347826
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.06956521739130433
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.06347826086956522
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.05130434782608694
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.05217391304347826
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.20869565217391303
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.3173913043478261
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.5130434782608696
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.24833360148474737
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.16793305728088342
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.1892957688791951
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.05652173913043478
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.22608695652173913
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.32608695652173914
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.5434782608695652
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.05652173913043478
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.0753623188405797
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.06521739130434782
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.05434782608695651
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.05652173913043478
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.22608695652173913
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.32608695652173914
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.5434782608695652
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.2660596038952714
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.18197895100069028
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.20038255187663148
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.05652173913043478
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.21739130434782608
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.3173913043478261
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.5434782608695652
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.05652173913043478
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.07246376811594202
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.06347826086956522
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.054347826086956506
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.05652173913043478
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.21739130434782608
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.3173913043478261
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.5434782608695652
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.2641081743881476
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.17965838509316792
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.19707496290303578
+      name: Cosine Map@100
+---
+
+# SentenceTransformer based on BAAI/bge-m3
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) on the json dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 -->
+- **Maximum Sequence Length:** 8192 tokens
+- **Output Dimensionality:** 1024 tokens
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - json
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
+  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("adriansanz/sqv-v5-10ep")
+# Run inference
+sentences = [
+    'Aquest tipus de transmissió entre cedent i cessionari només podrà ser de caràcter gratuït i no condicionada.',
+    'Quin és el caràcter de la transmissió de drets funeraris entre cedent i cessionari?',
+    'Quin és el propòsit de la Deixalleria municipal?',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+* Dataset: `dim_1024`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.0478     |
+| cosine_accuracy@3   | 0.2087     |
+| cosine_accuracy@5   | 0.3087     |
+| cosine_accuracy@10  | 0.5565     |
+| cosine_precision@1  | 0.0478     |
+| cosine_precision@3  | 0.0696     |
+| cosine_precision@5  | 0.0617     |
+| cosine_precision@10 | 0.0557     |
+| cosine_recall@1     | 0.0478     |
+| cosine_recall@3     | 0.2087     |
+| cosine_recall@5     | 0.3087     |
+| cosine_recall@10    | 0.5565     |
+| cosine_ndcg@10      | 0.2589     |
+| cosine_mrr@10       | 0.1696     |
+| **cosine_map@100**  | **0.1876** |
+
+#### Information Retrieval
+* Dataset: `dim_768`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.0609     |
+| cosine_accuracy@3   | 0.213      |
+| cosine_accuracy@5   | 0.3043     |
+| cosine_accuracy@10  | 0.5565     |
+| cosine_precision@1  | 0.0609     |
+| cosine_precision@3  | 0.071      |
+| cosine_precision@5  | 0.0609     |
+| cosine_precision@10 | 0.0557     |
+| cosine_recall@1     | 0.0609     |
+| cosine_recall@3     | 0.213      |
+| cosine_recall@5     | 0.3043     |
+| cosine_recall@10    | 0.5565     |
+| cosine_ndcg@10      | 0.2638     |
+| cosine_mrr@10       | 0.176      |
+| **cosine_map@100**  | **0.1934** |
+
+#### Information Retrieval
+* Dataset: `dim_512`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.0783     |
+| cosine_accuracy@3   | 0.2174     |
+| cosine_accuracy@5   | 0.3435     |
+| cosine_accuracy@10  | 0.5696     |
+| cosine_precision@1  | 0.0783     |
+| cosine_precision@3  | 0.0725     |
+| cosine_precision@5  | 0.0687     |
+| cosine_precision@10 | 0.057      |
+| cosine_recall@1     | 0.0783     |
+| cosine_recall@3     | 0.2174     |
+| cosine_recall@5     | 0.3435     |
+| cosine_recall@10    | 0.5696     |
+| cosine_ndcg@10      | 0.2812     |
+| cosine_mrr@10       | 0.1947     |
+| **cosine_map@100**  | **0.2122** |
+
+#### Information Retrieval
+* Dataset: `dim_256`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.0522     |
+| cosine_accuracy@3   | 0.2087     |
+| cosine_accuracy@5   | 0.3174     |
+| cosine_accuracy@10  | 0.513      |
+| cosine_precision@1  | 0.0522     |
+| cosine_precision@3  | 0.0696     |
+| cosine_precision@5  | 0.0635     |
+| cosine_precision@10 | 0.0513     |
+| cosine_recall@1     | 0.0522     |
+| cosine_recall@3     | 0.2087     |
+| cosine_recall@5     | 0.3174     |
+| cosine_recall@10    | 0.513      |
+| cosine_ndcg@10      | 0.2483     |
+| cosine_mrr@10       | 0.1679     |
+| **cosine_map@100**  | **0.1893** |
+
+#### Information Retrieval
+* Dataset: `dim_128`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.0565     |
+| cosine_accuracy@3   | 0.2261     |
+| cosine_accuracy@5   | 0.3261     |
+| cosine_accuracy@10  | 0.5435     |
+| cosine_precision@1  | 0.0565     |
+| cosine_precision@3  | 0.0754     |
+| cosine_precision@5  | 0.0652     |
+| cosine_precision@10 | 0.0543     |
+| cosine_recall@1     | 0.0565     |
+| cosine_recall@3     | 0.2261     |
+| cosine_recall@5     | 0.3261     |
+| cosine_recall@10    | 0.5435     |
+| cosine_ndcg@10      | 0.2661     |
+| cosine_mrr@10       | 0.182      |
+| **cosine_map@100**  | **0.2004** |
+
+#### Information Retrieval
+* Dataset: `dim_64`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.0565     |
+| cosine_accuracy@3   | 0.2174     |
+| cosine_accuracy@5   | 0.3174     |
+| cosine_accuracy@10  | 0.5435     |
+| cosine_precision@1  | 0.0565     |
+| cosine_precision@3  | 0.0725     |
+| cosine_precision@5  | 0.0635     |
+| cosine_precision@10 | 0.0543     |
+| cosine_recall@1     | 0.0565     |
+| cosine_recall@3     | 0.2174     |
+| cosine_recall@5     | 0.3174     |
+| cosine_recall@10    | 0.5435     |
+| cosine_ndcg@10      | 0.2641     |
+| cosine_mrr@10       | 0.1797     |
+| **cosine_map@100**  | **0.1971** |
+
+<!--
+## Bias, Risks and Limitations
+
+*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: 5,520 training samples
+* Columns: <code>positive</code> and <code>anchor</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | positive                                                                           | anchor                                                                           |
+  |:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                           |
+  | details | <ul><li>min: 5 tokens</li><li>mean: 43.78 tokens</li><li>max: 117 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 20.5 tokens</li><li>max: 51 tokens</li></ul> |
+* Samples:
+  | positive                                                                                                                                                                                                                                                                                                                                    | anchor                                                                                                                                                                 |
+  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>L’Ajuntament vol crear un banc de recursos on recollir tots els oferiments de la població i que servirà per atendre les necessitats de les famílies refugiades acollides al poble.</code>                                                                                                                                             | <code>Quin és el paper de l’Ajuntament en la integració de les persones refugiades acollides?</code>                                                                   |
+  | <code>Aquest tipus d'actuació requereix la intervenció d'una persona tècnica competent que subscrigui el projecte o la documentació tècnica corresponent i que assumeixi la direcció facultativa de l'execució de les obres.</code>                                                                                                         | <code>Quin és el requisit per a la intervenció d'una persona tècnica competent en les obres d'intervenció parcial interior en edificis amb elements catalogats?</code> |
+  | <code>Aquest títol, adreçat a persones empadronades a Sant Quirze del Vallès, es concedirà segons el nivell d’ingressos, la condició d’edat o de discapacitat, en base als criteris específics que recull l’ordenança reguladora del sistema de tarifació social del transport públic municipal en autobús a Sant Quirze del Vallès.</code> | <code>Quin és el benefici de la TBUS GRATUÏTA per a les persones majors?</code>                                                                                        |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "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 |
+|:----------:|:-------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
+| 0.4638     | 10      | 4.0375        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 0.9275     | 20      | 3.2095        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 0.9739     | 21      | -             | 0.1772                  | 0.1818                 | 0.1967                 | 0.1911                 | 0.1417                | 0.1750                 |
+| 1.3913     | 30      | 2.1843        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 1.8551     | 40      | 1.6095        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 1.9942     | 43      | -             | 0.1889                  | 0.1676                 | 0.1961                 | 0.1969                 | 0.1834                | 0.1899                 |
+| 2.3188     | 50      | 1.2099        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 2.7826     | 60      | 0.909         | -                       | -                      | -                      | -                      | -                     | -                      |
+| 2.9681     | 64      | -             | 0.1998                  | 0.1977                 | 0.2164                 | 0.2030                 | 0.1972                | 0.2156                 |
+| 3.2464     | 70      | 0.7534        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 3.7101     | 80      | 0.6339        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 3.9884     | 86      | -             | 0.2049                  | 0.2024                 | 0.1989                 | 0.1935                 | 0.2046                | 0.1949                 |
+| 4.1739     | 90      | 0.5423        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 4.6377     | 100     | 0.5135        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 4.9623     | 107     | -             | 0.1967                  | 0.2199                 | 0.1892                 | 0.2113                 | 0.1957                | 0.2037                 |
+| 5.1014     | 110     | 0.4563        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 5.5652     | 120     | 0.3837        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 5.9826     | 129     | -             | 0.2026                  | 0.1898                 | 0.1903                 | 0.2035                 | 0.2034                | 0.2187                 |
+| 6.0290     | 130     | 0.3991        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 6.4928     | 140     | 0.3996        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 6.9565     | 150     | 0.3225        | 0.2053                  | 0.1866                 | 0.2046                 | 0.2083                 | 0.1822                | 0.2086                 |
+| 7.4203     | 160     | 0.3407        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 7.8841     | 170     | 0.2982        | -                       | -                      | -                      | -                      | -                     | -                      |
+| **7.9768** | **172** | **-**         | **0.2092**              | **0.2197**             | **0.2005**             | **0.2178**             | **0.2063**            | **0.2042**             |
+| 8.3478     | 180     | 0.3169        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 8.8116     | 190     | 0.2799        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 8.9971     | 194     | -             | 0.2053                  | 0.2215                 | 0.1929                 | 0.2191                 | 0.2106                | 0.2170                 |
+| 9.2754     | 200     | 0.312         | -                       | -                      | -                      | -                      | -                     | -                      |
+| 9.7391     | 210     | 0.2684        | 0.1876                  | 0.2004                 | 0.1893                 | 0.2122                 | 0.1971                | 0.1934                 |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.1.1
+- Transformers: 4.44.2
+- PyTorch: 2.4.1+cu121
+- Accelerate: 0.35.0.dev0
+- Datasets: 3.0.1
+- Tokenizers: 0.19.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MatryoshkaLoss
+```bibtex
+@misc{kusupati2024matryoshka,
+    title={Matryoshka Representation Learning},
+    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
+    year={2024},
+    eprint={2205.13147},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## 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"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "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,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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+size 17083053

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
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+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "model_max_length": 8192,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}