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:8769
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: Aquelles persones que fan un ús regular i continuat de la deixalleria
+    municipal poden gaudir d’una bonificació del 20% sobre la quota de les taxes per
+    recollida, tractament i eliminació d'escombraries i altres residus urbans domiciliaris.
+  sentences:
+  - Quin és el contingut dels documents dirigits a l'Ajuntament de Sitges?
+  - Quin és el benefici de la deixalleria municipal?
+  - Quin és el mètode de pagament dels ajuts atorgats en cas de normalitat?
+- source_sentence: Les subvencions per al desenvolupament i/o consolidació de sectors
+    econòmics del municipi tenen com a objectiu generar un benefici ambiental per
+    al municipi, a través de la promoció de pràctiques sostenibles.
+  sentences:
+  - Quin és el requisit per a la llicència per a la modificació d'un règim de propietat
+    horitzontal?
+  - Quin és el benefici ambiental esperat de les subvencions per al desenvolupament
+    i/o consolidació de sectors econòmics del municipi?
+  - Quin és el propòsit de la liquidació de l'import corresponent a l'exercici?
+- source_sentence: Aquelles persones que s'hagin inscrit a les estades esportives
+    organitzades per l'Ajuntament de Sitges i que formin part d'una unitat familiar
+    amb uns ingressos bruts mensuals, que una vegada dividits pel nombre de membres,
+    siguin inferiors entre una i dues terceres parts de l'IPREM, poden sol·licitar
+    una reducció de la quota d'aquestes activitats o l'aplicació de la corresponent
+    tarifa bonificada establerta en les ordenances dels preus públics.
+  sentences:
+  - Quin és el benefici de les subvencions per a les entitats culturals?
+  - Quin és el paper de l'IPREM en la sol·licitud de reducció de la quota d'una estada
+    esportiva?
+  - Quin és el paper de l'Ajuntament en la resolució d'una situació sanitària no adequada
+    en un domini particular?
+- source_sentence: La inscripció al cens municipal facilita la recuperació d’aquests
+    animals en cas de pèrdua alhora que permet a l’Ajuntament disposar de les dades
+    necessàries en cas que s’hagin de realitzar campanyes sanitàries.
+  sentences:
+  - Quin és el tipus de serveis auxiliars que es consideren despeses elegibles?
+  - Quin és el benefici d'estacionar a les zones verdes per als residents?
+  - Quin és el motiu pel qual es crea el cens municipal d’animals de companyia?
+- source_sentence: A la nostra vila hi ha veïns i veïnes que els agradaria tornar
+    a fer de pagès o provar-ho per primera vegada.
+  sentences:
+  - Quin és l'objectiu principal de l'activitat del Viver dels Avis de Sitges?
+  - Quin és el propòsit del carnet de conductor de taxi?
+  - Quin és el paper de les persones en relació amb les indemnitzacions?
+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: 0.11054852320675106
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.2270042194092827
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.30548523206751055
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.4531645569620253
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.11054852320675106
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.07566807313642755
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.06109704641350212
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.04531645569620253
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.11054852320675106
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.2270042194092827
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.30548523206751055
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.4531645569620253
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.25622764604771076
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.1965350612818966
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.21859411055862238
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 768
+      type: dim_768
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.11561181434599156
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.2320675105485232
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.31139240506329113
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.44556962025316454
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.11561181434599156
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.07735583684950773
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.06227848101265824
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.044556962025316456
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.11561181434599156
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.2320675105485232
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.31139240506329113
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.44556962025316454
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.2579660315889156
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.20086732301922164
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.22344331787470567
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 512
+      type: dim_512
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.10379746835443038
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.2210970464135021
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.2970464135021097
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.43966244725738396
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.10379746835443038
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.07369901547116735
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.05940928270042194
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.043966244725738395
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.10379746835443038
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.2210970464135021
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.2970464135021097
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.43966244725738396
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.2473619714740055
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.18892840399169497
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.21182552044674802
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.10042194092827005
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.21518987341772153
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.2978902953586498
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.4438818565400844
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.10042194092827005
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.07172995780590716
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.05957805907172995
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.04438818565400844
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.10042194092827005
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.21518987341772153
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.2978902953586498
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.4438818565400844
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.2479637375723138
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.18831156653941447
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.21130848497160895
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.10886075949367088
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.22616033755274262
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.3029535864978903
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.4413502109704641
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.10886075949367088
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.07538677918424753
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.060590717299578066
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.04413502109704641
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.10886075949367088
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.22616033755274262
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.3029535864978903
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.4413502109704641
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.25366131313332974
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.19639441430580665
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.2187767008895725
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.09367088607594937
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.2
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.2742616033755274
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.4177215189873418
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.09367088607594937
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.06666666666666667
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.05485232067510549
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.04177215189873418
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.09367088607594937
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.2
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.2742616033755274
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.4177215189873418
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.23046340016141767
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.1738279418659165
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.19782551958501599
+      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-003-5ep")
+# Run inference
+sentences = [
+    'A la nostra vila hi ha veïns i veïnes que els agradaria tornar a fer de pagès o provar-ho per primera vegada.',
+    "Quin és l'objectiu principal de l'activitat del Viver dels Avis de Sitges?",
+    'Quin és el paper de les persones en relació amb les indemnitzacions?',
+]
+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   | 0.1105     |
+| cosine_accuracy@3   | 0.227      |
+| cosine_accuracy@5   | 0.3055     |
+| cosine_accuracy@10  | 0.4532     |
+| cosine_precision@1  | 0.1105     |
+| cosine_precision@3  | 0.0757     |
+| cosine_precision@5  | 0.0611     |
+| cosine_precision@10 | 0.0453     |
+| cosine_recall@1     | 0.1105     |
+| cosine_recall@3     | 0.227      |
+| cosine_recall@5     | 0.3055     |
+| cosine_recall@10    | 0.4532     |
+| cosine_ndcg@10      | 0.2562     |
+| cosine_mrr@10       | 0.1965     |
+| **cosine_map@100**  | **0.2186** |
+
+#### 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   | 0.1156     |
+| cosine_accuracy@3   | 0.2321     |
+| cosine_accuracy@5   | 0.3114     |
+| cosine_accuracy@10  | 0.4456     |
+| cosine_precision@1  | 0.1156     |
+| cosine_precision@3  | 0.0774     |
+| cosine_precision@5  | 0.0623     |
+| cosine_precision@10 | 0.0446     |
+| cosine_recall@1     | 0.1156     |
+| cosine_recall@3     | 0.2321     |
+| cosine_recall@5     | 0.3114     |
+| cosine_recall@10    | 0.4456     |
+| cosine_ndcg@10      | 0.258      |
+| cosine_mrr@10       | 0.2009     |
+| **cosine_map@100**  | **0.2234** |
+
+#### 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   | 0.1038     |
+| cosine_accuracy@3   | 0.2211     |
+| cosine_accuracy@5   | 0.297      |
+| cosine_accuracy@10  | 0.4397     |
+| cosine_precision@1  | 0.1038     |
+| cosine_precision@3  | 0.0737     |
+| cosine_precision@5  | 0.0594     |
+| cosine_precision@10 | 0.044      |
+| cosine_recall@1     | 0.1038     |
+| cosine_recall@3     | 0.2211     |
+| cosine_recall@5     | 0.297      |
+| cosine_recall@10    | 0.4397     |
+| cosine_ndcg@10      | 0.2474     |
+| cosine_mrr@10       | 0.1889     |
+| **cosine_map@100**  | **0.2118** |
+
+#### 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   | 0.1004     |
+| cosine_accuracy@3   | 0.2152     |
+| cosine_accuracy@5   | 0.2979     |
+| cosine_accuracy@10  | 0.4439     |
+| cosine_precision@1  | 0.1004     |
+| cosine_precision@3  | 0.0717     |
+| cosine_precision@5  | 0.0596     |
+| cosine_precision@10 | 0.0444     |
+| cosine_recall@1     | 0.1004     |
+| cosine_recall@3     | 0.2152     |
+| cosine_recall@5     | 0.2979     |
+| cosine_recall@10    | 0.4439     |
+| cosine_ndcg@10      | 0.248      |
+| cosine_mrr@10       | 0.1883     |
+| **cosine_map@100**  | **0.2113** |
+
+#### 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   | 0.1089     |
+| cosine_accuracy@3   | 0.2262     |
+| cosine_accuracy@5   | 0.303      |
+| cosine_accuracy@10  | 0.4414     |
+| cosine_precision@1  | 0.1089     |
+| cosine_precision@3  | 0.0754     |
+| cosine_precision@5  | 0.0606     |
+| cosine_precision@10 | 0.0441     |
+| cosine_recall@1     | 0.1089     |
+| cosine_recall@3     | 0.2262     |
+| cosine_recall@5     | 0.303      |
+| cosine_recall@10    | 0.4414     |
+| cosine_ndcg@10      | 0.2537     |
+| cosine_mrr@10       | 0.1964     |
+| **cosine_map@100**  | **0.2188** |
+
+#### 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   | 0.0937     |
+| cosine_accuracy@3   | 0.2        |
+| cosine_accuracy@5   | 0.2743     |
+| cosine_accuracy@10  | 0.4177     |
+| cosine_precision@1  | 0.0937     |
+| cosine_precision@3  | 0.0667     |
+| cosine_precision@5  | 0.0549     |
+| cosine_precision@10 | 0.0418     |
+| cosine_recall@1     | 0.0937     |
+| cosine_recall@3     | 0.2        |
+| cosine_recall@5     | 0.2743     |
+| cosine_recall@10    | 0.4177     |
+| cosine_ndcg@10      | 0.2305     |
+| cosine_mrr@10       | 0.1738     |
+| **cosine_map@100**  | **0.1978** |
+
+<!--
+## 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: 8,769 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: 49.22 tokens</li><li>max: 178 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 20.94 tokens</li><li>max: 48 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>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 al llarg de l'exercici per la qual es sol·licita la subvenció, i reuneixin les condicions assenyalades a les bases.</code> | <code>Quin és el període d'execució dels projectes i activitats esportives?</code> |
+  | <code>Certificat on s'indica el nombre d'habitatges que configuren el padró de l'Impost sobre Béns Immobles del municipi o bé d'una part d'aquest.</code>                                                                                                                                                                                                                                            | <code>Quin és el contingut del certificat del nombre d'habitatges?</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 |
+|:----------:|:-------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
+| 0.2914     | 10      | 3.6318        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 0.5829     | 20      | 2.329         | -                       | -                      | -                      | -                      | -                     | -                      |
+| 0.8743     | 30      | 1.5614        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 0.9909     | 34      | -             | 0.2055                  | 0.1998                 | 0.2020                 | 0.2001                 | 0.1903                | 0.2019                 |
+| 1.1658     | 40      | 1.2383        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 1.4572     | 50      | 0.9323        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 1.7486     | 60      | 0.6616        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 1.9818     | 68      | -             | 0.2244                  | 0.2063                 | 0.2223                 | 0.2166                 | 0.2011                | 0.2235                 |
+| 2.0401     | 70      | 0.5545        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 2.3315     | 80      | 0.5043        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 2.6230     | 90      | 0.3542        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 2.9144     | 100     | 0.3095        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 2.9727     | 102     | -             | 0.2224                  | 0.2046                 | 0.2170                 | 0.2100                 | 0.1986                | 0.2144                 |
+| 3.2058     | 110     | 0.2863        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 3.4973     | 120     | 0.2329        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 3.7887     | 130     | 0.2353        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 3.9927     | 137     | -             | 0.2197                  | 0.2112                 | 0.2098                 | 0.2154                 | 0.1949                | 0.2178                 |
+| 4.0801     | 140     | 0.1759        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 4.3716     | 150     | 0.2308        | -                       | -                      | -                      | -                      | -                     | -                      |
+| 4.6630     | 160     | 0.1656        | -                       | -                      | -                      | -                      | -                     | -                      |
+| **4.9545** | **170** | **0.1812**    | **0.2186**              | **0.2188**             | **0.2113**             | **0.2118**             | **0.1978**            | **0.2234**             |
+
+* 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"
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+  {
+    "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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+    "lstrip": false,
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+    "rstrip": false,
+    "single_word": false
+  },
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+    "lstrip": true,
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+    "rstrip": false,
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+    "lstrip": false,
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+size 17083053

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