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 +1,878 @@
+---
+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:2372
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: Heu de veure si és necessari un estudi d'aïllament acústic i quin
+    nivell d'aïllament acústic precisa l'activitat.
+  sentences:
+  - Quin és el paper de les persones que resideixen amb el titular del dret d'habitatge
+    en la política d'habitatge?
+  - Quin és el límit de superfície per a les carpes informatives?
+  - Quin és l'objectiu de l'estudi d'aïllament acústic?
+- source_sentence: 'Si us voleu matricular al proper curs 2022-2023 d''arts plàstiques
+    ho podeu fer a partir del 1 de juliol a les 16h, seleccionant una d''aquestes
+    opcions:'
+  sentences:
+  - Quin és el període de matrícula per al curs 2022-2023 d'arts plàstiques?
+  - Quan no cal presentar al·legacions en un expedient de baixa d'ofici?
+  - Quin és l'objectiu de les al·legacions respecte a un expedient sancionador de
+    l'Ordenança Municipal de Civisme i Convivència Ciutadana?
+- source_sentence: Annexes Econòmics (Cooperació)
+  sentences:
+  - Qui és el responsable de l'elaboració de l'informe d'adequació de l'habitatge?
+  - Què han de fer les persones interessades durant el tràmit d'audiència en el procés
+    d'inclusió al registre municipal d'immobles desocupats?
+  - Quin és l'àmbit de la cooperació econòmica?
+- source_sentence: En virtut del conveni de col.laboració amb l'Atrium de Viladecans,
+    tots els ciutadans que acreditin la seva residència a Viladecans es podran beneficiar
+    d'un 20% de descompte en la programació de teatre, música i dansa, objecte del
+    conveni.
+  sentences:
+  - Quin és el resultat de consultar un expedient d'activitats?
+  - Quin és el format de resposta d'aquesta sol·licitud?
+  - Quin és el descompte que s'aplica en la programació de teatre, música i dansa
+    per als ciutadans de Viladecans?
+- source_sentence: Descripció. Retorna en format JSON adequat
+  sentences:
+  - Quin és el contingut de l'annex específic?
+  - Quin tipus d'ocupació es refereix a la renúncia de la llicència?
+  - Què passa amb l'habitatge?
+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: 0.33220910623946037
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.5902192242833052
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.6998313659359191
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8094435075885329
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.33220910623946037
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.1967397414277684
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.1399662731871838
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08094435075885327
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.33220910623946037
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.5902192242833052
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.6998313659359191
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8094435075885329
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.5625986746470664
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.4843170320404718
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.49243646079034575
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 768
+      type: dim_768
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.3406408094435076
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.5767284991568297
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.6981450252951096
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8161888701517707
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.3406408094435076
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.19224283305227655
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.1396290050590219
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08161888701517706
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.3406408094435076
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.5767284991568297
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.6981450252951096
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8161888701517707
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.5661348054508011
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.4872065633448428
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.49520736709122076
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 512
+      type: dim_512
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.3305227655986509
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.5801011804384486
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.6947723440134908
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8161888701517707
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.3305227655986509
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.19336706014614952
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.13895446880269813
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08161888701517707
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.3305227655986509
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.5801011804384486
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.6947723440134908
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8161888701517707
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.5629643418278626
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.4829913809256133
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.49079988310494693
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.3288364249578415
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.5885328836424958
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.7015177065767285
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8094435075885329
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.3288364249578415
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.1961776278808319
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.14030354131534567
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08094435075885327
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.3288364249578415
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.5885328836424958
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.7015177065767285
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8094435075885329
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.5625842077927447
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.48416981182579805
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.49201787335851555
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.3473861720067454
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.581787521079258
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.6998313659359191
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.806070826306914
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.3473861720067454
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.19392917369308602
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.1399662731871838
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.0806070826306914
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.3473861720067454
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.581787521079258
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.6998313659359191
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.806070826306914
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.565365572327355
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.4893626703070211
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.49726527073459287
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.2917369308600337
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.5682967959527825
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.6644182124789207
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.7875210792580101
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.2917369308600337
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.18943226531759413
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.13288364249578413
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.07875210792580102
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.2917369308600337
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.5682967959527825
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.6644182124789207
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.7875210792580101
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.5320349463938843
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.45117106988945077
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.45948574441166834
+      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-SB-001-5ep")
+# Run inference
+sentences = [
+    'Descripció. Retorna en format JSON adequat',
+    "Quin és el contingut de l'annex específic?",
+    "Què passa amb l'habitatge?",
+]
+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   | 0.3322     |
+| cosine_accuracy@3   | 0.5902     |
+| cosine_accuracy@5   | 0.6998     |
+| cosine_accuracy@10  | 0.8094     |
+| cosine_precision@1  | 0.3322     |
+| cosine_precision@3  | 0.1967     |
+| cosine_precision@5  | 0.14       |
+| cosine_precision@10 | 0.0809     |
+| cosine_recall@1     | 0.3322     |
+| cosine_recall@3     | 0.5902     |
+| cosine_recall@5     | 0.6998     |
+| cosine_recall@10    | 0.8094     |
+| cosine_ndcg@10      | 0.5626     |
+| cosine_mrr@10       | 0.4843     |
+| **cosine_map@100**  | **0.4924** |
+
+#### 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   | 0.3406     |
+| cosine_accuracy@3   | 0.5767     |
+| cosine_accuracy@5   | 0.6981     |
+| cosine_accuracy@10  | 0.8162     |
+| cosine_precision@1  | 0.3406     |
+| cosine_precision@3  | 0.1922     |
+| cosine_precision@5  | 0.1396     |
+| cosine_precision@10 | 0.0816     |
+| cosine_recall@1     | 0.3406     |
+| cosine_recall@3     | 0.5767     |
+| cosine_recall@5     | 0.6981     |
+| cosine_recall@10    | 0.8162     |
+| cosine_ndcg@10      | 0.5661     |
+| cosine_mrr@10       | 0.4872     |
+| **cosine_map@100**  | **0.4952** |
+
+#### 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   | 0.3305     |
+| cosine_accuracy@3   | 0.5801     |
+| cosine_accuracy@5   | 0.6948     |
+| cosine_accuracy@10  | 0.8162     |
+| cosine_precision@1  | 0.3305     |
+| cosine_precision@3  | 0.1934     |
+| cosine_precision@5  | 0.139      |
+| cosine_precision@10 | 0.0816     |
+| cosine_recall@1     | 0.3305     |
+| cosine_recall@3     | 0.5801     |
+| cosine_recall@5     | 0.6948     |
+| cosine_recall@10    | 0.8162     |
+| cosine_ndcg@10      | 0.563      |
+| cosine_mrr@10       | 0.483      |
+| **cosine_map@100**  | **0.4908** |
+
+#### 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   | 0.3288    |
+| cosine_accuracy@3   | 0.5885    |
+| cosine_accuracy@5   | 0.7015    |
+| cosine_accuracy@10  | 0.8094    |
+| cosine_precision@1  | 0.3288    |
+| cosine_precision@3  | 0.1962    |
+| cosine_precision@5  | 0.1403    |
+| cosine_precision@10 | 0.0809    |
+| cosine_recall@1     | 0.3288    |
+| cosine_recall@3     | 0.5885    |
+| cosine_recall@5     | 0.7015    |
+| cosine_recall@10    | 0.8094    |
+| cosine_ndcg@10      | 0.5626    |
+| cosine_mrr@10       | 0.4842    |
+| **cosine_map@100**  | **0.492** |
+
+#### 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   | 0.3474     |
+| cosine_accuracy@3   | 0.5818     |
+| cosine_accuracy@5   | 0.6998     |
+| cosine_accuracy@10  | 0.8061     |
+| cosine_precision@1  | 0.3474     |
+| cosine_precision@3  | 0.1939     |
+| cosine_precision@5  | 0.14       |
+| cosine_precision@10 | 0.0806     |
+| cosine_recall@1     | 0.3474     |
+| cosine_recall@3     | 0.5818     |
+| cosine_recall@5     | 0.6998     |
+| cosine_recall@10    | 0.8061     |
+| cosine_ndcg@10      | 0.5654     |
+| cosine_mrr@10       | 0.4894     |
+| **cosine_map@100**  | **0.4973** |
+
+#### 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   | 0.2917     |
+| cosine_accuracy@3   | 0.5683     |
+| cosine_accuracy@5   | 0.6644     |
+| cosine_accuracy@10  | 0.7875     |
+| cosine_precision@1  | 0.2917     |
+| cosine_precision@3  | 0.1894     |
+| cosine_precision@5  | 0.1329     |
+| cosine_precision@10 | 0.0788     |
+| cosine_recall@1     | 0.2917     |
+| cosine_recall@3     | 0.5683     |
+| cosine_recall@5     | 0.6644     |
+| cosine_recall@10    | 0.7875     |
+| cosine_ndcg@10      | 0.532      |
+| cosine_mrr@10       | 0.4512     |
+| **cosine_map@100**  | **0.4595** |
+
+<!--
+## 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: 2,372 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: 35.12 tokens</li><li>max: 166 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 19.49 tokens</li><li>max: 47 tokens</li></ul> |
+* Samples:
+  | positive                                                                                                                                                                                                                                                                | anchor                                                                                          |
+  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|
+  | <code>Comunicar la variació d'alguna de les següents dades del Padró Municipal d'Habitants: Nom, Cognoms, Data de naixement, DNI, Passaport, Número de permís de residència (NIE), Sexe, Municipi i/o província de naixement, Nacionalitat, Titulació acadèmica.</code> | <code>Quin és l'objectiu del canvi de dades personals en el Padró Municipal d'Habitants?</code> |
+  | <code>EN QUÈ CONSISTEIX: Tramitar la sol·licitud de matrimoni civil a l'Ajuntament.</code>                                                                                                                                                                              | <code>Què és el matrimoni civil a l'Ajuntament de Sant Boi de Llobregat?</code>                 |
+  | <code>En domiciliar el pagament de tributs municipals en entitats bancàries.</code>                                                                                                                                                                                     | <code>Quin és el benefici de domiciliar el pagament de tributs?</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_768_cosine_map@100 | dim_512_cosine_map@100 | dim_256_cosine_map@100 | dim_128_cosine_map@100 | dim_64_cosine_map@100 |
+|:----------:|:------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
+| 0.9664     | 9      | -             | 0.4730                  | 0.4766                 | 0.4640                 | 0.4612                 | 0.4456                 | 0.4083                |
+| 1.0738     | 10     | 2.6023        | -                       | -                      | -                      | -                      | -                      | -                     |
+| 1.9329     | 18     | -             | 0.4951                  | 0.4966                 | 0.4977                 | 0.4773                 | 0.4849                 | 0.4501                |
+| 2.1477     | 20     | 0.974         | -                       | -                      | -                      | -                      | -                      | -                     |
+| 2.8993     | 27     | -             | 0.4891                  | 0.4973                 | 0.4941                 | 0.4867                 | 0.4925                 | 0.4684                |
+| 3.2215     | 30     | 0.408         | -                       | -                      | -                      | -                      | -                      | -                     |
+| **3.9732** | **37** | **-**         | **0.4944**              | **0.4998**             | **0.4931**             | **0.4991**             | **0.4974**             | **0.4616**            |
+| 4.2953     | 40     | 0.2718        | -                       | -                      | -                      | -                      | -                      | -                     |
+| 4.8322     | 45     | -             | 0.4924                  | 0.4952                 | 0.4908                 | 0.4920                 | 0.4973                 | 0.4595                |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.2.0
+- Transformers: 4.44.2
+- PyTorch: 2.4.1+cu121
+- Accelerate: 1.1.0.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

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+{
+  "__version__": {
+    "sentence_transformers": "3.2.0",
+    "transformers": "4.44.2",
+    "pytorch": "2.4.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

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+size 2271064456

modules.json

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+[
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+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
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+  {
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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

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+    "rstrip": false,
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+}