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 +1,858 @@
+---
+base_model: BAAI/bge-m3
+datasets: []
+language:
+- ca
+library_name: sentence-transformers
+license: apache-2.0
+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:3755
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: En el cas que la persona beneficiària mantingui les condicions
+    d’elegibilitat es podrà concedir la pròrroga de la prestació sempre que la persona
+    interessada ho sol·liciti i ho permetin les dotacions pressupostàries de cada
+    exercici.
+  sentences:
+  - Quin és el benefici de l'ajut a la consolidació d'empreses?
+  - Quin és el requisit per a la persona beneficiària?
+  - Quin és el benefici del Registre municipal d'entitats per a l'Ajuntament?
+- source_sentence: Aquest tràmit permet la presentació de les sol·licituds per a l’atorgament
+    de llicències d’aprofitament especial sense transformació del domini públic marítim
+    terrestre consistent en la instal·lació i explotació d'escola per oferir activitats
+    nàutiques, amb zona d’avarada, durant la temporada.
+  sentences:
+  - Quin és el propòsit de la llicència d'aprofitament especial sense transformació
+    del domini públic marítim terrestre?
+  - Quin és el termini per a presentar les sol·licituds de subvencions per a projectes
+    i activitats a entitats de l'àmbit de drets civils?
+  - Quin és el lloc on es realitzen les activitats amb aquest permís?
+- source_sentence: en cas de compliment dels requisits establerts (persones residents,
+    titulars de plaça d'aparcament, autotaxis, establiments hotelers)
+  sentences:
+  - Quin és el paper de l'administració en la justificació del projecte/activitat
+    subvencionada?
+  - Quin és el benefici de ser un autotaxi?
+  - Quin és el benefici per als establiments de la instal·lació de terrasses o vetlladors?
+- source_sentence: La convocatòria és el document que estableix les condicions i els
+    requisits per a poder sol·licitar les subvencions pel suport educatiu a les escoles
+    públiques de Sitges.
+  sentences:
+  - Quin és el paper de la convocatòria en les subvencions pel suport educatiu a les
+    escoles públiques de Sitges?
+  - Quin és el benefici de la consulta prèvia de classificació d'activitat per a l'Ajuntament
+    de Sitges?
+  - Quin és el tipus d'ocupació de la via pública que es pot realitzar amb aquest
+    permís?
+- source_sentence: Cal revisar la informació i els terminis de la convocatòria específica
+    de cada procés que trobareu a la Seu electrònica de l'Ajuntament de Sitges.
+  sentences:
+  - Quin és el document que es necessita per acreditar l'any de construcció i l'adequació
+    a la legalitat urbanística d'un immoble?
+  - Quin és el paper de l'Ajuntament en la gestió de les activitats per temporades?
+  - On es pot trobar la informació sobre els terminis de presentació d'al·legacions
+    en un procés de selecció de personal de l'Ajuntament de Sitges?
+model-index:
+- name: BGE SITGES  CAT
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 1024
+      type: dim_1024
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.13875598086124402
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.22248803827751196
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.30861244019138756
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.5
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.13875598086124402
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.07416267942583732
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.06172248803827752
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.049999999999999996
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.13875598086124402
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.22248803827751196
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.30861244019138756
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.5
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.28246378665685234
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.21777644869750143
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.24297774164515282
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 768
+      type: dim_768
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.13157894736842105
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.22248803827751196
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.3157894736842105
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.4904306220095694
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.13157894736842105
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.07416267942583732
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.06315789473684211
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.04904306220095694
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.13157894736842105
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.22248803827751196
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.3157894736842105
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.4904306220095694
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.27585932698577753
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.21171489329384077
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.23780085464747025
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 512
+      type: dim_512
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.13875598086124402
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.21770334928229665
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.3062200956937799
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.48564593301435405
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.13875598086124402
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.07256778309409888
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.06124401913875598
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.0485645933014354
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.13875598086124402
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.21770334928229665
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.3062200956937799
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.48564593301435405
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.276564299219231
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.21426198070934924
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.24076362333582052
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.12440191387559808
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.21770334928229665
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.3133971291866029
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.4688995215311005
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.12440191387559808
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.07256778309409888
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.06267942583732058
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.04688995215311005
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.12440191387559808
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.21770334928229665
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.3133971291866029
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.4688995215311005
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.2671493494247117
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.20640996430470124
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.23431223249888664
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.12200956937799043
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.21291866028708134
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.3014354066985646
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.49282296650717705
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.12200956937799043
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.07097288676236044
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.06028708133971292
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.049282296650717705
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.12200956937799043
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.21291866028708134
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.3014354066985646
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.49282296650717705
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.27152939051256636
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.20549764562922473
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.23082152106975815
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.11961722488038277
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.19856459330143542
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.2822966507177033
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.4688995215311005
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.11961722488038277
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.06618819776714513
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.056459330143540674
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.046889952153110044
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.11961722488038277
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.19856459330143542
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.2822966507177033
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.4688995215311005
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.2582882544405147
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.19569188121819714
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.22122525098210105
+      name: Cosine Map@100
+---
+
+# BGE SITGES  CAT
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3). 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:** Unknown -->
+- **Language:** ca
+- **License:** apache-2.0
+
+### 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/SITGES-BAAI2")
+# Run inference
+sentences = [
+    "Cal revisar la informació i els terminis de la convocatòria específica de cada procés que trobareu a la Seu electrònica de l'Ajuntament de Sitges.",
+    "On es pot trobar la informació sobre els terminis de presentació d'al·legacions en un procés de selecció de personal de l'Ajuntament de Sitges?",
+    "Quin és el document que es necessita per acreditar l'any de construcció i l'adequació a la legalitat urbanística d'un immoble?",
+]
+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   | 0.1388    |
+| cosine_accuracy@3   | 0.2225    |
+| cosine_accuracy@5   | 0.3086    |
+| cosine_accuracy@10  | 0.5       |
+| cosine_precision@1  | 0.1388    |
+| cosine_precision@3  | 0.0742    |
+| cosine_precision@5  | 0.0617    |
+| cosine_precision@10 | 0.05      |
+| cosine_recall@1     | 0.1388    |
+| cosine_recall@3     | 0.2225    |
+| cosine_recall@5     | 0.3086    |
+| cosine_recall@10    | 0.5       |
+| cosine_ndcg@10      | 0.2825    |
+| cosine_mrr@10       | 0.2178    |
+| **cosine_map@100**  | **0.243** |
+
+#### 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   | 0.1316     |
+| cosine_accuracy@3   | 0.2225     |
+| cosine_accuracy@5   | 0.3158     |
+| cosine_accuracy@10  | 0.4904     |
+| cosine_precision@1  | 0.1316     |
+| cosine_precision@3  | 0.0742     |
+| cosine_precision@5  | 0.0632     |
+| cosine_precision@10 | 0.049      |
+| cosine_recall@1     | 0.1316     |
+| cosine_recall@3     | 0.2225     |
+| cosine_recall@5     | 0.3158     |
+| cosine_recall@10    | 0.4904     |
+| cosine_ndcg@10      | 0.2759     |
+| cosine_mrr@10       | 0.2117     |
+| **cosine_map@100**  | **0.2378** |
+
+#### 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   | 0.1388     |
+| cosine_accuracy@3   | 0.2177     |
+| cosine_accuracy@5   | 0.3062     |
+| cosine_accuracy@10  | 0.4856     |
+| cosine_precision@1  | 0.1388     |
+| cosine_precision@3  | 0.0726     |
+| cosine_precision@5  | 0.0612     |
+| cosine_precision@10 | 0.0486     |
+| cosine_recall@1     | 0.1388     |
+| cosine_recall@3     | 0.2177     |
+| cosine_recall@5     | 0.3062     |
+| cosine_recall@10    | 0.4856     |
+| cosine_ndcg@10      | 0.2766     |
+| cosine_mrr@10       | 0.2143     |
+| **cosine_map@100**  | **0.2408** |
+
+#### 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   | 0.1244     |
+| cosine_accuracy@3   | 0.2177     |
+| cosine_accuracy@5   | 0.3134     |
+| cosine_accuracy@10  | 0.4689     |
+| cosine_precision@1  | 0.1244     |
+| cosine_precision@3  | 0.0726     |
+| cosine_precision@5  | 0.0627     |
+| cosine_precision@10 | 0.0469     |
+| cosine_recall@1     | 0.1244     |
+| cosine_recall@3     | 0.2177     |
+| cosine_recall@5     | 0.3134     |
+| cosine_recall@10    | 0.4689     |
+| cosine_ndcg@10      | 0.2671     |
+| cosine_mrr@10       | 0.2064     |
+| **cosine_map@100**  | **0.2343** |
+
+#### 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   | 0.122      |
+| cosine_accuracy@3   | 0.2129     |
+| cosine_accuracy@5   | 0.3014     |
+| cosine_accuracy@10  | 0.4928     |
+| cosine_precision@1  | 0.122      |
+| cosine_precision@3  | 0.071      |
+| cosine_precision@5  | 0.0603     |
+| cosine_precision@10 | 0.0493     |
+| cosine_recall@1     | 0.122      |
+| cosine_recall@3     | 0.2129     |
+| cosine_recall@5     | 0.3014     |
+| cosine_recall@10    | 0.4928     |
+| cosine_ndcg@10      | 0.2715     |
+| cosine_mrr@10       | 0.2055     |
+| **cosine_map@100**  | **0.2308** |
+
+#### 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   | 0.1196     |
+| cosine_accuracy@3   | 0.1986     |
+| cosine_accuracy@5   | 0.2823     |
+| cosine_accuracy@10  | 0.4689     |
+| cosine_precision@1  | 0.1196     |
+| cosine_precision@3  | 0.0662     |
+| cosine_precision@5  | 0.0565     |
+| cosine_precision@10 | 0.0469     |
+| cosine_recall@1     | 0.1196     |
+| cosine_recall@3     | 0.1986     |
+| cosine_recall@5     | 0.2823     |
+| cosine_recall@10    | 0.4689     |
+| cosine_ndcg@10      | 0.2583     |
+| cosine_mrr@10       | 0.1957     |
+| **cosine_map@100**  | **0.2212** |
+
+<!--
+## 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 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`: 6
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.1
+- `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
+- `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`: 6
+- `max_steps`: -1
+- `lr_scheduler_type`: cosine
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `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
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch      | Step   | Training Loss | 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 |
+|:----------:|:------:|:-------------:|:---------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
+| 0.3404     | 5      | 3.3256        | -         | -                       | -                      | -                      | -                      | -                     | -                      |
+| 0.6809     | 10     | 2.2115        | -         | -                       | -                      | -                      | -                      | -                     | -                      |
+| 0.9532     | 14     | -             | 1.2963    | 0.2260                  | 0.2148                 | 0.2144                 | 0.2258                 | 0.2069                | 0.2252                 |
+| 1.0213     | 15     | 1.7921        | -         | -                       | -                      | -                      | -                      | -                     | -                      |
+| 1.3617     | 20     | 1.2295        | -         | -                       | -                      | -                      | -                      | -                     | -                      |
+| 1.7021     | 25     | 0.9048        | -         | -                       | -                      | -                      | -                      | -                     | -                      |
+| 1.9745     | 29     | -             | 0.8667    | 0.2311                  | 0.2267                 | 0.2292                 | 0.2279                 | 0.2121                | 0.2278                 |
+| 2.0426     | 30     | 0.7256        | -         | -                       | -                      | -                      | -                      | -                     | -                      |
+| 2.3830     | 35     | 0.5252        | -         | -                       | -                      | -                      | -                      | -                     | -                      |
+| 2.7234     | 40     | 0.4648        | -         | -                       | -                      | -                      | -                      | -                     | -                      |
+| 2.9957     | 44     | -             | 0.6920    | 0.2311                  | 0.2243                 | 0.2332                 | 0.2319                 | 0.2211                | 0.2354                 |
+| 3.0638     | 45     | 0.3518        | -         | -                       | -                      | -                      | -                      | -                     | -                      |
+| 3.4043     | 50     | 0.321         | -         | -                       | -                      | -                      | -                      | -                     | -                      |
+| 3.7447     | 55     | 0.2923        | -         | -                       | -                      | -                      | -                      | -                     | -                      |
+| 3.9489     | 58     | -             | 0.6514    | 0.2343                  | 0.2210                 | 0.2293                 | 0.2338                 | 0.2242                | 0.2331                 |
+| 4.0851     | 60     | 0.2522        | -         | -                       | -                      | -                      | -                      | -                     | -                      |
+| 4.4255     | 65     | 0.2445        | -         | -                       | -                      | -                      | -                      | -                     | -                      |
+| 4.7660     | 70     | 0.2358        | -         | -                       | -                      | -                      | -                      | -                     | -                      |
+| 4.9702     | 73     | -             | 0.6481    | 0.2348                  | 0.2239                 | 0.2252                 | 0.2332                 | 0.2167                | 0.2298                 |
+| 5.1064     | 75     | 0.2301        | -         | -                       | -                      | -                      | -                      | -                     | -                      |
+| 5.4468     | 80     | 0.2262        | -         | -                       | -                      | -                      | -                      | -                     | -                      |
+| **5.7191** | **84** | **-**         | **0.646** | **0.243**               | **0.2308**             | **0.2343**             | **0.2408**             | **0.2212**            | **0.2378**             |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.0.1
+- Transformers: 4.42.3
+- PyTorch: 2.3.1+cu121
+- Accelerate: 0.32.1
+- Datasets: 2.20.0
+- 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

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+{
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+  "layer_norm_eps": 1e-05,
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+  "model_type": "xlm-roberta",
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+  "num_hidden_layers": 24,
+  "output_past": true,
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+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.42.3",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

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+  "__version__": {
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+    "transformers": "4.42.3",
+    "pytorch": "2.3.1+cu121"
+  },
+  "prompts": {},
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+  "similarity_fn_name": null
+}

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modules.json

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+    "name": "0",
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+  }
+]

sentence_bert_config.json

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

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+}