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 +1,880 @@
+---
+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: 0.12679425837320574
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.21291866028708134
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.30861244019138756
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.49521531100478466
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.12679425837320574
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.07097288676236044
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.06172248803827751
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.049521531100478466
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.12679425837320574
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.21291866028708134
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.30861244019138756
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.49521531100478466
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.27514703200596163
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.20944786207944124
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.23684652150885108
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 768
+      type: dim_768
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.11961722488038277
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.20574162679425836
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.31100478468899523
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.49760765550239233
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.11961722488038277
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.06858054226475278
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.06220095693779904
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.04976076555023923
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.11961722488038277
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.20574162679425836
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.31100478468899523
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.49760765550239233
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.2725409285822112
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.2052479684058634
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.23218215402287107
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 512
+      type: dim_512
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.12440191387559808
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.215311004784689
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.33014354066985646
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.5047846889952153
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.12440191387559808
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.07177033492822966
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.0660287081339713
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.050478468899521525
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.12440191387559808
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.215311004784689
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.33014354066985646
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.5047846889952153
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.2802134368260993
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.21296422875370263
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.23912050845024263
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.11961722488038277
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.23205741626794257
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.32057416267942584
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.47607655502392343
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.11961722488038277
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.07735247208931419
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.06411483253588517
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.04760765550239234
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.11961722488038277
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.23205741626794257
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.32057416267942584
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.47607655502392343
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.2689946292721634
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.20637104123946248
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.23511603125214608
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.11961722488038277
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.21770334928229665
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.3253588516746411
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.5
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.11961722488038277
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.07256778309409888
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.06507177033492824
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.049999999999999996
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.11961722488038277
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.21770334928229665
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.3253588516746411
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.5
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.2754707963170229
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.20811498443077409
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.23411435647414974
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.1291866028708134
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.21291866028708134
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.32057416267942584
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.48086124401913877
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.1291866028708134
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.07097288676236044
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.06411483253588518
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.04808612440191388
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.1291866028708134
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.21291866028708134
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.32057416267942584
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.48086124401913877
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.2704775725936489
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.20746753246753263
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.23395020532132502
+      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-BAAI3")
+# 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   | 0.1268     |
+| cosine_accuracy@3   | 0.2129     |
+| cosine_accuracy@5   | 0.3086     |
+| cosine_accuracy@10  | 0.4952     |
+| cosine_precision@1  | 0.1268     |
+| cosine_precision@3  | 0.071      |
+| cosine_precision@5  | 0.0617     |
+| cosine_precision@10 | 0.0495     |
+| cosine_recall@1     | 0.1268     |
+| cosine_recall@3     | 0.2129     |
+| cosine_recall@5     | 0.3086     |
+| cosine_recall@10    | 0.4952     |
+| cosine_ndcg@10      | 0.2751     |
+| cosine_mrr@10       | 0.2094     |
+| **cosine_map@100**  | **0.2368** |
+
+#### 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   | 0.1196     |
+| cosine_accuracy@3   | 0.2057     |
+| cosine_accuracy@5   | 0.311      |
+| cosine_accuracy@10  | 0.4976     |
+| cosine_precision@1  | 0.1196     |
+| cosine_precision@3  | 0.0686     |
+| cosine_precision@5  | 0.0622     |
+| cosine_precision@10 | 0.0498     |
+| cosine_recall@1     | 0.1196     |
+| cosine_recall@3     | 0.2057     |
+| cosine_recall@5     | 0.311      |
+| cosine_recall@10    | 0.4976     |
+| cosine_ndcg@10      | 0.2725     |
+| cosine_mrr@10       | 0.2052     |
+| **cosine_map@100**  | **0.2322** |
+
+#### 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   | 0.1244     |
+| cosine_accuracy@3   | 0.2153     |
+| cosine_accuracy@5   | 0.3301     |
+| cosine_accuracy@10  | 0.5048     |
+| cosine_precision@1  | 0.1244     |
+| cosine_precision@3  | 0.0718     |
+| cosine_precision@5  | 0.066      |
+| cosine_precision@10 | 0.0505     |
+| cosine_recall@1     | 0.1244     |
+| cosine_recall@3     | 0.2153     |
+| cosine_recall@5     | 0.3301     |
+| cosine_recall@10    | 0.5048     |
+| cosine_ndcg@10      | 0.2802     |
+| cosine_mrr@10       | 0.213      |
+| **cosine_map@100**  | **0.2391** |
+
+#### 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   | 0.1196     |
+| cosine_accuracy@3   | 0.2321     |
+| cosine_accuracy@5   | 0.3206     |
+| cosine_accuracy@10  | 0.4761     |
+| cosine_precision@1  | 0.1196     |
+| cosine_precision@3  | 0.0774     |
+| cosine_precision@5  | 0.0641     |
+| cosine_precision@10 | 0.0476     |
+| cosine_recall@1     | 0.1196     |
+| cosine_recall@3     | 0.2321     |
+| cosine_recall@5     | 0.3206     |
+| cosine_recall@10    | 0.4761     |
+| cosine_ndcg@10      | 0.269      |
+| cosine_mrr@10       | 0.2064     |
+| **cosine_map@100**  | **0.2351** |
+
+#### 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   | 0.1196     |
+| cosine_accuracy@3   | 0.2177     |
+| cosine_accuracy@5   | 0.3254     |
+| cosine_accuracy@10  | 0.5        |
+| cosine_precision@1  | 0.1196     |
+| cosine_precision@3  | 0.0726     |
+| cosine_precision@5  | 0.0651     |
+| cosine_precision@10 | 0.05       |
+| cosine_recall@1     | 0.1196     |
+| cosine_recall@3     | 0.2177     |
+| cosine_recall@5     | 0.3254     |
+| cosine_recall@10    | 0.5        |
+| cosine_ndcg@10      | 0.2755     |
+| cosine_mrr@10       | 0.2081     |
+| **cosine_map@100**  | **0.2341** |
+
+#### 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   | 0.1292    |
+| cosine_accuracy@3   | 0.2129    |
+| cosine_accuracy@5   | 0.3206    |
+| cosine_accuracy@10  | 0.4809    |
+| cosine_precision@1  | 0.1292    |
+| cosine_precision@3  | 0.071     |
+| cosine_precision@5  | 0.0641    |
+| cosine_precision@10 | 0.0481    |
+| cosine_recall@1     | 0.1292    |
+| cosine_recall@3     | 0.2129    |
+| cosine_recall@5     | 0.3206    |
+| cosine_recall@10    | 0.4809    |
+| cosine_ndcg@10      | 0.2705    |
+| cosine_mrr@10       | 0.2075    |
+| **cosine_map@100**  | **0.234** |
+
+<!--
+## 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** | **-**         | **0.692**  | **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     | -             | 0.6460     | 0.2430                  | 0.2308                 | 0.2343                 | 0.2408                 | 0.2212                | 0.2378                 |
+| 0.3404     | 5      | 0.1585        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
+| 0.6809     | 10     | 0.1465        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
+| 0.9532     | 14     | -             | 0.6325     | 0.2407                  | 0.2255                 | 0.2328                 | 0.2333                 | 0.2266                | 0.2429                 |
+| 1.0213     | 15     | 0.1411        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
+| 1.3617     | 20     | 0.079         | -          | -                       | -                      | -                      | -                      | -                     | -                      |
+| 1.7021     | 25     | 0.1159        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
+| 1.9745     | 29     | -             | 0.6772     | 0.2361                  | 0.2287                 | 0.2252                 | 0.2325                 | 0.2228                | 0.2387                 |
+| 2.0426     | 30     | 0.0838        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
+| 2.3830     | 35     | 0.0647        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
+| 2.7234     | 40     | 0.0752        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
+| **2.9957** | **44** | **-**         | **0.6668** | **0.2304**              | **0.2354**             | **0.2304**             | **0.2344**             | **0.2155**            | **0.2321**             |
+| 3.0638     | 45     | 0.0706        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
+| 3.4043     | 50     | 0.0478        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
+| 3.7447     | 55     | 0.0768        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
+| 3.9489     | 58     | -             | 0.6040     | 0.2318                  | 0.2293                 | 0.2292                 | 0.2305                 | 0.2165                | 0.2264                 |
+| 4.0851     | 60     | 0.0793        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
+| 4.4255     | 65     | 0.0559        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
+| 4.7660     | 70     | 0.0654        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
+| 4.9702     | 73     | -             | 0.6105     | 0.2328                  | 0.2328                 | 0.2313                 | 0.2364                 | 0.2279                | 0.2320                 |
+| 5.1064     | 75     | 0.0734        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
+| 5.4468     | 80     | 0.0616        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
+| 5.7191     | 84     | -             | 0.6107     | 0.2368                  | 0.2341                 | 0.2351                 | 0.2391                 | 0.2340                | 0.2322                 |
+
+* 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

@@ -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": "4.42.3",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.42.3",
+    "pytorch": "2.3.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

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+size 2271064456

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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

sentencepiece.bpe.model

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+size 5069051

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

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+size 17083053

tokenizer_config.json

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