|
--- |
|
base_model: cross-encoder/ms-marco-MiniLM-L-4-v2 |
|
datasets: [] |
|
language: [] |
|
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:4173 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: 'Aquelles persones (físiques o jurídiques) que es disposin a exercir |
|
una de les següents activitats: ... Han de comunicar-ho a l''Ajuntament prèviament |
|
a la data prevista de la seva obertura.' |
|
sentences: |
|
- Quin és el benefici que es pretén obtenir amb aquests ajuts econòmics per a les |
|
empreses d'hostaleria i restauració? |
|
- Quin és el benefici del sistema de teleassistència per a les persones que viuen |
|
amb altres persones amb discapacitat? |
|
- Quin és el propòsit de la comunicació prèvia d'una activitat recreativa o un espectacle |
|
públic? |
|
- source_sentence: Les persones titulars d’activitats que generin residus comercials |
|
o industrials assimilables als municipals, vindran obligats a acreditar davant |
|
l’Ajuntament que tenen contractat un gestor autoritzat per la recollida, tractament |
|
i eliminació dels residus que produeixi l’activitat corresponent. |
|
sentences: |
|
- Quin és el paper de l'Ajuntament en l'acreditació de recollida de residus? |
|
- Quin és el benefici de les activitats d'animació socio-cultural? |
|
- Quin és el benefici de l'ajut per a la creació de noves empreses? |
|
- source_sentence: Modificació de sol·licitud de permís d'ocupació de la via pública |
|
per filmacions, rodatges o sessions fotogràfiques. |
|
sentences: |
|
- Quin és el grau de discapacitat mínim per a rebre l'ajut de 300€ anuals? |
|
- Quin és el requisit per a la constitució o modificació del règim de propietat |
|
horitzontal? |
|
- Quin és el tipus de permís que es modifica? |
|
- source_sentence: El beneficiari és l'encarregat de complir les condicions de la |
|
subvenció i de presentar els informes de seguiment del projecte. |
|
sentences: |
|
- Quin és el paper del beneficiari en el procés de subvencions? |
|
- Quin és el càlcul dels interessos de demora en el fraccionament i l'ajornament? |
|
- Quin és el període de temps en què es poden efectuar les despeses mèdiques per |
|
a rebre l'ajuda? |
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- source_sentence: Aquest tràmit permet sol·licitar la llicència per a realitzar obres |
|
d'excavació a la via pública per a la instal·lació o reparació d'infraestructures |
|
de serveis i subministraments. |
|
sentences: |
|
- Quin és el paper de la via pública en aquest tràmit? |
|
- Quin és el requisit principal per obtenir el certificat? |
|
- Quin és l'objectiu de presentar una denúncia per presumpta infracció urbanística? |
|
model-index: |
|
- name: SentenceTransformer based on cross-encoder/ms-marco-MiniLM-L-4-v2 |
|
results: |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.05172413793103448 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.1271551724137931 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.1788793103448276 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.3254310344827586 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.05172413793103448 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.042385057471264365 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.03577586206896552 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.032543103448275865 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.05172413793103448 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.1271551724137931 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.1788793103448276 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.3254310344827586 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.16276692425092115 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.11428999042145602 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.13620420069102204 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.05172413793103448 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.1271551724137931 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.1788793103448276 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.3254310344827586 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.05172413793103448 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.042385057471264365 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.03577586206896552 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.032543103448275865 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.05172413793103448 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.1271551724137931 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.1788793103448276 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.3254310344827586 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.16276692425092115 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.11428999042145602 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.13620420069102204 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.04525862068965517 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.1206896551724138 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.17025862068965517 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.3232758620689655 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.04525862068965517 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.04022988505747126 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.03405172413793103 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.032327586206896554 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.04525862068965517 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.1206896551724138 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.17025862068965517 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.3232758620689655 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.15757998924712813 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.10828971674876857 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.13108979755674435 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.04741379310344827 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.1206896551724138 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.17672413793103448 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.3146551724137931 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.04741379310344827 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.04022988505747126 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.0353448275862069 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.03146551724137931 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.04741379310344827 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.1206896551724138 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.17672413793103448 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.3146551724137931 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.15563167494658142 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.10829484811165858 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.13156999055462598 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.036637931034482756 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.10129310344827586 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.15301724137931033 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.28448275862068967 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.036637931034482756 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.033764367816091954 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.03060344827586207 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.028448275862068963 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.036637931034482756 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.10129310344827586 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.15301724137931033 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.28448275862068967 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.13580741965441598 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.09167179802955677 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.1149404289076573 |
|
name: Cosine Map@100 |
|
--- |
|
|
|
# SentenceTransformer based on cross-encoder/ms-marco-MiniLM-L-4-v2 |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [cross-encoder/ms-marco-MiniLM-L-4-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-4-v2). It maps sentences & paragraphs to a 384-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:** [cross-encoder/ms-marco-MiniLM-L-4-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-4-v2) <!-- at revision 1f1ab0943a42a52afd702e7e8337bec985c189ea --> |
|
- **Maximum Sequence Length:** 512 tokens |
|
- **Output Dimensionality:** 384 tokens |
|
- **Similarity Function:** Cosine Similarity |
|
<!-- - **Training Dataset:** Unknown --> |
|
<!-- - **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': 512, 'do_lower_case': False}) with Transformer model: BertModel |
|
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
|
) |
|
``` |
|
|
|
## 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/sitges10242608-4ep-rerankv3") |
|
# Run inference |
|
sentences = [ |
|
"Aquest tràmit permet sol·licitar la llicència per a realitzar obres d'excavació a la via pública per a la instal·lació o reparació d'infraestructures de serveis i subministraments.", |
|
'Quin és el paper de la via pública en aquest tràmit?', |
|
"Quin és l'objectiu de presentar una denúncia per presumpta infracció urbanística?", |
|
] |
|
embeddings = model.encode(sentences) |
|
print(embeddings.shape) |
|
# [3, 384] |
|
|
|
# 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_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 | 0.0517 | |
|
| cosine_accuracy@3 | 0.1272 | |
|
| cosine_accuracy@5 | 0.1789 | |
|
| cosine_accuracy@10 | 0.3254 | |
|
| cosine_precision@1 | 0.0517 | |
|
| cosine_precision@3 | 0.0424 | |
|
| cosine_precision@5 | 0.0358 | |
|
| cosine_precision@10 | 0.0325 | |
|
| cosine_recall@1 | 0.0517 | |
|
| cosine_recall@3 | 0.1272 | |
|
| cosine_recall@5 | 0.1789 | |
|
| cosine_recall@10 | 0.3254 | |
|
| cosine_ndcg@10 | 0.1628 | |
|
| cosine_mrr@10 | 0.1143 | |
|
| **cosine_map@100** | **0.1362** | |
|
|
|
#### 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 | 0.0517 | |
|
| cosine_accuracy@3 | 0.1272 | |
|
| cosine_accuracy@5 | 0.1789 | |
|
| cosine_accuracy@10 | 0.3254 | |
|
| cosine_precision@1 | 0.0517 | |
|
| cosine_precision@3 | 0.0424 | |
|
| cosine_precision@5 | 0.0358 | |
|
| cosine_precision@10 | 0.0325 | |
|
| cosine_recall@1 | 0.0517 | |
|
| cosine_recall@3 | 0.1272 | |
|
| cosine_recall@5 | 0.1789 | |
|
| cosine_recall@10 | 0.3254 | |
|
| cosine_ndcg@10 | 0.1628 | |
|
| cosine_mrr@10 | 0.1143 | |
|
| **cosine_map@100** | **0.1362** | |
|
|
|
#### 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 | 0.0453 | |
|
| cosine_accuracy@3 | 0.1207 | |
|
| cosine_accuracy@5 | 0.1703 | |
|
| cosine_accuracy@10 | 0.3233 | |
|
| cosine_precision@1 | 0.0453 | |
|
| cosine_precision@3 | 0.0402 | |
|
| cosine_precision@5 | 0.0341 | |
|
| cosine_precision@10 | 0.0323 | |
|
| cosine_recall@1 | 0.0453 | |
|
| cosine_recall@3 | 0.1207 | |
|
| cosine_recall@5 | 0.1703 | |
|
| cosine_recall@10 | 0.3233 | |
|
| cosine_ndcg@10 | 0.1576 | |
|
| cosine_mrr@10 | 0.1083 | |
|
| **cosine_map@100** | **0.1311** | |
|
|
|
#### 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 | 0.0474 | |
|
| cosine_accuracy@3 | 0.1207 | |
|
| cosine_accuracy@5 | 0.1767 | |
|
| cosine_accuracy@10 | 0.3147 | |
|
| cosine_precision@1 | 0.0474 | |
|
| cosine_precision@3 | 0.0402 | |
|
| cosine_precision@5 | 0.0353 | |
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| cosine_precision@10 | 0.0315 | |
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| cosine_recall@1 | 0.0474 | |
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| cosine_recall@3 | 0.1207 | |
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| cosine_recall@5 | 0.1767 | |
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| cosine_recall@10 | 0.3147 | |
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| cosine_ndcg@10 | 0.1556 | |
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| cosine_mrr@10 | 0.1083 | |
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| **cosine_map@100** | **0.1316** | |
|
|
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#### Information Retrieval |
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* Dataset: `dim_64` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.0366 | |
|
| cosine_accuracy@3 | 0.1013 | |
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| cosine_accuracy@5 | 0.153 | |
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| cosine_accuracy@10 | 0.2845 | |
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| cosine_precision@1 | 0.0366 | |
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| cosine_precision@3 | 0.0338 | |
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| cosine_precision@5 | 0.0306 | |
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| cosine_precision@10 | 0.0284 | |
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| cosine_recall@1 | 0.0366 | |
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| cosine_recall@3 | 0.1013 | |
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| cosine_recall@5 | 0.153 | |
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| cosine_recall@10 | 0.2845 | |
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| cosine_ndcg@10 | 0.1358 | |
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| cosine_mrr@10 | 0.0917 | |
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| **cosine_map@100** | **0.1149** | |
|
|
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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## Training Details |
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|
|
### Training Dataset |
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|
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#### Unnamed Dataset |
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|
|
|
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* Size: 4,173 training samples |
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* Columns: <code>positive</code> and <code>anchor</code> |
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* Approximate statistics based on the first 1000 samples: |
|
| | positive | anchor | |
|
|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 10 tokens</li><li>mean: 67.49 tokens</li><li>max: 214 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 28.0 tokens</li><li>max: 61 tokens</li></ul> | |
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* Samples: |
|
| positive | anchor | |
|
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------| |
|
| <code>Havent-se d'acreditar la matriculació i inscripció en el respectiu centre públic o concertat, així com el cost de les llars d'infants, de l'educació especialitzada per les discapacitats físiques, psíquiques i sensorials en centres públics, concertats o privats.</code> | <code>Quin és el requisit per acreditar la llar d'infants?</code> | |
|
| <code>El volant històric de convivència és el document que informa de la residencia en el municipi de Sitges, així com altres fets relatius a l'empadronament d'una persona, i detalla tots els domicilis, la data inicial i final en els que ha estat empadronada en cadascun d'ells, i les persones amb les què constava inscrites, segons les dades que consten al Padró Municipal d'Habitants fins a la data d'expedició.</code> | <code>Quin és el propòsit del volant històric de convivència?</code> | |
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| <code>Instal·lació de tanques sense obra.</code> | <code>Quins són els exemples d'instal·lacions que es poden comunicar amb aquest tràmit?</code> | |
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* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
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```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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"matryoshka_dims": [ |
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384, |
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256, |
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128, |
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64 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": -1 |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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|
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- `eval_strategy`: epoch |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 16 |
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- `gradient_accumulation_steps`: 16 |
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- `num_train_epochs`: 10 |
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- `lr_scheduler_type`: cosine |
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- `warmup_ratio`: 0.2 |
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- `bf16`: True |
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- `tf32`: False |
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- `load_best_model_at_end`: True |
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- `optim`: adamw_torch_fused |
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- `batch_sampler`: no_duplicates |
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|
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#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: epoch |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 16 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 16 |
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- `eval_accumulation_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
|
- `num_train_epochs`: 10 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: cosine |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.2 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
|
- `log_level_replica`: warning |
|
- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
|
- `save_safetensors`: True |
|
- `save_on_each_node`: False |
|
- `save_only_model`: False |
|
- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
|
- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: True |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: False |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: True |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
|
- `optim`: adamw_torch_fused |
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- `optim_args`: None |
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- `adafactor`: False |
|
- `group_by_length`: False |
|
- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: False |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
|
- `push_to_hub_model_id`: None |
|
- `push_to_hub_organization`: None |
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- `mp_parameters`: |
|
- `auto_find_batch_size`: False |
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- `full_determinism`: False |
|
- `torchdynamo`: None |
|
- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `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 |
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- `optim_target_modules`: None |
|
- `batch_eval_metrics`: False |
|
- `eval_on_start`: False |
|
- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
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|
|
### Training Logs |
|
| Epoch | Step | Training Loss | 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 | |
|
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 0.6130 | 10 | 11.3695 | - | - | - | - | - | |
|
| 0.9808 | 16 | - | 0.0214 | 0.0243 | 0.0234 | 0.0199 | 0.0234 | |
|
| 1.2261 | 20 | 10.653 | - | - | - | - | - | |
|
| 1.8391 | 30 | 9.0745 | - | - | - | - | - | |
|
| 1.9617 | 32 | - | 0.0495 | 0.0517 | 0.0589 | 0.0481 | 0.0589 | |
|
| 2.4521 | 40 | 7.3468 | - | - | - | - | - | |
|
| 2.9425 | 48 | - | 0.0764 | 0.0734 | 0.0811 | 0.0709 | 0.0811 | |
|
| 3.0651 | 50 | 5.887 | - | - | - | - | - | |
|
| 3.6782 | 60 | 5.3568 | - | - | - | - | - | |
|
| 3.9847 | 65 | - | 0.0922 | 0.0857 | 0.0896 | 0.0808 | 0.0896 | |
|
| 4.2912 | 70 | 4.8338 | - | - | - | - | - | |
|
| **4.9042** | **80** | **4.9251** | **0.0899** | **0.0899** | **0.0906** | **0.0837** | **0.0906** | |
|
| 0.9771 | 8 | - | 0.0953 | 0.0965 | 0.0957 | 0.0841 | 0.0957 | |
|
| 1.2214 | 10 | 6.7779 | - | - | - | - | - | |
|
| 1.9542 | 16 | - | 0.1056 | 0.1036 | 0.1078 | 0.0948 | 0.1078 | |
|
| 2.4427 | 20 | 5.8485 | - | - | - | - | - | |
|
| 2.9313 | 24 | - | 0.1112 | 0.1107 | 0.1170 | 0.1009 | 0.1170 | |
|
| 3.6641 | 30 | 4.6394 | - | - | - | - | - | |
|
| 3.9084 | 32 | - | 0.1243 | 0.1189 | 0.1247 | 0.1152 | 0.1247 | |
|
| 4.8855 | 40 | 3.8786 | 0.1248 | 0.1248 | 0.1335 | 0.1148 | 0.1335 | |
|
| 5.9847 | 49 | - | 0.1298 | 0.1298 | 0.1371 | 0.1204 | 0.1371 | |
|
| 6.1069 | 50 | 3.3198 | - | - | - | - | - | |
|
| 6.9618 | 57 | - | 0.1284 | 0.1347 | 0.1370 | 0.1208 | 0.1370 | |
|
| 7.3282 | 60 | 3.081 | - | - | - | - | - | |
|
| 7.9389 | 65 | - | 0.1273 | 0.1344 | 0.1360 | 0.1215 | 0.1360 | |
|
| 8.5496 | 70 | 2.8556 | - | - | - | - | - | |
|
| 8.9160 | 73 | - | 0.1313 | 0.1315 | 0.1350 | 0.1147 | 0.1350 | |
|
| **9.771** | **80** | **2.7635** | **0.1316** | **0.1311** | **0.1362** | **0.1149** | **0.1362** | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.0.1 |
|
- Transformers: 4.42.4 |
|
- PyTorch: 2.4.0+cu121 |
|
- Accelerate: 0.34.0.dev0 |
|
- Datasets: 2.21.0 |
|
- 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} |
|
} |
|
``` |
|
|
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*Clearly define terms in order to be accessible across audiences.* |
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