|
--- |
|
base_model: BAAI/bge-m3 |
|
datasets: [] |
|
language: |
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- es |
|
library_name: sentence-transformers |
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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: |
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- sentence-transformers |
|
- sentence-similarity |
|
- feature-extraction |
|
- generated_from_trainer |
|
- dataset_size:81 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
|
widget: |
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- source_sentence: Disposeu del servei OAC360º d'assistència en la tramitació electrònica |
|
amb el que podeu contactar de dilluns a divendres de 08:00 a 20:00 a través del |
|
tel. 935 955 094, del correu [email protected], o del servei Truca'm, omplint |
|
el formulari perquè us truquin. |
|
sentences: |
|
- Com es pot demanar la comunicació prèvia d'obres per instal·lacions de plaques |
|
solars en sol urbà? |
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- Quin és el correu electrònic per contactar amb el servei OAC360º? |
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- Quin és l'efecte del silenci administratiu? |
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- source_sentence: Positiu, llevat els casos en els quals manquin informes preceptius |
|
i vinculants d’altres administracions o d’aquells en els què es transfereixin |
|
al sol·licitant facultats contràries al planejament i la legislació urbanística. |
|
sentences: |
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- Quin és el document que cal aportar per a aquest tràmit? |
|
- Quin és el lloc on es pot tramitar la presentació de justificants de pagament |
|
per als ajuts del lloguer just dels habitatges? |
|
- Quin és el sentit del silenci administratiu per a la comunicació prèvia d'obres |
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per instal·lacions de plaques solars en sol urbà? |
|
model-index: |
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- name: BGE large Legal Spanish |
|
results: |
|
- task: |
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type: information-retrieval |
|
name: Information Retrieval |
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dataset: |
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name: dim 1024 |
|
type: dim_1024 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.1111111111111111 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.3333333333333333 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.4444444444444444 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.7777777777777778 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.1111111111111111 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.1111111111111111 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.08888888888888889 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.07777777777777778 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.1111111111111111 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.3333333333333333 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.4444444444444444 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.7777777777777778 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.37561164042849293 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.2550705467372134 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.26453109424123916 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
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name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.1111111111111111 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.3333333333333333 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.4444444444444444 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.7777777777777778 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.1111111111111111 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.1111111111111111 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.08888888888888889 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.07777777777777778 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.1111111111111111 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.3333333333333333 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.4444444444444444 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.7777777777777778 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.37561164042849293 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.2550705467372134 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.26591710758377424 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.1111111111111111 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.3333333333333333 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.4444444444444444 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.7777777777777778 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.1111111111111111 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.1111111111111111 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.08888888888888889 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.07777777777777778 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.1111111111111111 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.3333333333333333 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.4444444444444444 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.7777777777777778 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.36941287151905455 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.24828042328042324 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.25912698412698415 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.1111111111111111 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.3333333333333333 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.4444444444444444 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.6666666666666666 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.1111111111111111 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.1111111111111111 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.08888888888888889 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.06666666666666668 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.1111111111111111 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.3333333333333333 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.4444444444444444 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.6666666666666666 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.33724514013077883 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.23796296296296296 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.2553057025279247 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.1111111111111111 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.3333333333333333 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.5555555555555556 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.7777777777777778 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.1111111111111111 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.1111111111111111 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1111111111111111 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.07777777777777778 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.1111111111111111 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.3333333333333333 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.5555555555555556 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.7777777777777778 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.3920021980903836 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.27248677248677244 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.2795432240996757 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.2222222222222222 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.3333333333333333 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.4444444444444444 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5555555555555556 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.2222222222222222 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.1111111111111111 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.08888888888888889 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.05555555555555555 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.2222222222222222 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.3333333333333333 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.4444444444444444 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5555555555555556 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.3626677657118585 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.3029100529100529 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.32598958775429365 |
|
name: Cosine Map@100 |
|
--- |
|
|
|
# BGE large Legal Spanish |
|
|
|
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:** es |
|
- **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/bge-m3-es-legal-tmp-6") |
|
# Run inference |
|
sentences = [ |
|
'Positiu, llevat els casos en els quals manquin informes preceptius i vinculants d’altres administracions o d’aquells en els què es transfereixin al sol·licitant facultats contràries al planejament i la legislació urbanística.', |
|
"Quin és el sentit del silenci administratiu per a la comunicació prèvia d'obres per instal·lacions de plaques solars en sol urbà?", |
|
'Quin és el lloc on es pot tramitar la presentació de justificants de pagament per als ajuts del lloguer just dels habitatges?', |
|
] |
|
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 | 0.1111 | |
|
| cosine_accuracy@3 | 0.3333 | |
|
| cosine_accuracy@5 | 0.4444 | |
|
| cosine_accuracy@10 | 0.7778 | |
|
| cosine_precision@1 | 0.1111 | |
|
| cosine_precision@3 | 0.1111 | |
|
| cosine_precision@5 | 0.0889 | |
|
| cosine_precision@10 | 0.0778 | |
|
| cosine_recall@1 | 0.1111 | |
|
| cosine_recall@3 | 0.3333 | |
|
| cosine_recall@5 | 0.4444 | |
|
| cosine_recall@10 | 0.7778 | |
|
| cosine_ndcg@10 | 0.3756 | |
|
| cosine_mrr@10 | 0.2551 | |
|
| **cosine_map@100** | **0.2645** | |
|
|
|
#### 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 | 0.1111 | |
|
| cosine_accuracy@3 | 0.3333 | |
|
| cosine_accuracy@5 | 0.4444 | |
|
| cosine_accuracy@10 | 0.7778 | |
|
| cosine_precision@1 | 0.1111 | |
|
| cosine_precision@3 | 0.1111 | |
|
| cosine_precision@5 | 0.0889 | |
|
| cosine_precision@10 | 0.0778 | |
|
| cosine_recall@1 | 0.1111 | |
|
| cosine_recall@3 | 0.3333 | |
|
| cosine_recall@5 | 0.4444 | |
|
| cosine_recall@10 | 0.7778 | |
|
| cosine_ndcg@10 | 0.3756 | |
|
| cosine_mrr@10 | 0.2551 | |
|
| **cosine_map@100** | **0.2659** | |
|
|
|
#### 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 | 0.1111 | |
|
| cosine_accuracy@3 | 0.3333 | |
|
| cosine_accuracy@5 | 0.4444 | |
|
| cosine_accuracy@10 | 0.7778 | |
|
| cosine_precision@1 | 0.1111 | |
|
| cosine_precision@3 | 0.1111 | |
|
| cosine_precision@5 | 0.0889 | |
|
| cosine_precision@10 | 0.0778 | |
|
| cosine_recall@1 | 0.1111 | |
|
| cosine_recall@3 | 0.3333 | |
|
| cosine_recall@5 | 0.4444 | |
|
| cosine_recall@10 | 0.7778 | |
|
| cosine_ndcg@10 | 0.3694 | |
|
| cosine_mrr@10 | 0.2483 | |
|
| **cosine_map@100** | **0.2591** | |
|
|
|
#### 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 | 0.1111 | |
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| cosine_accuracy@3 | 0.3333 | |
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| cosine_accuracy@5 | 0.4444 | |
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| cosine_accuracy@10 | 0.6667 | |
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| cosine_precision@1 | 0.1111 | |
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| cosine_precision@3 | 0.1111 | |
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| cosine_precision@5 | 0.0889 | |
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| cosine_precision@10 | 0.0667 | |
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| cosine_recall@1 | 0.1111 | |
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| cosine_recall@3 | 0.3333 | |
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| cosine_recall@5 | 0.4444 | |
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| cosine_recall@10 | 0.6667 | |
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| cosine_ndcg@10 | 0.3372 | |
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| cosine_mrr@10 | 0.238 | |
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| **cosine_map@100** | **0.2553** | |
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#### Information Retrieval |
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* Dataset: `dim_128` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
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| Metric | Value | |
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|:--------------------|:-----------| |
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| cosine_accuracy@1 | 0.1111 | |
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| cosine_accuracy@3 | 0.3333 | |
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| cosine_accuracy@5 | 0.5556 | |
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| cosine_accuracy@10 | 0.7778 | |
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| cosine_precision@1 | 0.1111 | |
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| cosine_precision@3 | 0.1111 | |
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| cosine_precision@5 | 0.1111 | |
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| cosine_precision@10 | 0.0778 | |
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| cosine_recall@1 | 0.1111 | |
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| cosine_recall@3 | 0.3333 | |
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| cosine_recall@5 | 0.5556 | |
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| cosine_recall@10 | 0.7778 | |
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| cosine_ndcg@10 | 0.392 | |
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| cosine_mrr@10 | 0.2725 | |
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| **cosine_map@100** | **0.2795** | |
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|
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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) |
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|
| Metric | Value | |
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|:--------------------|:----------| |
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| cosine_accuracy@1 | 0.2222 | |
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| cosine_accuracy@3 | 0.3333 | |
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| cosine_accuracy@5 | 0.4444 | |
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| cosine_accuracy@10 | 0.5556 | |
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| cosine_precision@1 | 0.2222 | |
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| cosine_precision@3 | 0.1111 | |
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| cosine_precision@5 | 0.0889 | |
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| cosine_precision@10 | 0.0556 | |
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| cosine_recall@1 | 0.2222 | |
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| cosine_recall@3 | 0.3333 | |
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| cosine_recall@5 | 0.4444 | |
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| cosine_recall@10 | 0.5556 | |
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| cosine_ndcg@10 | 0.3627 | |
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| cosine_mrr@10 | 0.3029 | |
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| **cosine_map@100** | **0.326** | |
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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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--> |
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|
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## Training Details |
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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`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `gradient_accumulation_steps`: 16 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 6 |
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- `lr_scheduler_type`: cosine |
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- `warmup_ratio`: 0.1 |
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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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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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|
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- `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`: 16 |
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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`: 2e-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 |
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- `num_train_epochs`: 6 |
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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`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `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 |
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- `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 |
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- `optim`: adamw_torch_fused |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `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 |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
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|
|
</details> |
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|
|
### 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 | |
|
|:-------:|:-----:|:-------------:|:----------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 1.0 | 1 | - | 3.7675 | 0.2475 | 0.2919 | 0.2372 | 0.2511 | 0.2510 | 0.2468 | |
|
| **2.0** | **2** | **-** | **3.9701** | **0.2533** | **0.3028** | **0.2473** | **0.2601** | **0.3449** | **0.2716** | |
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| 3.0 | 4 | - | 4.1211 | 0.2645 | 0.2704 | 0.2548 | 0.2614 | 0.3283 | 0.2654 | |
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| 4.0 | 5 | 1.8359 | 4.0228 | 0.2645 | 0.2789 | 0.2553 | 0.2619 | 0.3260 | 0.2659 | |
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| 5.0 | 6 | - | 3.9758 | 0.2645 | 0.2795 | 0.2553 | 0.2591 | 0.3260 | 0.2659 | |
|
|
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* The bold row denotes the saved checkpoint. |
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|
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### Framework Versions |
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- Python: 3.10.12 |
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- Sentence Transformers: 3.0.1 |
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- Transformers: 4.42.3 |
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- PyTorch: 2.3.1+cu121 |
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- Accelerate: 0.32.1 |
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- Datasets: 2.20.0 |
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- Tokenizers: 0.19.1 |
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|
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## Citation |
|
|
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### 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", |
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month = "11", |
|
year = "2019", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/1908.10084", |
|
} |
|
``` |
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|
|
#### 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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## Model Card Authors |
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*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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