|
--- |
|
base_model: BAAI/bge-m3 |
|
library_name: sentence-transformers |
|
metrics: |
|
- cosine_accuracy@1 |
|
- cosine_accuracy@3 |
|
- cosine_accuracy@5 |
|
- cosine_accuracy@10 |
|
- cosine_precision@1 |
|
- cosine_precision@3 |
|
- cosine_precision@5 |
|
- cosine_precision@10 |
|
- cosine_recall@1 |
|
- cosine_recall@3 |
|
- cosine_recall@5 |
|
- cosine_recall@10 |
|
- cosine_ndcg@10 |
|
- cosine_mrr@10 |
|
- cosine_map@100 |
|
pipeline_tag: sentence-similarity |
|
tags: |
|
- sentence-transformers |
|
- sentence-similarity |
|
- feature-extraction |
|
- generated_from_trainer |
|
- dataset_size:5520 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: Pagar un rebut o una liquidació pendent de pagament |
|
sentences: |
|
- Què és el tràmit per pagar un rebut o liquidació? |
|
- Quin és el tràmit que permet la inscripció d'una entitat o associació? |
|
- Quin és el límit de temps per a la instal·lació de tanques provisionals? |
|
- source_sentence: Mitjançant decret de data 11/10/2022 núm. 202204494 s'inicia el |
|
procés de concurrència competitiva per accedir a les parades vacants del mercat |
|
de les Fonts. |
|
sentences: |
|
- Quin és el mercat on es va iniciar el procés de concurrència competitiva per accedir |
|
a les parades vacants? |
|
- Puc sol·licitar un certificat històric d'empadronament per a una persona que ja |
|
no viu al municipi? |
|
- Necessito obtenir un duplicat del títol de dret funerari perquè he perdut l'original |
|
- source_sentence: Comunicar les dades per realitzar la notificació electrònica de |
|
tots els procediments en què l’obligat legal sigui titular o part implicada, i |
|
hagi de ser notificat o notificada. |
|
sentences: |
|
- Quin és el paper de l'Ajuntament en la inspecció de les condicions específiques? |
|
- Quin és el tràmit relacionat amb la targeta ciutadana de serveis? |
|
- Qui és el titular o part implicada en els procediments? |
|
- source_sentence: Aquest tràmit permet sol·licitar l'informe municipal sobre la integració |
|
social de persones estrangeres. |
|
sentences: |
|
- Puc canviar la concessió del meu dret funerari per una raó específica? |
|
- Quin és el procediment per a obtenir l'informe d'inserció social? |
|
- Quin és el propòsit de la formació en higiene alimentària |
|
- source_sentence: Permet tramitar la baixa de les activitats esportives municipals. |
|
sentences: |
|
- Quin és el procés per a donar de baixa una activitat esportiva? |
|
- On es pot recollir els dorsals el dia de la cursa? |
|
- Quin és el benefici fiscal que es pot obtenir? |
|
model-index: |
|
- name: SentenceTransformer based on BAAI/bge-m3 |
|
results: |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 1024 |
|
type: dim_1024 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.1 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.22608695652173913 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.30434782608695654 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.4956521739130435 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.1 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.0753623188405797 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.060869565217391314 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.04956521739130433 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.1 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.22608695652173913 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.30434782608695654 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.4956521739130435 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.2644535096144644 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.19486714975845426 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.21422014718167715 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.1 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.21304347826086956 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.49130434782608695 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.1 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.07101449275362319 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.06000000000000001 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.04913043478260868 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.1 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.21304347826086956 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.49130434782608695 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.2611989525147102 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.19224465148378198 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.21168860407432996 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.09565217391304348 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.25217391304347825 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3217391304347826 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5043478260869565 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.09565217391304348 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.08405797101449275 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.06434782608695652 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.05043478260869564 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.09565217391304348 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.25217391304347825 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3217391304347826 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5043478260869565 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.2736727362077943 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.20330400276052454 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.2225493022129085 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.09130434782608696 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.24347826086956523 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.32608695652173914 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.4782608695652174 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.09130434782608696 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.08115942028985507 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.06521739130434782 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.04782608695652173 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.09130434782608696 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.24347826086956523 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.32608695652173914 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.4782608695652174 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.25842339032219125 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.19112146307798494 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.21262325852877148 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.09565217391304348 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.2217391304347826 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.32608695652173914 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5130434782608696 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.09565217391304348 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.07391304347826087 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.06521739130434782 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.05130434782608694 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.09565217391304348 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.2217391304347826 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.32608695652173914 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5130434782608696 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.2703816814799584 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.1968685300207041 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.21575875323163748 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.10434782608695652 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.23478260869565218 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3217391304347826 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.49130434782608695 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.10434782608695652 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.0782608695652174 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.06434782608695652 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.049130434782608694 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.10434782608695652 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.23478260869565218 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3217391304347826 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.49130434782608695 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.268671836286108 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.20097135955831624 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.22058427749634182 |
|
name: Cosine Map@100 |
|
--- |
|
|
|
# SentenceTransformer based on BAAI/bge-m3 |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) on the json dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
|
|
|
## Model Details |
|
|
|
### Model Description |
|
- **Model Type:** Sentence Transformer |
|
- **Base model:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 --> |
|
- **Maximum Sequence Length:** 8192 tokens |
|
- **Output Dimensionality:** 1024 tokens |
|
- **Similarity Function:** Cosine Similarity |
|
- **Training Dataset:** |
|
- json |
|
<!-- - **Language:** Unknown --> |
|
<!-- - **License:** Unknown --> |
|
|
|
### Model Sources |
|
|
|
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
|
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
|
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
|
### Full Model Architecture |
|
|
|
``` |
|
SentenceTransformer( |
|
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel |
|
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
|
(2): Normalize() |
|
) |
|
``` |
|
|
|
## Usage |
|
|
|
### Direct Usage (Sentence Transformers) |
|
|
|
First install the Sentence Transformers library: |
|
|
|
```bash |
|
pip install -U sentence-transformers |
|
``` |
|
|
|
Then you can load this model and run inference. |
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
|
|
# Download from the 🤗 Hub |
|
model = SentenceTransformer("adriansanz/sqv-v5-5ep") |
|
# Run inference |
|
sentences = [ |
|
'Permet tramitar la baixa de les activitats esportives municipals.', |
|
'Quin és el procés per a donar de baixa una activitat esportiva?', |
|
'Quin és el benefici fiscal que es pot obtenir?', |
|
] |
|
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 | 0.1 | |
|
| cosine_accuracy@3 | 0.2261 | |
|
| cosine_accuracy@5 | 0.3043 | |
|
| cosine_accuracy@10 | 0.4957 | |
|
| cosine_precision@1 | 0.1 | |
|
| cosine_precision@3 | 0.0754 | |
|
| cosine_precision@5 | 0.0609 | |
|
| cosine_precision@10 | 0.0496 | |
|
| cosine_recall@1 | 0.1 | |
|
| cosine_recall@3 | 0.2261 | |
|
| cosine_recall@5 | 0.3043 | |
|
| cosine_recall@10 | 0.4957 | |
|
| cosine_ndcg@10 | 0.2645 | |
|
| cosine_mrr@10 | 0.1949 | |
|
| **cosine_map@100** | **0.2142** | |
|
|
|
#### 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 | 0.1 | |
|
| cosine_accuracy@3 | 0.213 | |
|
| cosine_accuracy@5 | 0.3 | |
|
| cosine_accuracy@10 | 0.4913 | |
|
| cosine_precision@1 | 0.1 | |
|
| cosine_precision@3 | 0.071 | |
|
| cosine_precision@5 | 0.06 | |
|
| cosine_precision@10 | 0.0491 | |
|
| cosine_recall@1 | 0.1 | |
|
| cosine_recall@3 | 0.213 | |
|
| cosine_recall@5 | 0.3 | |
|
| cosine_recall@10 | 0.4913 | |
|
| cosine_ndcg@10 | 0.2612 | |
|
| cosine_mrr@10 | 0.1922 | |
|
| **cosine_map@100** | **0.2117** | |
|
|
|
#### 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 | 0.0957 | |
|
| cosine_accuracy@3 | 0.2522 | |
|
| cosine_accuracy@5 | 0.3217 | |
|
| cosine_accuracy@10 | 0.5043 | |
|
| cosine_precision@1 | 0.0957 | |
|
| cosine_precision@3 | 0.0841 | |
|
| cosine_precision@5 | 0.0643 | |
|
| cosine_precision@10 | 0.0504 | |
|
| cosine_recall@1 | 0.0957 | |
|
| cosine_recall@3 | 0.2522 | |
|
| cosine_recall@5 | 0.3217 | |
|
| cosine_recall@10 | 0.5043 | |
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| cosine_ndcg@10 | 0.2737 | |
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| cosine_mrr@10 | 0.2033 | |
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| **cosine_map@100** | **0.2225** | |
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#### Information Retrieval |
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* Dataset: `dim_256` |
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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 | 0.0913 | |
|
| cosine_accuracy@3 | 0.2435 | |
|
| cosine_accuracy@5 | 0.3261 | |
|
| cosine_accuracy@10 | 0.4783 | |
|
| cosine_precision@1 | 0.0913 | |
|
| cosine_precision@3 | 0.0812 | |
|
| cosine_precision@5 | 0.0652 | |
|
| cosine_precision@10 | 0.0478 | |
|
| cosine_recall@1 | 0.0913 | |
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| cosine_recall@3 | 0.2435 | |
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| cosine_recall@5 | 0.3261 | |
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| cosine_recall@10 | 0.4783 | |
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| cosine_ndcg@10 | 0.2584 | |
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| cosine_mrr@10 | 0.1911 | |
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| **cosine_map@100** | **0.2126** | |
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|
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#### Information Retrieval |
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* 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 | 0.0957 | |
|
| cosine_accuracy@3 | 0.2217 | |
|
| cosine_accuracy@5 | 0.3261 | |
|
| cosine_accuracy@10 | 0.513 | |
|
| cosine_precision@1 | 0.0957 | |
|
| cosine_precision@3 | 0.0739 | |
|
| cosine_precision@5 | 0.0652 | |
|
| cosine_precision@10 | 0.0513 | |
|
| cosine_recall@1 | 0.0957 | |
|
| cosine_recall@3 | 0.2217 | |
|
| cosine_recall@5 | 0.3261 | |
|
| cosine_recall@10 | 0.513 | |
|
| cosine_ndcg@10 | 0.2704 | |
|
| cosine_mrr@10 | 0.1969 | |
|
| **cosine_map@100** | **0.2158** | |
|
|
|
#### 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 | 0.1043 | |
|
| cosine_accuracy@3 | 0.2348 | |
|
| cosine_accuracy@5 | 0.3217 | |
|
| cosine_accuracy@10 | 0.4913 | |
|
| cosine_precision@1 | 0.1043 | |
|
| cosine_precision@3 | 0.0783 | |
|
| cosine_precision@5 | 0.0643 | |
|
| cosine_precision@10 | 0.0491 | |
|
| cosine_recall@1 | 0.1043 | |
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| cosine_recall@3 | 0.2348 | |
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| cosine_recall@5 | 0.3217 | |
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| cosine_recall@10 | 0.4913 | |
|
| cosine_ndcg@10 | 0.2687 | |
|
| cosine_mrr@10 | 0.201 | |
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| **cosine_map@100** | **0.2206** | |
|
|
|
<!-- |
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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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|
|
### Training Dataset |
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|
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#### json |
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|
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* Dataset: json |
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* Size: 5,520 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 | |
|
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 5 tokens</li><li>mean: 43.7 tokens</li><li>max: 117 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 20.51 tokens</li><li>max: 51 tokens</li></ul> | |
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* Samples: |
|
| positive | anchor | |
|
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>L’Ajuntament vol crear un banc de recursos on recollir tots els oferiments de la població i que servirà per atendre les necessitats de les famílies refugiades acollides al poble.</code> | <code>Quin és el paper de l’Ajuntament en la integració de les persones refugiades acollides?</code> | |
|
| <code>Aquest tipus d'actuació requereix la intervenció d'una persona tècnica competent que subscrigui el projecte o la documentació tècnica corresponent i que assumeixi la direcció facultativa de l'execució de les obres.</code> | <code>Quin és el requisit per a la intervenció d'una persona tècnica competent en les obres d'intervenció parcial interior en edificis amb elements catalogats?</code> | |
|
| <code>Aquest títol, adreçat a persones empadronades a Sant Quirze del Vallès, es concedirà segons el nivell d’ingressos, la condició d’edat o de discapacitat, en base als criteris específics que recull l’ordenança reguladora del sistema de tarifació social del transport públic municipal en autobús a Sant Quirze del Vallès.</code> | <code>Quin és el benefici de la TBUS GRATUÏTA per a les persones majors?</code> | |
|
* 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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1024, |
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768, |
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512, |
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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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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`: 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`: 5 |
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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`: True |
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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 |
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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 |
|
- `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 |
|
- `torch_empty_cache_steps`: None |
|
- `learning_rate`: 2e-05 |
|
- `weight_decay`: 0.0 |
|
- `adam_beta1`: 0.9 |
|
- `adam_beta2`: 0.999 |
|
- `adam_epsilon`: 1e-08 |
|
- `max_grad_norm`: 1.0 |
|
- `num_train_epochs`: 5 |
|
- `max_steps`: -1 |
|
- `lr_scheduler_type`: cosine |
|
- `lr_scheduler_kwargs`: {} |
|
- `warmup_ratio`: 0.2 |
|
- `warmup_steps`: 0 |
|
- `log_level`: passive |
|
- `log_level_replica`: warning |
|
- `log_on_each_node`: True |
|
- `logging_nan_inf_filter`: True |
|
- `save_safetensors`: True |
|
- `save_on_each_node`: False |
|
- `save_only_model`: False |
|
- `restore_callback_states_from_checkpoint`: False |
|
- `no_cuda`: False |
|
- `use_cpu`: False |
|
- `use_mps_device`: False |
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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`: True |
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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 |
|
- `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 |
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- `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 |
|
- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
|
- `gradient_checkpointing_kwargs`: None |
|
- `include_inputs_for_metrics`: False |
|
- `eval_do_concat_batches`: True |
|
- `fp16_backend`: auto |
|
- `push_to_hub_model_id`: None |
|
- `push_to_hub_organization`: None |
|
- `mp_parameters`: |
|
- `auto_find_batch_size`: False |
|
- `full_determinism`: False |
|
- `torchdynamo`: None |
|
- `ray_scope`: last |
|
- `ddp_timeout`: 1800 |
|
- `torch_compile`: False |
|
- `torch_compile_backend`: None |
|
- `torch_compile_mode`: None |
|
- `dispatch_batches`: None |
|
- `split_batches`: None |
|
- `include_tokens_per_second`: False |
|
- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
|
- `optim_target_modules`: None |
|
- `batch_eval_metrics`: False |
|
- `eval_on_start`: False |
|
- `eval_use_gather_object`: False |
|
- `batch_sampler`: no_duplicates |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | Training Loss | dim_1024_cosine_map@100 | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 | |
|
|:----------:|:-------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 0.4638 | 10 | 4.122 | - | - | - | - | - | - | |
|
| 0.9275 | 20 | 2.7131 | - | - | - | - | - | - | |
|
| 0.9739 | 21 | - | 0.2085 | 0.1973 | 0.1884 | 0.2087 | 0.1886 | 0.2177 | |
|
| 1.3913 | 30 | 1.6964 | - | - | - | - | - | - | |
|
| 1.8551 | 40 | 1.2311 | - | - | - | - | - | - | |
|
| 1.9942 | 43 | - | 0.2148 | 0.2135 | 0.2170 | 0.2351 | 0.2091 | 0.2386 | |
|
| 2.3188 | 50 | 0.9216 | - | - | - | - | - | - | |
|
| 2.7826 | 60 | 0.737 | - | - | - | - | - | - | |
|
| 2.9681 | 64 | - | 0.2145 | 0.2058 | 0.2072 | 0.2277 | 0.2127 | 0.2085 | |
|
| 3.2464 | 70 | 0.6678 | - | - | - | - | - | - | |
|
| 3.7101 | 80 | 0.555 | - | - | - | - | - | - | |
|
| 3.9884 | 86 | - | 0.2028 | 0.2154 | 0.2117 | 0.2331 | 0.2113 | 0.2028 | |
|
| 4.1739 | 90 | 0.5542 | - | - | - | - | - | - | |
|
| 4.6377 | 100 | 0.5058 | - | - | - | - | - | - | |
|
| **4.8696** | **105** | **-** | **0.2142** | **0.2158** | **0.2126** | **0.2225** | **0.2206** | **0.2117** | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.1.1 |
|
- Transformers: 4.44.2 |
|
- PyTorch: 2.4.1+cu121 |
|
- Accelerate: 0.35.0.dev0 |
|
- Datasets: 3.0.1 |
|
- Tokenizers: 0.19.1 |
|
|
|
## Citation |
|
|
|
### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
month = "11", |
|
year = "2019", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/1908.10084", |
|
} |
|
``` |
|
|
|
#### MatryoshkaLoss |
|
```bibtex |
|
@misc{kusupati2024matryoshka, |
|
title={Matryoshka Representation Learning}, |
|
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
|
year={2024}, |
|
eprint={2205.13147}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.LG} |
|
} |
|
``` |
|
|
|
#### MultipleNegativesRankingLoss |
|
```bibtex |
|
@misc{henderson2017efficient, |
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
year={2017}, |
|
eprint={1705.00652}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
|
|
|
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*Clearly define terms in order to be accessible across audiences.* |
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