|
--- |
|
base_model: nomic-ai/modernbert-embed-base |
|
language: |
|
- fr |
|
library_name: sentence-transformers |
|
license: apache-2.0 |
|
metrics: |
|
- cosine_accuracy@1 |
|
- cosine_accuracy@3 |
|
- cosine_accuracy@5 |
|
- cosine_accuracy@10 |
|
- cosine_precision@1 |
|
- cosine_precision@3 |
|
- cosine_precision@5 |
|
- cosine_precision@10 |
|
- cosine_recall@1 |
|
- cosine_recall@3 |
|
- cosine_recall@5 |
|
- cosine_recall@10 |
|
- cosine_ndcg@10 |
|
- cosine_ndcg@15 |
|
- cosine_ndcg@20 |
|
- cosine_mrr@10 |
|
- cosine_map@100 |
|
pipeline_tag: sentence-similarity |
|
tags: |
|
- sentence-transformers |
|
- sentence-similarity |
|
- feature-extraction |
|
- generated_from_trainer |
|
- dataset_size:47560 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: Pourquoi l'enfant de Jéroboam sera-t-il le seul de sa maison à |
|
être enterré? |
|
sentences: |
|
- Nathan le prophète. |
|
- Parce qu'il est le seul de la maison de Jéroboam en qui se soit trouvé quelque |
|
chose de bon devant l'Éternel, le Dieu d'Israël. |
|
- Deux ans. |
|
- source_sentence: Que dit le texte sur la foi capable de transporter des montagnes |
|
sans charité? |
|
sentences: |
|
- Urie était un Héthien. |
|
- Il dit que même avec une foi capable de transporter des montagnes, sans la charité, |
|
cela ne vaut rien. |
|
- David est allé se présenter devant l'Éternel et a exprimé son humilité et sa gratitude |
|
envers Dieu. |
|
- source_sentence: Quels sont les noms des fils de Schobal? |
|
sentences: |
|
- Reaja, Jachath, Achumaï et Lahad. |
|
- Le côté du midi échut à Obed-Édom, et la maison des magasins à ses fils. |
|
- Meschélémia avait dix-huit fils et frères vaillants. |
|
- source_sentence: Qui a succédé au roi Asa après sa mort? |
|
sentences: |
|
- 'L''un dit: Moi, je suis de Paul! Et un autre: Moi, d''Apollos!' |
|
- 'Neuf fils: Zemira, Joasch, Éliézer, Éljoénaï, Omri, Jerémoth, Abija, Anathoth |
|
et Alameth, enregistrés au nombre de vingt mille deux cents.' |
|
- Josaphat, son fils. |
|
- source_sentence: Quelles tâches les Lévites devaient-ils accomplir dans le service |
|
de la maison de l'Éternel? |
|
sentences: |
|
- Ils devaient prendre soin des parvis et des chambres, purifier toutes les choses |
|
saintes, s'occuper des pains de proposition, de la fleur de farine pour les offrandes, |
|
des galettes sans levain, des gâteaux cuits sur la plaque et des gâteaux frits, |
|
et de toutes les mesures de capacité et de longueur. |
|
- Les chefs des maisons paternelles, les chefs des tribus d'Israël, les chefs de |
|
milliers et de centaines, et les intendants du roi. |
|
- Les enfants sont considérés comme saints. |
|
co2_eq_emissions: |
|
emissions: 11.494424944753328 |
|
energy_consumed: 0.20511474053343792 |
|
source: codecarbon |
|
training_type: fine-tuning |
|
on_cloud: false |
|
cpu_model: Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz |
|
ram_total_size: 7.6847381591796875 |
|
hours_used: 6.806 |
|
hardware_used: 1 x NVIDIA GeForce GTX 1660 Ti |
|
model-index: |
|
- name: modernbert-embed-base-bible |
|
results: |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.17498667614141056 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.24835672410730147 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.2762480014212116 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.320305560490318 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.17498667614141056 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.08278557470243382 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.05524960028424231 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.0320305560490318 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.17498667614141056 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.24835672410730147 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.2762480014212116 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.320305560490318 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.24430049048684818 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_ndcg@15 |
|
value: 0.2525347835304927 |
|
name: Cosine Ndcg@15 |
|
- type: cosine_ndcg@20 |
|
value: 0.2574496509992833 |
|
name: Cosine Ndcg@20 |
|
- type: cosine_mrr@10 |
|
value: 0.2204687601338871 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.22764969395073778 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.17161129863208385 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.24018475750577367 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.2719843666725884 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.31621957718955407 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.17161129863208385 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.08006158583525788 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.05439687333451768 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.03162195771895541 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.17161129863208385 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.24018475750577367 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.2719843666725884 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.31621957718955407 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.23947113373513576 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_ndcg@15 |
|
value: 0.24636222462199156 |
|
name: Cosine Ndcg@15 |
|
- type: cosine_ndcg@20 |
|
value: 0.2517242130957284 |
|
name: Cosine Ndcg@20 |
|
- type: cosine_mrr@10 |
|
value: 0.2154852845384024 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.2225725360678114 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.16024160596908865 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.22757150470776336 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.2602593711138746 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.3075146562444484 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.16024160596908865 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.07585716823592112 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.052051874222774915 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.030751465624444838 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.16024160596908865 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.22757150470776336 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.2602593711138746 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.3075146562444484 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.22844579790475078 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_ndcg@15 |
|
value: 0.2357050364715922 |
|
name: Cosine Ndcg@15 |
|
- type: cosine_ndcg@20 |
|
value: 0.24051535612507915 |
|
name: Cosine Ndcg@20 |
|
- type: cosine_mrr@10 |
|
value: 0.20381231547513284 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.21077486383464478 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.14372002131817374 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.20465446793391368 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.23307869959140168 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.279445727482679 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.14372002131817374 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.06821815597797122 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.04661573991828033 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.0279445727482679 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.14372002131817374 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.20465446793391368 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.23307869959140168 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.279445727482679 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.20572968417646773 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_ndcg@15 |
|
value: 0.21411686675503838 |
|
name: Cosine Ndcg@15 |
|
- type: cosine_ndcg@20 |
|
value: 0.21935674398662894 |
|
name: Cosine Ndcg@20 |
|
- type: cosine_mrr@10 |
|
value: 0.1828928000406064 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.19012440317942259 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.11067685201634393 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.15953100017765146 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.18617871735654645 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.22721620181204477 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.11067685201634393 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.05317700005921715 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.03723574347130929 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.022721620181204476 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.11067685201634393 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.15953100017765146 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.18617871735654645 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.22721620181204477 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.16327341570689552 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_ndcg@15 |
|
value: 0.1699977455983759 |
|
name: Cosine Ndcg@15 |
|
- type: cosine_ndcg@20 |
|
value: 0.17462327712912765 |
|
name: Cosine Ndcg@20 |
|
- type: cosine_mrr@10 |
|
value: 0.1435284115422685 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.1500325081763102 |
|
name: Cosine Map@100 |
|
--- |
|
|
|
# modernbert-embed-base-bible |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/modernbert-embed-base](https://huggingface.co./nomic-ai/modernbert-embed-base) on the json dataset. It maps sentences & paragraphs to a 768-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:** [nomic-ai/modernbert-embed-base](https://huggingface.co./nomic-ai/modernbert-embed-base) <!-- at revision bb0033c9f3def40c3c5b26ff0b53c74f3320d703 --> |
|
- **Maximum Sequence Length:** 8192 tokens |
|
- **Output Dimensionality:** 768 dimensions |
|
- **Similarity Function:** Cosine Similarity |
|
- **Training Dataset:** |
|
- json |
|
- **Language:** fr |
|
- **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: ModernBertModel |
|
(1): Pooling({'word_embedding_dimension': 768, '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}) |
|
(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("Steve77/modernbert-embed-base-bible") |
|
# Run inference |
|
sentences = [ |
|
"Quelles tâches les Lévites devaient-ils accomplir dans le service de la maison de l'Éternel?", |
|
"Ils devaient prendre soin des parvis et des chambres, purifier toutes les choses saintes, s'occuper des pains de proposition, de la fleur de farine pour les offrandes, des galettes sans levain, des gâteaux cuits sur la plaque et des gâteaux frits, et de toutes les mesures de capacité et de longueur.", |
|
"Les chefs des maisons paternelles, les chefs des tribus d'Israël, les chefs de milliers et de centaines, et les intendants du roi.", |
|
] |
|
embeddings = model.encode(sentences) |
|
print(embeddings.shape) |
|
# [3, 768] |
|
|
|
# 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 |
|
|
|
* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | dim_768 | dim_512 | dim_256 | dim_128 | dim_64 | |
|
|:--------------------|:-----------|:-----------|:-----------|:-----------|:-----------| |
|
| cosine_accuracy@1 | 0.175 | 0.1716 | 0.1602 | 0.1437 | 0.1107 | |
|
| cosine_accuracy@3 | 0.2484 | 0.2402 | 0.2276 | 0.2047 | 0.1595 | |
|
| cosine_accuracy@5 | 0.2762 | 0.272 | 0.2603 | 0.2331 | 0.1862 | |
|
| cosine_accuracy@10 | 0.3203 | 0.3162 | 0.3075 | 0.2794 | 0.2272 | |
|
| cosine_precision@1 | 0.175 | 0.1716 | 0.1602 | 0.1437 | 0.1107 | |
|
| cosine_precision@3 | 0.0828 | 0.0801 | 0.0759 | 0.0682 | 0.0532 | |
|
| cosine_precision@5 | 0.0552 | 0.0544 | 0.0521 | 0.0466 | 0.0372 | |
|
| cosine_precision@10 | 0.032 | 0.0316 | 0.0308 | 0.0279 | 0.0227 | |
|
| cosine_recall@1 | 0.175 | 0.1716 | 0.1602 | 0.1437 | 0.1107 | |
|
| cosine_recall@3 | 0.2484 | 0.2402 | 0.2276 | 0.2047 | 0.1595 | |
|
| cosine_recall@5 | 0.2762 | 0.272 | 0.2603 | 0.2331 | 0.1862 | |
|
| cosine_recall@10 | 0.3203 | 0.3162 | 0.3075 | 0.2794 | 0.2272 | |
|
| cosine_ndcg@10 | 0.2443 | 0.2395 | 0.2284 | 0.2057 | 0.1633 | |
|
| cosine_ndcg@15 | 0.2525 | 0.2464 | 0.2357 | 0.2141 | 0.17 | |
|
| **cosine_ndcg@20** | **0.2574** | **0.2517** | **0.2405** | **0.2194** | **0.1746** | |
|
| cosine_mrr@10 | 0.2205 | 0.2155 | 0.2038 | 0.1829 | 0.1435 | |
|
| cosine_map@100 | 0.2276 | 0.2226 | 0.2108 | 0.1901 | 0.15 | |
|
|
|
<!-- |
|
## Bias, Risks and Limitations |
|
|
|
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
|
--> |
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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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## Training Details |
|
|
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### Training Dataset |
|
|
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#### json |
|
|
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* Dataset: json |
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* Size: 47,560 training samples |
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* Columns: <code>anchor</code> and <code>positive</code> |
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* Approximate statistics based on the first 1000 samples: |
|
| | anchor | positive | |
|
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 8 tokens</li><li>mean: 21.11 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 24.84 tokens</li><li>max: 108 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | |
|
|:------------------------------------------------------|:----------------------------------------------------| |
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| <code>Quels sont les noms des fils de Schobal?</code> | <code>Aljan, Manahath, Ébal, Schephi et Onam</code> | |
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| <code>Quels sont les noms des fils de Tsibeon?</code> | <code>Ajja et Ana</code> | |
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| <code>Qui est le fils d'Ana?</code> | <code>Dischon</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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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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], |
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"n_dims_per_step": -1 |
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} |
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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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- `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`: 4 |
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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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- `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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- `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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- `torch_empty_cache_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`: 4 |
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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`: None |
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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`: None |
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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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- `include_for_metrics`: [] |
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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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- `use_liger_kernel`: False |
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- `eval_use_gather_object`: False |
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- `average_tokens_across_devices`: False |
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- `prompts`: None |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
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|
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### Training Logs |
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| Epoch | Step | Training Loss | dim_768_cosine_ndcg@20 | dim_512_cosine_ndcg@20 | dim_256_cosine_ndcg@20 | dim_128_cosine_ndcg@20 | dim_64_cosine_ndcg@20 | |
|
|:----------:|:-------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| |
|
| 0.0538 | 10 | 12.274 | - | - | - | - | - | |
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| 0.1076 | 20 | 11.5084 | - | - | - | - | - | |
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| 0.1615 | 30 | 10.5276 | - | - | - | - | - | |
|
| 0.2153 | 40 | 9.0432 | - | - | - | - | - | |
|
| 0.2691 | 50 | 7.572 | - | - | - | - | - | |
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| 0.3229 | 60 | 7.7696 | - | - | - | - | - | |
|
| 0.3767 | 70 | 6.5673 | - | - | - | - | - | |
|
| 0.4305 | 80 | 6.6586 | - | - | - | - | - | |
|
| 0.4844 | 90 | 5.5276 | - | - | - | - | - | |
|
| 0.5382 | 100 | 5.9891 | - | - | - | - | - | |
|
| 0.5920 | 110 | 5.2983 | - | - | - | - | - | |
|
| 0.6458 | 120 | 5.6242 | - | - | - | - | - | |
|
| 0.6996 | 130 | 5.498 | - | - | - | - | - | |
|
| 0.7534 | 140 | 4.4201 | - | - | - | - | - | |
|
| 0.8073 | 150 | 4.3818 | - | - | - | - | - | |
|
| 0.8611 | 160 | 4.2175 | - | - | - | - | - | |
|
| 0.9149 | 170 | 4.2341 | - | - | - | - | - | |
|
| 0.9687 | 180 | 4.3349 | - | - | - | - | - | |
|
| 0.9956 | 185 | - | 0.2664 | 0.2607 | 0.2508 | 0.2263 | 0.1796 | |
|
| 1.0269 | 190 | 4.6803 | - | - | - | - | - | |
|
| 1.0807 | 200 | 3.877 | - | - | - | - | - | |
|
| 1.1345 | 210 | 4.0309 | - | - | - | - | - | |
|
| 1.1884 | 220 | 4.0755 | - | - | - | - | - | |
|
| 1.2422 | 230 | 3.9068 | - | - | - | - | - | |
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| 1.2960 | 240 | 4.188 | - | - | - | - | - | |
|
| 1.3498 | 250 | 4.3417 | - | - | - | - | - | |
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| 1.4036 | 260 | 4.0526 | - | - | - | - | - | |
|
| 1.4575 | 270 | 3.3933 | - | - | - | - | - | |
|
| 1.5113 | 280 | 3.8309 | - | - | - | - | - | |
|
| 1.5651 | 290 | 3.5633 | - | - | - | - | - | |
|
| 1.6189 | 300 | 3.8179 | - | - | - | - | - | |
|
| 1.6727 | 310 | 4.0671 | - | - | - | - | - | |
|
| 1.7265 | 320 | 3.3919 | - | - | - | - | - | |
|
| 1.7804 | 330 | 2.6578 | - | - | - | - | - | |
|
| 1.8342 | 340 | 2.6953 | - | - | - | - | - | |
|
| 1.8880 | 350 | 2.8858 | - | - | - | - | - | |
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| 1.9418 | 360 | 2.8933 | - | - | - | - | - | |
|
| **1.9956** | **370** | **2.9603** | **0.2775** | **0.2737** | **0.2637** | **0.2402** | **0.1916** | |
|
| 2.0538 | 380 | 3.3361 | - | - | - | - | - | |
|
| 2.1076 | 390 | 2.7904 | - | - | - | - | - | |
|
| 2.1615 | 400 | 3.0108 | - | - | - | - | - | |
|
| 2.2153 | 410 | 2.8917 | - | - | - | - | - | |
|
| 2.2691 | 420 | 3.0295 | - | - | - | - | - | |
|
| 2.3229 | 430 | 3.5609 | - | - | - | - | - | |
|
| 2.3767 | 440 | 2.7722 | - | - | - | - | - | |
|
| 2.4305 | 450 | 3.2115 | - | - | - | - | - | |
|
| 2.4844 | 460 | 2.6333 | - | - | - | - | - | |
|
| 2.5382 | 470 | 3.2503 | - | - | - | - | - | |
|
| 2.5920 | 480 | 2.7708 | - | - | - | - | - | |
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| 2.6458 | 490 | 3.167 | - | - | - | - | - | |
|
| 2.6996 | 500 | 3.1447 | - | - | - | - | - | |
|
| 2.7534 | 510 | 2.0428 | - | - | - | - | - | |
|
| 2.8073 | 520 | 2.0001 | - | - | - | - | - | |
|
| 2.8611 | 530 | 2.0826 | - | - | - | - | - | |
|
| 2.9149 | 540 | 2.0853 | - | - | - | - | - | |
|
| 2.9687 | 550 | 2.2365 | - | - | - | - | - | |
|
| 2.9956 | 555 | - | 0.2660 | 0.2604 | 0.2509 | 0.2266 | 0.1810 | |
|
| 3.0269 | 560 | 2.762 | - | - | - | - | - | |
|
| 3.0807 | 570 | 2.1219 | - | - | - | - | - | |
|
| 3.1345 | 580 | 2.2908 | - | - | - | - | - | |
|
| 3.1884 | 590 | 2.6195 | - | - | - | - | - | |
|
| 3.2422 | 600 | 2.3468 | - | - | - | - | - | |
|
| 3.2960 | 610 | 2.7504 | - | - | - | - | - | |
|
| 3.3498 | 620 | 2.9486 | - | - | - | - | - | |
|
| 3.4036 | 630 | 2.7281 | - | - | - | - | - | |
|
| 3.4575 | 640 | 2.188 | - | - | - | - | - | |
|
| 3.5113 | 650 | 2.5494 | - | - | - | - | - | |
|
| 3.5651 | 660 | 2.426 | - | - | - | - | - | |
|
| 3.6189 | 670 | 2.6478 | - | - | - | - | - | |
|
| 3.6727 | 680 | 2.9209 | - | - | - | - | - | |
|
| 3.7265 | 690 | 2.3512 | - | - | - | - | - | |
|
| 3.7804 | 700 | 1.6746 | - | - | - | - | - | |
|
| 3.8342 | 710 | 1.739 | - | - | - | - | - | |
|
| 3.8880 | 720 | 1.951 | - | - | - | - | - | |
|
| 3.9418 | 730 | 1.9886 | - | - | - | - | - | |
|
| 3.9956 | 740 | 2.1022 | 0.2574 | 0.2517 | 0.2405 | 0.2194 | 0.1746 | |
|
|
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* The bold row denotes the saved checkpoint. |
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|
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### Environmental Impact |
|
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). |
|
- **Energy Consumed**: 0.205 kWh |
|
- **Carbon Emitted**: 0.011 kg of CO2 |
|
- **Hours Used**: 6.806 hours |
|
|
|
### Training Hardware |
|
- **On Cloud**: No |
|
- **GPU Model**: 1 x NVIDIA GeForce GTX 1660 Ti |
|
- **CPU Model**: Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz |
|
- **RAM Size**: 7.68 GB |
|
|
|
### Framework Versions |
|
- Python: 3.11.11 |
|
- Sentence Transformers: 3.3.1 |
|
- Transformers: 4.48.0.dev0 |
|
- PyTorch: 2.5.1 |
|
- Accelerate: 1.2.1 |
|
- Datasets: 2.19.1 |
|
- Tokenizers: 0.21.0 |
|
|
|
## 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, |
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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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} |
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``` |
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