|
--- |
|
base_model: intfloat/multilingual-e5-large |
|
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:198 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: Najčešći tipovi uključuju iznad/ispod 2.5, ukupno golova, i klađenje |
|
na broj golova u poluvremenima. |
|
sentences: |
|
- Koji su najčešći tipovi klađenja na golove? |
|
- Koje kladionice u Srbiji nude DNB opciju? |
|
- Šta je hendikep klađenje? |
|
- source_sentence: Facebook grupe posvećene klađenju omogućavaju korisnicima da dobijaju |
|
savete i predloge od velikih zajednica korisnika i kladioničara. |
|
sentences: |
|
- Šta je limit u klađenju? |
|
- Kako se koristi Facebook za klađenje? |
|
- Šta je cash-out opcija u uživo klađenju? |
|
- source_sentence: Najčešći tipovi uključuju klađenje na konačan ishod, broj gemova, |
|
broj setova, i klađenje uživo. |
|
sentences: |
|
- Koje su prednosti praćenja utakmica uživo? |
|
- Koji su najčešći tipovi klađenja na tenis? |
|
- Šta je e-novčanik? |
|
- source_sentence: Premijum provizija je dodatna naknada koju berze kvota mogu naplatiti |
|
igračima za specifične usluge ili dobitke. |
|
sentences: |
|
- Šta je premijum provizija? |
|
- Koje su strategije za uspešno uživo klađenje? |
|
- Kako funkcioniše klađenje na ukupan broj poena timova? |
|
- source_sentence: '''Super Jenki'' sistem uključuje pet događaja i 26 pojedinačnih |
|
opklada, takođe poznat kao kanadski sistem.' |
|
sentences: |
|
- Šta je 'Super Jenki' sistem klađenja? |
|
- Šta je procena verovatnoće? |
|
- Kako klađenje uživo funkcioniše u tenisu? |
|
model-index: |
|
- name: SentenceTransformer based on intfloat/multilingual-e5-large |
|
results: |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.8260869565217391 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.9565217391304348 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 1.0 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 1.0 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.8260869565217391 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.31884057971014484 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.20000000000000007 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.10000000000000003 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.8260869565217391 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.9565217391304348 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 1.0 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 1.0 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.9271072095125116 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.9021739130434783 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.9021739130434783 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.8695652173913043 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 1.0 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 1.0 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 1.0 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.8695652173913043 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.3333333333333332 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.20000000000000007 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.10000000000000003 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.8695652173913043 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 1.0 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 1.0 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 1.0 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.9461678046583877 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.9275362318840579 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.9275362318840579 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.8260869565217391 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 1.0 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 1.0 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 1.0 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.8260869565217391 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.3333333333333332 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.20000000000000007 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.10000000000000003 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.8260869565217391 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 1.0 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 1.0 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 1.0 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.9301212722049728 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.9057971014492753 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.9057971014492753 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.782608695652174 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.9565217391304348 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 1.0 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 1.0 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.782608695652174 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.31884057971014484 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.20000000000000007 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.10000000000000003 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.782608695652174 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.9565217391304348 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 1.0 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 1.0 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.9091552965878422 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.8782608695652173 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.8782608695652173 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.8260869565217391 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.9565217391304348 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.9565217391304348 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 1.0 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.8260869565217391 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.31884057971014484 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.19130434782608702 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.10000000000000003 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.8260869565217391 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.9565217391304348 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.9565217391304348 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 1.0 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.9164054079968976 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.8894927536231884 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.8894927536231884 |
|
name: Cosine Map@100 |
|
--- |
|
|
|
# SentenceTransformer based on intfloat/multilingual-e5-large |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) 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:** [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) <!-- at revision ab10c1a7f42e74530fe7ae5be82e6d4f11a719eb --> |
|
- **Maximum Sequence Length:** 512 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': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel |
|
(1): Pooling({'word_embedding_dimension': 1024, '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("luka023/proba") |
|
# Run inference |
|
sentences = [ |
|
"'Super Jenki' sistem uključuje pet događaja i 26 pojedinačnih opklada, takođe poznat kao kanadski sistem.", |
|
"Šta je 'Super Jenki' sistem klađenja?", |
|
'Kako klađenje uživo funkcioniše u tenisu?', |
|
] |
|
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_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 | 0.8261 | |
|
| cosine_accuracy@3 | 0.9565 | |
|
| cosine_accuracy@5 | 1.0 | |
|
| cosine_accuracy@10 | 1.0 | |
|
| cosine_precision@1 | 0.8261 | |
|
| cosine_precision@3 | 0.3188 | |
|
| cosine_precision@5 | 0.2 | |
|
| cosine_precision@10 | 0.1 | |
|
| cosine_recall@1 | 0.8261 | |
|
| cosine_recall@3 | 0.9565 | |
|
| cosine_recall@5 | 1.0 | |
|
| cosine_recall@10 | 1.0 | |
|
| cosine_ndcg@10 | 0.9271 | |
|
| cosine_mrr@10 | 0.9022 | |
|
| **cosine_map@100** | **0.9022** | |
|
|
|
#### 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 | 0.8696 | |
|
| cosine_accuracy@3 | 1.0 | |
|
| cosine_accuracy@5 | 1.0 | |
|
| cosine_accuracy@10 | 1.0 | |
|
| cosine_precision@1 | 0.8696 | |
|
| cosine_precision@3 | 0.3333 | |
|
| cosine_precision@5 | 0.2 | |
|
| cosine_precision@10 | 0.1 | |
|
| cosine_recall@1 | 0.8696 | |
|
| cosine_recall@3 | 1.0 | |
|
| cosine_recall@5 | 1.0 | |
|
| cosine_recall@10 | 1.0 | |
|
| cosine_ndcg@10 | 0.9462 | |
|
| cosine_mrr@10 | 0.9275 | |
|
| **cosine_map@100** | **0.9275** | |
|
|
|
#### 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 | 0.8261 | |
|
| cosine_accuracy@3 | 1.0 | |
|
| cosine_accuracy@5 | 1.0 | |
|
| cosine_accuracy@10 | 1.0 | |
|
| cosine_precision@1 | 0.8261 | |
|
| cosine_precision@3 | 0.3333 | |
|
| cosine_precision@5 | 0.2 | |
|
| cosine_precision@10 | 0.1 | |
|
| cosine_recall@1 | 0.8261 | |
|
| cosine_recall@3 | 1.0 | |
|
| cosine_recall@5 | 1.0 | |
|
| cosine_recall@10 | 1.0 | |
|
| cosine_ndcg@10 | 0.9301 | |
|
| cosine_mrr@10 | 0.9058 | |
|
| **cosine_map@100** | **0.9058** | |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_128` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.7826 | |
|
| cosine_accuracy@3 | 0.9565 | |
|
| cosine_accuracy@5 | 1.0 | |
|
| cosine_accuracy@10 | 1.0 | |
|
| cosine_precision@1 | 0.7826 | |
|
| cosine_precision@3 | 0.3188 | |
|
| cosine_precision@5 | 0.2 | |
|
| cosine_precision@10 | 0.1 | |
|
| cosine_recall@1 | 0.7826 | |
|
| cosine_recall@3 | 0.9565 | |
|
| cosine_recall@5 | 1.0 | |
|
| cosine_recall@10 | 1.0 | |
|
| cosine_ndcg@10 | 0.9092 | |
|
| cosine_mrr@10 | 0.8783 | |
|
| **cosine_map@100** | **0.8783** | |
|
|
|
#### 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 | 0.8261 | |
|
| cosine_accuracy@3 | 0.9565 | |
|
| cosine_accuracy@5 | 0.9565 | |
|
| cosine_accuracy@10 | 1.0 | |
|
| cosine_precision@1 | 0.8261 | |
|
| cosine_precision@3 | 0.3188 | |
|
| cosine_precision@5 | 0.1913 | |
|
| cosine_precision@10 | 0.1 | |
|
| cosine_recall@1 | 0.8261 | |
|
| cosine_recall@3 | 0.9565 | |
|
| cosine_recall@5 | 0.9565 | |
|
| cosine_recall@10 | 1.0 | |
|
| cosine_ndcg@10 | 0.9164 | |
|
| cosine_mrr@10 | 0.8895 | |
|
| **cosine_map@100** | **0.8895** | |
|
|
|
<!-- |
|
## Bias, Risks and Limitations |
|
|
|
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
|
--> |
|
|
|
<!-- |
|
### Recommendations |
|
|
|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
|
--> |
|
|
|
## Training Details |
|
|
|
### Training Dataset |
|
|
|
#### json |
|
|
|
* Dataset: json |
|
* Size: 198 training samples |
|
* Columns: <code>positive</code> and <code>anchor</code> |
|
* Approximate statistics based on the first 198 samples: |
|
| | positive | anchor | |
|
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 19 tokens</li><li>mean: 33.76 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.87 tokens</li><li>max: 21 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------| |
|
| <code>Klađenje na ukupan broj poena timova podrazumeva predviđanje da li će jedan tim postići više ili manje poena od postavljene granice, nezavisno od konačnog ishoda.</code> | <code>Kako funkcioniše klađenje na ukupan broj poena timova?</code> | |
|
| <code>Konačan ishod podrazumeva klađenje na to ko će pobediti u utakmici, pri čemu postoje tri mogućnosti: pobeda domaćina, pobeda gosta ili nerešeno.</code> | <code>Šta znači klađenje na konačan ishod?</code> | |
|
| <code>Patent opklada uključuje tri događaja sa ukupno sedam pojedinačnih opklada: tri singl, tri dubl i jedna trostruka opklada.</code> | <code>Šta je patent opklada?</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
```json |
|
{ |
|
"loss": "MultipleNegativesRankingLoss", |
|
"matryoshka_dims": [ |
|
768, |
|
512, |
|
256, |
|
128, |
|
64 |
|
], |
|
"matryoshka_weights": [ |
|
1, |
|
1, |
|
1, |
|
1, |
|
1 |
|
], |
|
"n_dims_per_step": -1 |
|
} |
|
``` |
|
|
|
### Training Hyperparameters |
|
#### Non-Default Hyperparameters |
|
|
|
- `eval_strategy`: epoch |
|
- `per_device_train_batch_size`: 32 |
|
- `per_device_eval_batch_size`: 16 |
|
- `gradient_accumulation_steps`: 16 |
|
- `learning_rate`: 2e-05 |
|
- `num_train_epochs`: 4 |
|
- `lr_scheduler_type`: cosine |
|
- `warmup_ratio`: 0.1 |
|
- `bf16`: True |
|
- `tf32`: False |
|
- `load_best_model_at_end`: True |
|
- `optim`: adamw_torch_fused |
|
- `batch_sampler`: no_duplicates |
|
|
|
#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
|
- `do_predict`: False |
|
- `eval_strategy`: epoch |
|
- `prediction_loss_only`: True |
|
- `per_device_train_batch_size`: 32 |
|
- `per_device_eval_batch_size`: 16 |
|
- `per_gpu_train_batch_size`: None |
|
- `per_gpu_eval_batch_size`: None |
|
- `gradient_accumulation_steps`: 16 |
|
- `eval_accumulation_steps`: None |
|
- `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`: 4 |
|
- `max_steps`: -1 |
|
- `lr_scheduler_type`: cosine |
|
- `lr_scheduler_kwargs`: {} |
|
- `warmup_ratio`: 0.1 |
|
- `warmup_steps`: 0 |
|
- `log_level`: passive |
|
- `log_level_replica`: warning |
|
- `log_on_each_node`: True |
|
- `logging_nan_inf_filter`: True |
|
- `save_safetensors`: True |
|
- `save_on_each_node`: False |
|
- `save_only_model`: False |
|
- `restore_callback_states_from_checkpoint`: False |
|
- `no_cuda`: False |
|
- `use_cpu`: False |
|
- `use_mps_device`: False |
|
- `seed`: 42 |
|
- `data_seed`: None |
|
- `jit_mode_eval`: False |
|
- `use_ipex`: False |
|
- `bf16`: True |
|
- `fp16`: False |
|
- `fp16_opt_level`: O1 |
|
- `half_precision_backend`: auto |
|
- `bf16_full_eval`: False |
|
- `fp16_full_eval`: False |
|
- `tf32`: False |
|
- `local_rank`: 0 |
|
- `ddp_backend`: None |
|
- `tpu_num_cores`: None |
|
- `tpu_metrics_debug`: False |
|
- `debug`: [] |
|
- `dataloader_drop_last`: False |
|
- `dataloader_num_workers`: 0 |
|
- `dataloader_prefetch_factor`: None |
|
- `past_index`: -1 |
|
- `disable_tqdm`: False |
|
- `remove_unused_columns`: True |
|
- `label_names`: None |
|
- `load_best_model_at_end`: True |
|
- `ignore_data_skip`: False |
|
- `fsdp`: [] |
|
- `fsdp_min_num_params`: 0 |
|
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
|
- `fsdp_transformer_layer_cls_to_wrap`: None |
|
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
|
- `deepspeed`: None |
|
- `label_smoothing_factor`: 0.0 |
|
- `optim`: adamw_torch_fused |
|
- `optim_args`: None |
|
- `adafactor`: False |
|
- `group_by_length`: False |
|
- `length_column_name`: length |
|
- `ddp_find_unused_parameters`: None |
|
- `ddp_bucket_cap_mb`: None |
|
- `ddp_broadcast_buffers`: False |
|
- `dataloader_pin_memory`: True |
|
- `dataloader_persistent_workers`: False |
|
- `skip_memory_metrics`: True |
|
- `use_legacy_prediction_loop`: False |
|
- `push_to_hub`: False |
|
- `resume_from_checkpoint`: None |
|
- `hub_model_id`: None |
|
- `hub_strategy`: every_save |
|
- `hub_private_repo`: False |
|
- `hub_always_push`: False |
|
- `gradient_checkpointing`: False |
|
- `gradient_checkpointing_kwargs`: None |
|
- `include_inputs_for_metrics`: False |
|
- `eval_do_concat_batches`: True |
|
- `fp16_backend`: auto |
|
- `push_to_hub_model_id`: None |
|
- `push_to_hub_organization`: None |
|
- `mp_parameters`: |
|
- `auto_find_batch_size`: False |
|
- `full_determinism`: False |
|
- `torchdynamo`: None |
|
- `ray_scope`: last |
|
- `ddp_timeout`: 1800 |
|
- `torch_compile`: False |
|
- `torch_compile_backend`: None |
|
- `torch_compile_mode`: None |
|
- `dispatch_batches`: None |
|
- `split_batches`: None |
|
- `include_tokens_per_second`: False |
|
- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
|
- `optim_target_modules`: None |
|
- `batch_eval_metrics`: False |
|
- `eval_on_start`: False |
|
- `eval_use_gather_object`: False |
|
- `batch_sampler`: no_duplicates |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | 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 | 0.6717 | 0.7663 | 0.8229 | 0.5755 | 0.8242 | |
|
| **2.0** | **2** | **0.7779** | **0.8457** | **0.8638** | **0.7833** | **0.8635** | |
|
| 3.0 | 4 | 0.8410 | 0.8732 | 0.8674 | 0.8167 | 0.8659 | |
|
| 1.0 | 1 | 0.8410 | 0.8732 | 0.8674 | 0.8167 | 0.8659 | |
|
| **2.0** | **2** | **0.8845** | **0.8732** | **0.9022** | **0.858** | **0.9022** | |
|
| 3.0 | 4 | 0.8783 | 0.9058 | 0.9275 | 0.8895 | 0.9022 | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.1.0 |
|
- Transformers: 4.44.2 |
|
- PyTorch: 2.4.0+cu121 |
|
- Accelerate: 0.33.0 |
|
- Datasets: 3.0.0 |
|
- Tokenizers: 0.19.1 |
|
|
|
## Citation |
|
|
|
### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
month = "11", |
|
year = "2019", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/1908.10084", |
|
} |
|
``` |
|
|
|
#### MatryoshkaLoss |
|
```bibtex |
|
@misc{kusupati2024matryoshka, |
|
title={Matryoshka Representation Learning}, |
|
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
|
year={2024}, |
|
eprint={2205.13147}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.LG} |
|
} |
|
``` |
|
|
|
#### MultipleNegativesRankingLoss |
|
```bibtex |
|
@misc{henderson2017efficient, |
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
year={2017}, |
|
eprint={1705.00652}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
|
|
|
<!-- |
|
## Glossary |
|
|
|
*Clearly define terms in order to be accessible across audiences.* |
|
--> |
|
|
|
<!-- |
|
## Model Card Authors |
|
|
|
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
|
--> |
|
|
|
<!-- |
|
## Model Card Contact |
|
|
|
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
|
--> |
