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---
base_model: BAAI/bge-base-en-v1.5
datasets: []
language:
- my
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_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:389
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Tukang kayu adalah individu yang bekerja dengan kayu untuk membina
atau membaiki struktur dan perabot.
sentences:
- Apakah itu pakar latihan?
- Apakah itu tukang kayu?
- Apakah itu pakar mikrobiologi?
- source_sentence: Pakar pemakanan adalah profesional yang memberi nasihat mengenai
pemakanan dan diet untuk meningkatkan kesihatan.
sentences:
- Apakah itu penulis kreatif?
- Apakah itu ahli geologi marin?
- Apakah itu pakar pemakanan?
- source_sentence: Dokter adalah profesional medis yang mendiagnosis dan merawat penyakit
serta cedera pasien.
sentences:
- Apa itu dokter?
- Apakah itu pengurus kargo?
- Apakah itu pakar teknologi nano?
- source_sentence: Juruteknik pembinaan kapal adalah individu yang terlibat dalam
proses pembinaan dan pembaikan kapal, memastikan struktur dan sistem kapal dibina
mengikut spesifikasi.
sentences:
- Apakah itu juruteknik pembinaan kapal?
- Apakah itu pengurus projek IT?
- Apakah itu pakar perkapalan?
- source_sentence: Penyelaras kempen iklan adalah individu yang menyelaraskan semua
aspek kempen iklan, termasuk jadual, pelaksanaan, dan laporan prestasi.
sentences:
- Apakah itu jurutera sistem propulsi?
- Apakah itu pembuat roti?
- Apakah itu penyelaras kempen iklan?
model-index:
- name: BGE base Financial Matryoshka
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.8226221079691517
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9768637532133676
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.987146529562982
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9974293059125964
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.8226221079691517
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.32562125107112255
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1974293059125964
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09974293059125963
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.8226221079691517
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9768637532133676
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.987146529562982
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9974293059125964
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9255252859780915
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9009670706328802
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9011023703216912
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.8046272493573264
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.974293059125964
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.987146529562982
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9922879177377892
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.8046272493573264
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.324764353041988
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1974293059125964
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0992287917737789
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.8046272493573264
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.974293059125964
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.987146529562982
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9922879177377892
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9158947182791948
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8895519647447668
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8900397092700132
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.7892030848329049
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9665809768637532
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.974293059125964
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.987146529562982
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7892030848329049
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3221936589545844
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19485861182519276
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0987146529562982
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7892030848329049
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9665809768637532
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.974293059125964
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.987146529562982
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9046037741833534
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8764455053658137
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8770676096874822
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.7480719794344473
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9408740359897172
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9537275064267352
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9691516709511568
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7480719794344473
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.31362467866323906
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.190745501285347
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09691516709511568
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7480719794344473
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9408740359897172
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9537275064267352
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9691516709511568
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8765083941585068
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8449820459460564
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8461326502118156
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.7223650385604113
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.897172236503856
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9254498714652957
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9434447300771208
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7223650385604113
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.29905741216795206
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.18508997429305912
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09434447300771207
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7223650385604113
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.897172236503856
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9254498714652957
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9434447300771208
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8455216956566762
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8126851511812953
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8145628077638951
name: Cosine Map@100
---
# BGE base Financial Matryoshka
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). 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:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** my
- **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': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, '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("IlhamEbdesk/bge-base-financial-matryoshka_test_my")
# Run inference
sentences = [
'Penyelaras kempen iklan adalah individu yang menyelaraskan semua aspek kempen iklan, termasuk jadual, pelaksanaan, dan laporan prestasi.',
'Apakah itu penyelaras kempen iklan?',
'Apakah itu pembuat roti?',
]
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
* 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 | 0.8226 |
| cosine_accuracy@3 | 0.9769 |
| cosine_accuracy@5 | 0.9871 |
| cosine_accuracy@10 | 0.9974 |
| cosine_precision@1 | 0.8226 |
| cosine_precision@3 | 0.3256 |
| cosine_precision@5 | 0.1974 |
| cosine_precision@10 | 0.0997 |
| cosine_recall@1 | 0.8226 |
| cosine_recall@3 | 0.9769 |
| cosine_recall@5 | 0.9871 |
| cosine_recall@10 | 0.9974 |
| cosine_ndcg@10 | 0.9255 |
| cosine_mrr@10 | 0.901 |
| **cosine_map@100** | **0.9011** |
#### 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 | 0.8046 |
| cosine_accuracy@3 | 0.9743 |
| cosine_accuracy@5 | 0.9871 |
| cosine_accuracy@10 | 0.9923 |
| cosine_precision@1 | 0.8046 |
| cosine_precision@3 | 0.3248 |
| cosine_precision@5 | 0.1974 |
| cosine_precision@10 | 0.0992 |
| cosine_recall@1 | 0.8046 |
| cosine_recall@3 | 0.9743 |
| cosine_recall@5 | 0.9871 |
| cosine_recall@10 | 0.9923 |
| cosine_ndcg@10 | 0.9159 |
| cosine_mrr@10 | 0.8896 |
| **cosine_map@100** | **0.89** |
#### 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 | 0.7892 |
| cosine_accuracy@3 | 0.9666 |
| cosine_accuracy@5 | 0.9743 |
| cosine_accuracy@10 | 0.9871 |
| cosine_precision@1 | 0.7892 |
| cosine_precision@3 | 0.3222 |
| cosine_precision@5 | 0.1949 |
| cosine_precision@10 | 0.0987 |
| cosine_recall@1 | 0.7892 |
| cosine_recall@3 | 0.9666 |
| cosine_recall@5 | 0.9743 |
| cosine_recall@10 | 0.9871 |
| cosine_ndcg@10 | 0.9046 |
| cosine_mrr@10 | 0.8764 |
| **cosine_map@100** | **0.8771** |
#### 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 | 0.7481 |
| cosine_accuracy@3 | 0.9409 |
| cosine_accuracy@5 | 0.9537 |
| cosine_accuracy@10 | 0.9692 |
| cosine_precision@1 | 0.7481 |
| cosine_precision@3 | 0.3136 |
| cosine_precision@5 | 0.1907 |
| cosine_precision@10 | 0.0969 |
| cosine_recall@1 | 0.7481 |
| cosine_recall@3 | 0.9409 |
| cosine_recall@5 | 0.9537 |
| cosine_recall@10 | 0.9692 |
| cosine_ndcg@10 | 0.8765 |
| cosine_mrr@10 | 0.845 |
| **cosine_map@100** | **0.8461** |
#### 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 | 0.7224 |
| cosine_accuracy@3 | 0.8972 |
| cosine_accuracy@5 | 0.9254 |
| cosine_accuracy@10 | 0.9434 |
| cosine_precision@1 | 0.7224 |
| cosine_precision@3 | 0.2991 |
| cosine_precision@5 | 0.1851 |
| cosine_precision@10 | 0.0943 |
| cosine_recall@1 | 0.7224 |
| cosine_recall@3 | 0.8972 |
| cosine_recall@5 | 0.9254 |
| cosine_recall@10 | 0.9434 |
| cosine_ndcg@10 | 0.8455 |
| cosine_mrr@10 | 0.8127 |
| **cosine_map@100** | **0.8146** |
<!--
## 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
#### Unnamed Dataset
* Size: 389 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
| | positive | anchor |
|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 27 tokens</li><li>mean: 61.59 tokens</li><li>max: 139 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 15.26 tokens</li><li>max: 24 tokens</li></ul> |
* Samples:
| positive | anchor |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------|
| <code>Dokter adalah profesional medis yang mendiagnosis dan merawat penyakit serta cedera pasien.</code> | <code>Apa itu dokter?</code> |
| <code>Pereka sistem akuakultur adalah individu yang merancang dan membangunkan sistem untuk membiakkan ikan secara berkesan, termasuk reka bentuk kolam, sistem aliran air, dan pemantauan kualiti air.</code> | <code>Apakah itu pereka sistem akuakultur?</code> |
| <code>Ahli sejarah seni adalah individu yang mengkaji perkembangan seni sepanjang sejarah dan konteks sosial, politik, dan budaya yang mempengaruhi penciptaannya. Mereka bekerja di muzium, galeri, dan institusi akademik, menganalisis karya seni</code> | <code>Apakah itu ahli sejarah seni?</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
- `tf32`: False
- `load_best_model_at_end`: True
- `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
- `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`: False
- `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
- `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
- `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 | 0.6375 | 0.7065 | 0.7339 | 0.5984 | 0.7483 |
| 2.0 | 3 | 0.8282 | 0.8712 | 0.8821 | 0.7994 | 0.8929 |
| **2.4615** | **4** | **0.8461** | **0.8771** | **0.89** | **0.8146** | **0.9011** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.32.1
- Datasets: 2.19.1
- 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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