sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:389 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss my arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:BAAI/bge-base-en-v1.5 base_model:finetune:BAAI/bge-base-en-v1.5 license:apache-2.0 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
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.*
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### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## 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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