sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:530 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss en arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:BAAI/bge-large-en-v1.5 base_model:finetune:BAAI/bge-large-en-v1.5 license:apache-2.0 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: BAAI/bge-large-en-v1.5
datasets: []
language:
- en
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:530
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: If you receive a BharatPe speaker that you didn't order, please
    contact BharatPe support immediately. They will assist in resolving the issue
    and advise on the next steps.
  sentences:
  - Can I control multiple BharatPe speakers from one app?
  - What to do if the BharatPe speaker's transaction announcements are intermittently
    silent?
  - What should I do if I receive a BharatPe speaker without ordering it?
- source_sentence: Remote control capabilities depend on the model of the BharatPe
    speaker. Check if your model supports remote control through the BharatPe app
    or a connected device.
  sentences:
  - How do I update my personal details in my Bharatpe account?
  - What are the benefits of the BharatPe speaker?
  - Can I control the BharatPe speaker remotely?
- source_sentence: If the announcements are not clear, check the speaker's volume
    settings and ensure it's not placed near noisy equipment. If clarity doesn't improve,
    the speaker may need servicing.
  sentences:
  - What to do if my BharatPe speaker is not syncing with the transaction history
    in the app?
  - What should I do if the speaker is not announcing payments clearly?
  - The speaker doesn't produce any sound, what can be done?
- source_sentence: If the speaker is causing interference, try relocating it or other
    devices to reduce the interference. Ensure there's a reasonable distance between
    the speaker and other wireless equipment.
  sentences:
  - Can I use my Bharatpe device for international transactions?
  - How do I know if my BharatPe speaker is under warranty?
  - What should I do if the BharatPe speaker is causing interference with other wireless
    devices?
- source_sentence: I can understand and respond in multiple Indian regional languages.
    Feel free to communicate with me in the language you're most comfortable with.
  sentences:
  - How can I check if the BharatPe speaker is receiving a network signal?
  - Bharti, can you provide tips for effective online communication?
  - Bharti, what languages can you understand and respond to?
model-index:
- name: BGE large Chatbot Matryoshka
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.8837209302325582
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9534883720930233
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9534883720930233
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9534883720930233
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8837209302325582
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3178294573643411
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19069767441860463
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09534883720930232
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8837209302325582
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9534883720930233
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9534883720930233
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9534883720930233
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9246944071428586
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9147286821705425
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9186317558410582
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.8837209302325582
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9534883720930233
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9534883720930233
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9534883720930233
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8837209302325582
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3178294573643411
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19069767441860463
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09534883720930232
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8837209302325582
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9534883720930233
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9534883720930233
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9534883720930233
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9246944071428586
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9147286821705425
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9186317558410582
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.8837209302325582
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9302325581395349
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9534883720930233
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9534883720930233
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8837209302325582
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.31007751937984496
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19069767441860463
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09534883720930232
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8837209302325582
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9302325581395349
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9534883720930233
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9534883720930233
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9220630770785455
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9116279069767442
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9147848047984846
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.9069767441860465
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9302325581395349
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9302325581395349
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9534883720930233
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.9069767441860465
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.31007751937984496
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.18604651162790697
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09534883720930232
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.9069767441860465
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9302325581395349
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9302325581395349
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9534883720930233
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9299334172251043
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9224806201550388
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.92549351912877
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.8604651162790697
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9534883720930233
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9767441860465116
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9767441860465116
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8604651162790697
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3178294573643411
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1953488372093023
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09767441860465115
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8604651162790697
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9534883720930233
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9767441860465116
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9767441860465116
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9261271120648318
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9089147286821706
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9089147286821704
      name: Cosine Map@100
---

# BGE large Chatbot Matryoshka

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) <!-- at revision d4aa6901d3a41ba39fb536a557fa166f842b0e09 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **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': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("MANMEET75/bge-large-Chatbot-matryoshka")
# Run inference
sentences = [
    "I can understand and respond in multiple Indian regional languages. Feel free to communicate with me in the language you're most comfortable with.",
    'Bharti, what languages can you understand and respond to?',
    'Bharti, can you provide tips for effective online communication?',
]
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   | 0.8837     |
| cosine_accuracy@3   | 0.9535     |
| cosine_accuracy@5   | 0.9535     |
| cosine_accuracy@10  | 0.9535     |
| cosine_precision@1  | 0.8837     |
| cosine_precision@3  | 0.3178     |
| cosine_precision@5  | 0.1907     |
| cosine_precision@10 | 0.0953     |
| cosine_recall@1     | 0.8837     |
| cosine_recall@3     | 0.9535     |
| cosine_recall@5     | 0.9535     |
| cosine_recall@10    | 0.9535     |
| cosine_ndcg@10      | 0.9247     |
| cosine_mrr@10       | 0.9147     |
| **cosine_map@100**  | **0.9186** |

#### 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   | 0.8837     |
| cosine_accuracy@3   | 0.9535     |
| cosine_accuracy@5   | 0.9535     |
| cosine_accuracy@10  | 0.9535     |
| cosine_precision@1  | 0.8837     |
| cosine_precision@3  | 0.3178     |
| cosine_precision@5  | 0.1907     |
| cosine_precision@10 | 0.0953     |
| cosine_recall@1     | 0.8837     |
| cosine_recall@3     | 0.9535     |
| cosine_recall@5     | 0.9535     |
| cosine_recall@10    | 0.9535     |
| cosine_ndcg@10      | 0.9247     |
| cosine_mrr@10       | 0.9147     |
| **cosine_map@100**  | **0.9186** |

#### 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   | 0.8837     |
| cosine_accuracy@3   | 0.9302     |
| cosine_accuracy@5   | 0.9535     |
| cosine_accuracy@10  | 0.9535     |
| cosine_precision@1  | 0.8837     |
| cosine_precision@3  | 0.3101     |
| cosine_precision@5  | 0.1907     |
| cosine_precision@10 | 0.0953     |
| cosine_recall@1     | 0.8837     |
| cosine_recall@3     | 0.9302     |
| cosine_recall@5     | 0.9535     |
| cosine_recall@10    | 0.9535     |
| cosine_ndcg@10      | 0.9221     |
| cosine_mrr@10       | 0.9116     |
| **cosine_map@100**  | **0.9148** |

#### 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   | 0.907      |
| cosine_accuracy@3   | 0.9302     |
| cosine_accuracy@5   | 0.9302     |
| cosine_accuracy@10  | 0.9535     |
| cosine_precision@1  | 0.907      |
| cosine_precision@3  | 0.3101     |
| cosine_precision@5  | 0.186      |
| cosine_precision@10 | 0.0953     |
| cosine_recall@1     | 0.907      |
| cosine_recall@3     | 0.9302     |
| cosine_recall@5     | 0.9302     |
| cosine_recall@10    | 0.9535     |
| cosine_ndcg@10      | 0.9299     |
| cosine_mrr@10       | 0.9225     |
| **cosine_map@100**  | **0.9255** |

#### 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   | 0.8605     |
| cosine_accuracy@3   | 0.9535     |
| cosine_accuracy@5   | 0.9767     |
| cosine_accuracy@10  | 0.9767     |
| cosine_precision@1  | 0.8605     |
| cosine_precision@3  | 0.3178     |
| cosine_precision@5  | 0.1953     |
| cosine_precision@10 | 0.0977     |
| cosine_recall@1     | 0.8605     |
| cosine_recall@3     | 0.9535     |
| cosine_recall@5     | 0.9767     |
| cosine_recall@10    | 0.9767     |
| cosine_ndcg@10      | 0.9261     |
| cosine_mrr@10       | 0.9089     |
| **cosine_map@100**  | **0.9089** |

<!--
## 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: 530 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: 11 tokens</li><li>mean: 35.33 tokens</li><li>max: 99 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 17.3 tokens</li><li>max: 29 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                                                                                                                            | anchor                                                                   |
  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------|
  | <code>BharatPe Speaker comes with the following benefits: - Helps you avoid payment fraud - Lightweight & Easy installation process - Compatible with SIM & GPRS connectivity - Comes with a battery, no hassle of constant charging - Available in 10 Languages - Cashback Offers - Free replacement To Know more and place an order, tap below http://bharatpe.in/speaker.</code> | <code>What are the benefits of the BharatPe speaker?</code>              |
  | <code>BharatPe Speaker comes with the following benefits: - Helps you avoid payment fraud - Lightweight & Easy installation process - Compatible with SIM & GPRS connectivity - Comes with a battery, no hassle of constant charging - Available in 10 Languages - Cashback Offers - Free replacement To Know more and place an order, tap below http://bharatpe.in/speaker.</code> | <code>What advantages does the BharatPe speaker offer?</code>            |
  | <code>BharatPe Speaker comes with the following benefits: - Helps you avoid payment fraud - Lightweight & Easy installation process - Compatible with SIM & GPRS connectivity - Comes with a battery, no hassle of constant charging - Available in 10 Languages - Cashback Offers - Free replacement To Know more and place an order, tap below http://bharatpe.in/speaker.</code> | <code>Can you outline the benefits of using the BharatPe speaker?</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`: 10
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `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
- `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`: 10
- `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_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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch      | Step  | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
|:----------:|:-----:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.9412     | 1     | -             | 0.7980                 | 0.8251                 | 0.8141                 | 0.7124                | 0.8260                 |
| 1.8824     | 2     | -             | 0.8624                 | 0.8619                 | 0.8691                 | 0.7637                | 0.8557                 |
| 2.8235     | 3     | -             | 0.8763                 | 0.8792                 | 0.8770                 | 0.8588                | 0.8832                 |
| 3.7647     | 4     | -             | 0.9007                 | 0.9014                 | 0.9115                 | 0.8820                | 0.9130                 |
| 4.7059     | 5     | -             | 0.9014                 | 0.9146                 | 0.9186                 | 0.9053                | 0.9185                 |
| 5.6471     | 6     | -             | 0.9134                 | 0.9146                 | 0.9186                 | 0.9205                | 0.9183                 |
| **6.5882** | **7** | **-**         | **0.9255**             | **0.9146**             | **0.9186**             | **0.9089**            | **0.9185**             |
| 7.5294     | 8     | -             | 0.9255                 | 0.9147                 | 0.9186                 | 0.9089                | 0.9185                 |
| 8.4706     | 9     | -             | 0.9255                 | 0.9147                 | 0.9186                 | 0.9089                | 0.9186                 |
| 9.4118     | 10    | 2.0337        | 0.9255                 | 0.9148                 | 0.9186                 | 0.9089                | 0.9186                 |

* 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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