---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:713743
- loss:MultipleNegativesRankingLoss
base_model: Alibaba-NLP/gte-modernbert-base
widget:
- source_sentence: 'Abraham Lincoln: Why is the Gettysburg Address so memorable?'
  sentences:
  - 'Abraham Lincoln: Why is the Gettysburg Address so memorable?'
  - What does the Gettysburg Address really mean?
  - What is eatalo.com?
- source_sentence: Has the influence of Ancient Carthage in science, math, and society
    been underestimated?
  sentences:
  - How does one earn money online without an investment from home?
  - Has the influence of Ancient Carthage in science, math, and society been underestimated?
  - Has the influence of the Ancient Etruscans in science and math been underestimated?
- source_sentence: Is there any app that shares charging to others like share it how
    we transfer files?
  sentences:
  - How do you think of Chinese claims that the present Private Arbitration is illegal,
    its verdict violates the UNCLOS and is illegal?
  - Is there any app that shares charging to others like share it how we transfer
    files?
  - Are there any platforms that provides end-to-end encryption for file transfer/
    sharing?
- source_sentence: Why AAP’s MLA Dinesh Mohaniya has been arrested?
  sentences:
  - What are your views on the latest sex scandal by AAP MLA Sandeep Kumar?
  - What is a dc current? What are some examples?
  - Why AAP’s MLA Dinesh Mohaniya has been arrested?
- source_sentence: What is the difference between economic growth and economic development?
  sentences:
  - How cold can the Gobi Desert get, and how do its average temperatures compare
    to the ones in the Simpson Desert?
  - the difference between economic growth and economic development is What?
  - What is the difference between economic growth and economic development?
pipeline_tag: sentence-similarity
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
model-index:
- name: SentenceTransformer based on Alibaba-NLP/gte-modernbert-base
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoMSMARCO
      type: NanoMSMARCO
    metrics:
    - type: cosine_accuracy@1
      value: 0.38
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.54
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.68
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.38
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.18
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.136
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.38
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.54
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.68
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5686686381597302
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.49702380952380953
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.5063338862610184
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoNQ
      type: NanoNQ
    metrics:
    - type: cosine_accuracy@1
      value: 0.4
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.56
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.66
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.4
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.12800000000000003
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.36
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.54
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.58
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.63
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5105228253020769
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.48852380952380947
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.4728184565167554
      name: Cosine Map@100
  - task:
      type: nano-beir
      name: Nano BEIR
    dataset:
      name: NanoBEIR mean
      type: NanoBEIR_mean
    metrics:
    - type: cosine_accuracy@1
      value: 0.39
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.55
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.64
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.73
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.39
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.19
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.132
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07500000000000001
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.37
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.54
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.63
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.7150000000000001
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5395957317309036
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4927738095238095
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.48957617138888687
      name: Cosine Map@100
---

# SentenceTransformer based on Alibaba-NLP/gte-modernbert-base

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base). 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:** [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) <!-- at revision e7f32e3c00f91d699e8c43b53106206bcc72bb22 -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/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': 128, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
  (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})
)
```

## 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("redis/model-b-structured")
# Run inference
sentences = [
    'What is the difference between economic growth and economic development?',
    'What is the difference between economic growth and economic development?',
    'the difference between economic growth and economic development is What?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  1.0000, -0.0629],
#         [ 1.0000,  1.0000, -0.0629],
#         [-0.0629, -0.0629,  1.0001]])
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

## Evaluation

### Metrics

#### Information Retrieval

* Datasets: `NanoMSMARCO` and `NanoNQ`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | NanoMSMARCO | NanoNQ     |
|:--------------------|:------------|:-----------|
| cosine_accuracy@1   | 0.38        | 0.4        |
| cosine_accuracy@3   | 0.54        | 0.56       |
| cosine_accuracy@5   | 0.68        | 0.6        |
| cosine_accuracy@10  | 0.8         | 0.66       |
| cosine_precision@1  | 0.38        | 0.4        |
| cosine_precision@3  | 0.18        | 0.2        |
| cosine_precision@5  | 0.136       | 0.128      |
| cosine_precision@10 | 0.08        | 0.07       |
| cosine_recall@1     | 0.38        | 0.36       |
| cosine_recall@3     | 0.54        | 0.54       |
| cosine_recall@5     | 0.68        | 0.58       |
| cosine_recall@10    | 0.8         | 0.63       |
| **cosine_ndcg@10**  | **0.5687**  | **0.5105** |
| cosine_mrr@10       | 0.497       | 0.4885     |
| cosine_map@100      | 0.5063      | 0.4728     |

#### Nano BEIR

* Dataset: `NanoBEIR_mean`
* Evaluated with [<code>NanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator) with these parameters:
  ```json
  {
      "dataset_names": [
          "msmarco",
          "nq"
      ],
      "dataset_id": "lightonai/NanoBEIR-en"
  }
  ```

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.39       |
| cosine_accuracy@3   | 0.55       |
| cosine_accuracy@5   | 0.64       |
| cosine_accuracy@10  | 0.73       |
| cosine_precision@1  | 0.39       |
| cosine_precision@3  | 0.19       |
| cosine_precision@5  | 0.132      |
| cosine_precision@10 | 0.075      |
| cosine_recall@1     | 0.37       |
| cosine_recall@3     | 0.54       |
| cosine_recall@5     | 0.63       |
| cosine_recall@10    | 0.715      |
| **cosine_ndcg@10**  | **0.5396** |
| cosine_mrr@10       | 0.4928     |
| cosine_map@100      | 0.4896     |

<!--
## 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: 713,743 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 6 tokens</li><li>mean: 15.96 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.93 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.72 tokens</li><li>max: 59 tokens</li></ul> |
* Samples:
  | anchor                                                                         | positive                                                                              | negative                                                                                                          |
  |:-------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------|
  | <code>Which one is better Linux OS? Ubuntu or Mint?</code>                     | <code>Why do you use Linux Mint?</code>                                               | <code>Which one is not better Linux OS ? Ubuntu or Mint ?</code>                                                  |
  | <code>What is flow?</code>                                                     | <code>What is flow?</code>                                                            | <code>What are flow lines?</code>                                                                                 |
  | <code>How is Trump planning to get Mexico to pay for his supposed wall?</code> | <code>How is it possible for Donald Trump to force Mexico to pay for the wall?</code> | <code>Why do we connect the positive terminal before the negative terminal to ground in a vehicle battery?</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 7.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset

* Size: 40,000 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 7 tokens</li><li>mean: 15.47 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.48 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.76 tokens</li><li>max: 67 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                                                       | positive                                                                                                                                                     | negative                                                                                                                                                            |
  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Why are all my questions on Quora marked needing improvement?</code>                                                                                   | <code>Why are all my questions immediately being marked as needing improvement?</code>                                                                       | <code>For a post-graduate student in IIT, is it allowed to take an external scholarship as a top-up to his/her MHRD assistantship?</code>                           |
  | <code>Can blue butter fly needle with vaccum tube be reused? Is it HIV risk? . Heard the needle is too small to be reused . Had blood draw at clinic?</code> | <code>Can blue butter fly needle with vaccum tube be reused? Is it HIV risk? . Heard the needle is too small to be reused . Had blood draw at clinic?</code> | <code>Can blue butter fly needle with vaccum tube be reused not ? Is it HIV risk ? . Heard the needle is too small to be reused . Had blood draw at clinic ?</code> |
  | <code>Why do people still believe the world is flat?</code>                                                                                                  | <code>Why are there still people who believe the world is flat?</code>                                                                                       | <code>I'm not able to buy Udemy course .it is not accepting mine and my friends debit card.my card can be used for Flipkart .how to purchase now?</code>            |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 7.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `learning_rate`: 2e-05
- `weight_decay`: 0.0001
- `max_steps`: 5000
- `warmup_ratio`: 0.1
- `fp16`: True
- `dataloader_drop_last`: True
- `dataloader_num_workers`: 1
- `dataloader_prefetch_factor`: 1
- `load_best_model_at_end`: True
- `optim`: adamw_torch
- `ddp_find_unused_parameters`: False
- `push_to_hub`: True
- `hub_model_id`: redis/model-b-structured
- `eval_on_start`: True

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0001
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3.0
- `max_steps`: 5000
- `lr_scheduler_type`: linear
- `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
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: True
- `dataloader_num_workers`: 1
- `dataloader_prefetch_factor`: 1
- `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}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `project`: huggingface
- `trackio_space_id`: trackio
- `ddp_find_unused_parameters`: False
- `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`: True
- `resume_from_checkpoint`: None
- `hub_model_id`: redis/model-b-structured
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: no
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: True
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: True
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}

</details>

### Training Logs
| Epoch  | Step | Training Loss | Validation Loss | NanoMSMARCO_cosine_ndcg@10 | NanoNQ_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 |
|:------:|:----:|:-------------:|:---------------:|:--------------------------:|:---------------------:|:----------------------------:|
| 0      | 0    | -             | 2.2389          | 0.6530                     | 0.6552                | 0.6541                       |
| 0.0448 | 250  | 1.0022        | 0.4154          | 0.6615                     | 0.5429                | 0.6022                       |
| 0.0897 | 500  | 0.3871        | 0.3658          | 0.6042                     | 0.4458                | 0.5250                       |
| 0.1345 | 750  | 0.3575        | 0.3479          | 0.5819                     | 0.5160                | 0.5489                       |
| 0.1793 | 1000 | 0.3454        | 0.3355          | 0.5976                     | 0.5595                | 0.5785                       |
| 0.2242 | 1250 | 0.337         | 0.3284          | 0.5901                     | 0.4544                | 0.5223                       |
| 0.2690 | 1500 | 0.3291        | 0.3235          | 0.6138                     | 0.5729                | 0.5933                       |
| 0.3138 | 1750 | 0.323         | 0.3182          | 0.6210                     | 0.5608                | 0.5909                       |
| 0.3587 | 2000 | 0.3206        | 0.3141          | 0.6139                     | 0.5474                | 0.5807                       |
| 0.4035 | 2250 | 0.3151        | 0.3120          | 0.6275                     | 0.5665                | 0.5970                       |
| 0.4484 | 2500 | 0.3132        | 0.3093          | 0.6059                     | 0.5349                | 0.5704                       |
| 0.4932 | 2750 | 0.3087        | 0.3072          | 0.6011                     | 0.5305                | 0.5658                       |
| 0.5380 | 3000 | 0.3065        | 0.3051          | 0.5816                     | 0.5057                | 0.5436                       |
| 0.5829 | 3250 | 0.3044        | 0.3033          | 0.5959                     | 0.5203                | 0.5581                       |
| 0.6277 | 3500 | 0.3053        | 0.3018          | 0.5817                     | 0.5185                | 0.5501                       |
| 0.6725 | 3750 | 0.3028        | 0.3006          | 0.5744                     | 0.5052                | 0.5398                       |
| 0.7174 | 4000 | 0.3018        | 0.2996          | 0.5783                     | 0.5190                | 0.5487                       |
| 0.7622 | 4250 | 0.3011        | 0.2994          | 0.5679                     | 0.4959                | 0.5319                       |
| 0.8070 | 4500 | 0.3009        | 0.2979          | 0.5689                     | 0.5068                | 0.5378                       |
| 0.8519 | 4750 | 0.2985        | 0.2975          | 0.5687                     | 0.5135                | 0.5411                       |
| 0.8967 | 5000 | 0.2995        | 0.2971          | 0.5687                     | 0.5105                | 0.5396                       |


### Framework Versions
- Python: 3.10.18
- Sentence Transformers: 5.2.0
- Transformers: 4.57.3
- PyTorch: 2.9.1+cu128
- Accelerate: 1.12.0
- Datasets: 2.21.0
- Tokenizers: 0.22.1

## 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",
}
```

#### 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.*
-->