---
language:
- en
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:557850
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: microsoft/mpnet-base
widget:
- source_sentence: A man is jumping unto his filthy bed.
  sentences:
  - A young male is looking at a newspaper while 2 females walks past him.
  - The bed is dirty.
  - The man is on the moon.
- source_sentence: A carefully balanced male stands on one foot near a clean ocean
    beach area.
  sentences:
  - A man is ouside near the beach.
  - Three policemen patrol the streets on bikes
  - A man is sitting on his couch.
- source_sentence: The man is wearing a blue shirt.
  sentences:
  - Near the trashcan the man stood and smoked
  - A man in a blue shirt leans on a wall beside a road with a blue van and red car
    with water in the background.
  - A man in a black shirt is playing a guitar.
- source_sentence: The girls are outdoors.
  sentences:
  - Two girls riding on an amusement part ride.
  - a guy laughs while doing laundry
  - Three girls are standing together in a room, one is listening, one is writing
    on a wall and the third is talking to them.
- source_sentence: A construction worker peeking out of a manhole while his coworker
    sits on the sidewalk smiling.
  sentences:
  - A worker is looking out of a manhole.
  - A man is giving a presentation.
  - The workers are both inside the manhole.
datasets:
- sentence-transformers/all-nli
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
co2_eq_emissions:
  emissions: 205.739032893975
  energy_consumed: 0.5292975927419334
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
  ram_total_size: 31.777088165283203
  hours_used: 2.452
  hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: SentenceTransformer based on microsoft/mpnet-base
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 768
      type: sts-dev-768
    metrics:
    - type: pearson_cosine
      value: 0.8427806843466507
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8508672705970183
      name: Spearman Cosine
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 512
      type: sts-dev-512
    metrics:
    - type: pearson_cosine
      value: 0.8402650019702758
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8492501196021981
      name: Spearman Cosine
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 256
      type: sts-dev-256
    metrics:
    - type: pearson_cosine
      value: 0.8346871892249894
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8462852114011874
      name: Spearman Cosine
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 128
      type: sts-dev-128
    metrics:
    - type: pearson_cosine
      value: 0.8258126981506843
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8396442287070809
      name: Spearman Cosine
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 64
      type: sts-dev-64
    metrics:
    - type: pearson_cosine
      value: 0.8133510090549183
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8314093123007742
      name: Spearman Cosine
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 768
      type: sts-test-768
    metrics:
    - type: pearson_cosine
      value: 0.8189065344720828
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8358553875433253
      name: Spearman Cosine
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 512
      type: sts-test-512
    metrics:
    - type: pearson_cosine
      value: 0.8185683063331012
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8361687236813662
      name: Spearman Cosine
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 256
      type: sts-test-256
    metrics:
    - type: pearson_cosine
      value: 0.8129602938883278
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8332021961323041
      name: Spearman Cosine
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 128
      type: sts-test-128
    metrics:
    - type: pearson_cosine
      value: 0.8030325360463209
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.826154869627039
      name: Spearman Cosine
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 64
      type: sts-test-64
    metrics:
    - type: pearson_cosine
      value: 0.7903762214352186
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8193971659006509
      name: Spearman Cosine
---

# SentenceTransformer based on microsoft/mpnet-base

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. 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:** [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
- **Language:** en
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```

## 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("tomaarsen/mpnet-base-nli-matryoshka-reproduced")
# Run inference
sentences = [
    'A construction worker peeking out of a manhole while his coworker sits on the sidewalk smiling.',
    'A worker is looking out of a manhole.',
    'The workers are both inside the manhole.',
]
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]
```

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<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>
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### Out-of-Scope Use

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## Evaluation

### Metrics

#### Semantic Similarity

* Datasets: `sts-dev-768` and `sts-test-768`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) with these parameters:
  ```json
  {
      "truncate_dim": 768
  }
  ```

| Metric              | sts-dev-768 | sts-test-768 |
|:--------------------|:------------|:-------------|
| pearson_cosine      | 0.8428      | 0.8189       |
| **spearman_cosine** | **0.8509**  | **0.8359**   |

#### Semantic Similarity

* Datasets: `sts-dev-512` and `sts-test-512`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) with these parameters:
  ```json
  {
      "truncate_dim": 512
  }
  ```

| Metric              | sts-dev-512 | sts-test-512 |
|:--------------------|:------------|:-------------|
| pearson_cosine      | 0.8403      | 0.8186       |
| **spearman_cosine** | **0.8493**  | **0.8362**   |

#### Semantic Similarity

* Datasets: `sts-dev-256` and `sts-test-256`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) with these parameters:
  ```json
  {
      "truncate_dim": 256
  }
  ```

| Metric              | sts-dev-256 | sts-test-256 |
|:--------------------|:------------|:-------------|
| pearson_cosine      | 0.8347      | 0.813        |
| **spearman_cosine** | **0.8463**  | **0.8332**   |

#### Semantic Similarity

* Datasets: `sts-dev-128` and `sts-test-128`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) with these parameters:
  ```json
  {
      "truncate_dim": 128
  }
  ```

| Metric              | sts-dev-128 | sts-test-128 |
|:--------------------|:------------|:-------------|
| pearson_cosine      | 0.8258      | 0.803        |
| **spearman_cosine** | **0.8396**  | **0.8262**   |

#### Semantic Similarity

* Datasets: `sts-dev-64` and `sts-test-64`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) with these parameters:
  ```json
  {
      "truncate_dim": 64
  }
  ```

| Metric              | sts-dev-64 | sts-test-64 |
|:--------------------|:-----------|:------------|
| pearson_cosine      | 0.8134     | 0.7904      |
| **spearman_cosine** | **0.8314** | **0.8194**  |

<!--
## Bias, Risks and Limitations

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### Recommendations

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## Training Details

### Training Dataset

#### all-nli

* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
* Size: 557,850 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: 7 tokens</li><li>mean: 10.46 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.81 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.4 tokens</li><li>max: 50 tokens</li></ul> |
* Samples:
  | anchor                                                                     | positive                                         | negative                                                   |
  |:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------|
  | <code>A person on a horse jumps over a broken down airplane.</code>        | <code>A person is outdoors, on a horse.</code>   | <code>A person is at a diner, ordering an omelette.</code> |
  | <code>Children smiling and waving at camera</code>                         | <code>There are children present</code>          | <code>The kids are frowning</code>                         |
  | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</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
  }
  ```

### Evaluation Dataset

#### all-nli

* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
* Size: 6,584 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: 6 tokens</li><li>mean: 17.95 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.78 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.35 tokens</li><li>max: 29 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                                                                         | positive                                                    | negative                                                |
  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------|
  | <code>Two women are embracing while holding to go packages.</code>                                                                                                             | <code>Two woman are holding packages.</code>                | <code>The men are fighting outside a deli.</code>       |
  | <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>Two kids in numbered jerseys wash their hands.</code> | <code>Two kids in jackets walk to school.</code>        |
  | <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code>                                                                    | <code>A man selling donuts to a customer.</code>            | <code>A woman drinks her coffee in a small cafe.</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`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates

#### 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`: 16
- `per_device_eval_batch_size`: 16
- `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`: 5e-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`: 1
- `max_steps`: -1
- `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
- `use_ipex`: 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`: 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`: False
- `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}
- `tp_size`: 0
- `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`: None
- `hub_always_push`: False
- `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`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step  | Training Loss | Validation Loss | sts-dev-768_spearman_cosine | sts-dev-512_spearman_cosine | sts-dev-256_spearman_cosine | sts-dev-128_spearman_cosine | sts-dev-64_spearman_cosine | sts-test-768_spearman_cosine | sts-test-512_spearman_cosine | sts-test-256_spearman_cosine | sts-test-128_spearman_cosine | sts-test-64_spearman_cosine |
|:------:|:-----:|:-------------:|:---------------:|:---------------------------:|:---------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|
| 0.0459 | 1600  | 4.3243        | 1.5267          | 0.8525                      | 0.8475                      | 0.8438                      | 0.8356                      | 0.8155                     | -                            | -                            | -                            | -                            | -                           |
| 0.0918 | 3200  | 2.4538        | 1.4448          | 0.8479                      | 0.8439                      | 0.8403                      | 0.8346                      | 0.8249                     | -                            | -                            | -                            | -                            | -                           |
| 0.1377 | 4800  | 2.2829        | 1.5117          | 0.8507                      | 0.8481                      | 0.8429                      | 0.8348                      | 0.8203                     | -                            | -                            | -                            | -                            | -                           |
| 0.1836 | 6400  | 2.0446        | 1.2684          | 0.8574                      | 0.8541                      | 0.8498                      | 0.8413                      | 0.8302                     | -                            | -                            | -                            | -                            | -                           |
| 0.2294 | 8000  | 1.8867        | 1.3107          | 0.8452                      | 0.8423                      | 0.8400                      | 0.8352                      | 0.8255                     | -                            | -                            | -                            | -                            | -                           |
| 0.2753 | 9600  | 1.747         | 1.1663          | 0.8456                      | 0.8420                      | 0.8384                      | 0.8292                      | 0.8229                     | -                            | -                            | -                            | -                            | -                           |
| 0.3212 | 11200 | 1.6297        | 1.0809          | 0.8420                      | 0.8388                      | 0.8360                      | 0.8294                      | 0.8205                     | -                            | -                            | -                            | -                            | -                           |
| 0.3671 | 12800 | 1.5974        | 1.0853          | 0.8374                      | 0.8352                      | 0.8310                      | 0.8264                      | 0.8184                     | -                            | -                            | -                            | -                            | -                           |
| 0.4130 | 14400 | 1.5227        | 1.0440          | 0.8479                      | 0.8457                      | 0.8434                      | 0.8380                      | 0.8266                     | -                            | -                            | -                            | -                            | -                           |
| 0.4589 | 16000 | 1.3835        | 1.0718          | 0.8365                      | 0.8341                      | 0.8310                      | 0.8258                      | 0.8172                     | -                            | -                            | -                            | -                            | -                           |
| 0.5048 | 17600 | 1.3893        | 1.0140          | 0.8384                      | 0.8363                      | 0.8339                      | 0.8275                      | 0.8178                     | -                            | -                            | -                            | -                            | -                           |
| 0.5507 | 19200 | 1.3203        | 1.0048          | 0.8418                      | 0.8400                      | 0.8364                      | 0.8292                      | 0.8204                     | -                            | -                            | -                            | -                            | -                           |
| 0.5966 | 20800 | 1.2396        | 0.9407          | 0.8458                      | 0.8439                      | 0.8404                      | 0.8353                      | 0.8274                     | -                            | -                            | -                            | -                            | -                           |
| 0.6425 | 22400 | 1.1842        | 0.9541          | 0.8435                      | 0.8404                      | 0.8384                      | 0.8335                      | 0.8257                     | -                            | -                            | -                            | -                            | -                           |
| 0.6883 | 24000 | 1.1217        | 0.9000          | 0.8534                      | 0.8512                      | 0.8478                      | 0.8408                      | 0.8297                     | -                            | -                            | -                            | -                            | -                           |
| 0.7342 | 25600 | 1.093         | 0.8731          | 0.8525                      | 0.8503                      | 0.8467                      | 0.8406                      | 0.8313                     | -                            | -                            | -                            | -                            | -                           |
| 0.7801 | 27200 | 1.0609        | 0.8238          | 0.8528                      | 0.8510                      | 0.8469                      | 0.8399                      | 0.8312                     | -                            | -                            | -                            | -                            | -                           |
| 0.8260 | 28800 | 0.9807        | 0.8264          | 0.8497                      | 0.8478                      | 0.8448                      | 0.8384                      | 0.8295                     | -                            | -                            | -                            | -                            | -                           |
| 0.8719 | 30400 | 1.0061        | 0.8135          | 0.8455                      | 0.8439                      | 0.8405                      | 0.8338                      | 0.8256                     | -                            | -                            | -                            | -                            | -                           |
| 0.9178 | 32000 | 0.9724        | 0.7965          | 0.8517                      | 0.8499                      | 0.8465                      | 0.8401                      | 0.8319                     | -                            | -                            | -                            | -                            | -                           |
| 0.9637 | 33600 | 0.9057        | 0.7841          | 0.8509                      | 0.8493                      | 0.8463                      | 0.8396                      | 0.8314                     | -                            | -                            | -                            | -                            | -                           |
| -1     | -1    | -             | -               | -                           | -                           | -                           | -                           | -                          | 0.8359                       | 0.8362                       | 0.8332                       | 0.8262                       | 0.8194                      |


### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.529 kWh
- **Carbon Emitted**: 0.206 kg of CO2
- **Hours Used**: 2.452 hours

### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB

### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 4.1.0.dev0
- Transformers: 4.51.1
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.1
- Datasets: 3.3.2
- Tokenizers: 0.21.1

## 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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