---
language:
- en
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_with_trainer
- dataset_size:100K<n<1M
- loss:SoftmaxLoss
- loss:CosineSimilarityLoss
base_model: microsoft/mpnet-base
datasets:
- nyu-mll/multi_nli
- stanfordnlp/snli
- mteb/stsbenchmark-sts
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: A taxi SUV drives past an urban construction site, as a man walks
    down the street in the other direction.
  sentences:
  - The woman is walking down the street with high heels.
  - A man is reading documents in a binder.
  - A man is chasing an SUV that is going in the same direction as him.
- source_sentence: Young man running towards a tennis court while another is waiting
    in the other side of the net.
  sentences:
  - The person is cooking a hamburger.
  - A young man is running to grab a tennis ball.
  - A woman is dancing near a fire.
- source_sentence: An asian woman sitting outside an outdoor market stall.
  sentences:
  - There are three workers
  - A woman sits outdoors.
  - Five women sit at a table.
- source_sentence: All the same methods of analysis that are used with spoken languages
    apply successfully to signed languages.
  sentences:
  - One idea that's been going around at least since the 80s is that you can distinguish
    between Holds and Moves.
  - You only need two-dimensional trigonometry if you know the distances to the two
    stars and their angular separation.
  - A woman driving a car is talking to the man seated beside her.
- source_sentence: Rouen is the ancient center of Normandy's thriving textile industry,
    and the place of Joan of Arc's martyrdom ' a national symbol of resistance to
    tyranny.
  sentences:
  - The islands are part of France now instead of just colonies.
  - Joan of Arc sacrificed her life at Rouen, which became an enduring symbol of opposition
    to tyranny.
  - I don't know how cold it got last night.
pipeline_tag: sentence-similarity
co2_eq_emissions:
  emissions: 6.863209894681815
  energy_consumed: 0.017656739339344318
  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: 0.068
  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
      type: sts-dev
    metrics:
    - type: pearson_cosine
      value: 0.8344104750902503
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8294923795333993
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8316959259914674
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8331844817222047
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8272941934077804
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8294923795333993
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8344104825648291
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8294923795333993
      name: Spearman Dot
    - type: pearson_max
      value: 0.8344104825648291
      name: Pearson Max
    - type: spearman_max
      value: 0.8331844817222047
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test
      type: sts-test
    metrics:
    - type: pearson_cosine
      value: 0.7776062173443514
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7642518713703523
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7788269653910183
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.7659203139768728
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.7763456809736229
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.7642518713703523
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7776062158976489
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7642518713703523
      name: Spearman Dot
    - type: pearson_max
      value: 0.7788269653910183
      name: Pearson Max
    - type: spearman_max
      value: 0.7659203139768728
      name: Spearman Max
---

# 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 [multi_nli](https://huggingface.co/datasets/nyu-mll/multi_nli), [snli](https://huggingface.co/datasets/stanfordnlp/snli) and [stsb](https://huggingface.co/datasets/mteb/stsbenchmark-sts) datasets. 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:** 384 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Dot Product
- **Training Datasets:**
    - [multi_nli](https://huggingface.co/datasets/nyu-mll/multi_nli)
    - [snli](https://huggingface.co/datasets/stanfordnlp/snli)
    - [stsb](https://huggingface.co/datasets/mteb/stsbenchmark-sts)
- **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': 384, '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})
  (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("tomaarsen/mpnet-base-allnli")
# Run inference
sentences = [
    "Rouen is the ancient center of Normandy's thriving textile industry, and the place of Joan of Arc's martyrdom ' a national symbol of resistance to tyranny.",
    'Joan of Arc sacrificed her life at Rouen, which became an enduring symbol of opposition to tyranny.',
    'The islands are part of France now instead of just colonies.',
]
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>
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### 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
* Dataset: `sts-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric             | Value      |
|:-------------------|:-----------|
| pearson_cosine     | 0.8344     |
| spearman_cosine    | 0.8295     |
| pearson_manhattan  | 0.8317     |
| spearman_manhattan | 0.8332     |
| pearson_euclidean  | 0.8273     |
| spearman_euclidean | 0.8295     |
| pearson_dot        | 0.8344     |
| **spearman_dot**   | **0.8295** |
| pearson_max        | 0.8344     |
| spearman_max       | 0.8332     |

#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.7776     |
| **spearman_cosine** | **0.7643** |
| pearson_manhattan   | 0.7788     |
| spearman_manhattan  | 0.7659     |
| pearson_euclidean   | 0.7763     |
| spearman_euclidean  | 0.7643     |
| pearson_dot         | 0.7776     |
| spearman_dot        | 0.7643     |
| pearson_max         | 0.7788     |
| spearman_max        | 0.7659     |

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## Bias, Risks and Limitations

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### Recommendations

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## Training Details

### Training Datasets

#### multi_nli

* Dataset: [multi_nli](https://huggingface.co/datasets/nyu-mll/multi_nli) at [da70db2](https://huggingface.co/datasets/nyu-mll/multi_nli/tree/da70db2af9d09693783c3320c4249840212ee221)
* Size: 392,702 training samples
* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | premise                                                                            | hypothesis                                                                        | label                                                              |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            | int                                                                |
  | details | <ul><li>min: 4 tokens</li><li>mean: 26.95 tokens</li><li>max: 189 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.11 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>0: ~34.30%</li><li>1: ~28.20%</li><li>2: ~37.50%</li></ul> |
* Samples:
  | premise                                                                                                                                                                                                                                                                                              | hypothesis                                                                        | label          |
  |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------|
  | <code>Conceptually cream skimming has two basic dimensions - product and geography.</code>                                                                                                                                                                                                           | <code>Product and geography are what make cream skimming work. </code>            | <code>1</code> |
  | <code>you know during the season and i guess at at your level uh you lose them to the next level if if they decide to recall the the parent team the Braves decide to call to recall a guy from triple A then a double A guy goes up to replace him and a single A guy goes up to replace him</code> | <code>You lose the things to the following level if the people recall.</code>     | <code>0</code> |
  | <code>One of our number will carry out your instructions minutely.</code>                                                                                                                                                                                                                            | <code>A member of my team will execute your orders with immense precision.</code> | <code>0</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)

#### snli

* Dataset: [snli](https://huggingface.co/datasets/stanfordnlp/snli) at [cdb5c3d](https://huggingface.co/datasets/stanfordnlp/snli/tree/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b)
* Size: 549,367 training samples
* Columns: <code>snli_premise</code>, <code>hypothesis</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | snli_premise                                                                      | hypothesis                                                                       | label                                                              |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                           | int                                                                |
  | details | <ul><li>min: 6 tokens</li><li>mean: 17.38 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.7 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>0: ~33.40%</li><li>1: ~33.30%</li><li>2: ~33.30%</li></ul> |
* Samples:
  | snli_premise                                                        | hypothesis                                                     | label          |
  |:--------------------------------------------------------------------|:---------------------------------------------------------------|:---------------|
  | <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is training his horse for a competition.</code> | <code>1</code> |
  | <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is at a diner, ordering an omelette.</code>     | <code>2</code> |
  | <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code>                 | <code>0</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)

#### stsb

* Dataset: [stsb](https://huggingface.co/datasets/mteb/stsbenchmark-sts) at [8913289](https://huggingface.co/datasets/mteb/stsbenchmark-sts/tree/8913289635987208e6e7c72789e4be2fe94b6abd)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                        | sentence2                                                                        | label                                                          |
  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                           | string                                                                           | float                                                          |
  | details | <ul><li>min: 6 tokens</li><li>mean: 10.0 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 9.95 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                  | sentence2                                                             | label             |
  |:-----------------------------------------------------------|:----------------------------------------------------------------------|:------------------|
  | <code>A plane is taking off.</code>                        | <code>An air plane is taking off.</code>                              | <code>1.0</code>  |
  | <code>A man is playing a large flute.</code>               | <code>A man is playing a flute.</code>                                | <code>0.76</code> |
  | <code>A man is spreading shreded cheese on a pizza.</code> | <code>A man is spreading shredded cheese on an uncooked pizza.</code> | <code>0.76</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
  ```json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  ```

### Evaluation Datasets

#### multi_nli

* Dataset: [multi_nli](https://huggingface.co/datasets/nyu-mll/multi_nli) at [da70db2](https://huggingface.co/datasets/nyu-mll/multi_nli/tree/da70db2af9d09693783c3320c4249840212ee221)
* Size: 100 evaluation samples
* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | premise                                                                            | hypothesis                                                                        | label                                                              |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            | int                                                                |
  | details | <ul><li>min: 5 tokens</li><li>mean: 27.67 tokens</li><li>max: 138 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.48 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>0: ~35.00%</li><li>1: ~31.00%</li><li>2: ~34.00%</li></ul> |
* Samples:
  | premise                                                                                                                                      | hypothesis                                                                                        | label          |
  |:---------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:---------------|
  | <code>The new rights are nice enough</code>                                                                                                  | <code>Everyone really likes the newest benefits </code>                                           | <code>1</code> |
  | <code>This site includes a list of all award winners and a searchable database of Government Executive articles.</code>                      | <code>The Government Executive articles housed on the website are not able to be searched.</code> | <code>2</code> |
  | <code>uh i don't know i i have mixed emotions about him uh sometimes i like him but at the same times i love to see somebody beat him</code> | <code>I like him for the most part, but would still enjoy seeing someone beat him.</code>         | <code>0</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)

#### snli

* Dataset: [snli](https://huggingface.co/datasets/stanfordnlp/snli) at [cdb5c3d](https://huggingface.co/datasets/stanfordnlp/snli/tree/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b)
* Size: 9,842 evaluation samples
* Columns: <code>snli_premise</code>, <code>hypothesis</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | snli_premise                                                                      | hypothesis                                                                        | label                                                              |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | int                                                                |
  | details | <ul><li>min: 6 tokens</li><li>mean: 18.44 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.57 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>0: ~33.10%</li><li>1: ~33.30%</li><li>2: ~33.60%</li></ul> |
* Samples:
  | snli_premise                                                       | hypothesis                                                                                         | label          |
  |:-------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|:---------------|
  | <code>Two women are embracing while holding to go packages.</code> | <code>The sisters are hugging goodbye while holding to go packages after just eating lunch.</code> | <code>1</code> |
  | <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code>                                                       | <code>0</code> |
  | <code>Two women are embracing while holding to go packages.</code> | <code>The men are fighting outside a deli.</code>                                                  | <code>2</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)

#### stsb

* Dataset: [stsb](https://huggingface.co/datasets/mteb/stsbenchmark-sts) at [8913289](https://huggingface.co/datasets/mteb/stsbenchmark-sts/tree/8913289635987208e6e7c72789e4be2fe94b6abd)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                        | sentence2                                                                         | label                                                          |
  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                           | string                                                                            | float                                                          |
  | details | <ul><li>min: 5 tokens</li><li>mean: 15.1 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.11 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                         | sentence2                                             | label             |
  |:--------------------------------------------------|:------------------------------------------------------|:------------------|
  | <code>A man with a hard hat is dancing.</code>    | <code>A man wearing a hard hat is dancing.</code>     | <code>1.0</code>  |
  | <code>A young child is riding a horse.</code>     | <code>A child is riding a horse.</code>               | <code>0.95</code> |
  | <code>A man is feeding a mouse to a snake.</code> | <code>The man is feeding a mouse to the snake.</code> | <code>1.0</code>  |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
  ```json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `seed`: 33
- `bf16`: True
- `load_best_model_at_end`: True
- `push_to_hub`: True
- `hub_model_id`: tomaarsen/mpnet-base-allnli
- `hub_private_repo`: True
- `multi_dataset_batch_sampler`: round_robin

#### 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`: 64
- `per_device_eval_batch_size`: 64
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `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`: 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`: 33
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `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`: 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`: True
- `resume_from_checkpoint`: None
- `hub_model_id`: tomaarsen/mpnet-base-allnli
- `hub_strategy`: every_save
- `hub_private_repo`: True
- `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`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch      | Step    | Training Loss | multi nli loss | snli loss  | stsb loss  | sts-dev_spearman_dot | sts-test_spearman_cosine |
|:----------:|:-------:|:-------------:|:--------------:|:----------:|:----------:|:--------------------:|:------------------------:|
| 0.0370     | 10      | 0.8347        | -              | -          | -          | -                    | -                        |
| 0.0741     | 20      | 0.8269        | -              | -          | -          | -                    | -                        |
| 0.1111     | 30      | 0.7036        | 1.0978         | 1.0984     | 0.0830     | 0.6636               | -                        |
| 0.1481     | 40      | 0.7889        | -              | -          | -          | -                    | -                        |
| 0.1852     | 50      | 0.7948        | -              | -          | -          | -                    | -                        |
| 0.2222     | 60      | 0.688         | 1.0976         | 1.0961     | 0.0679     | 0.7124               | -                        |
| 0.2593     | 70      | 0.7911        | -              | -          | -          | -                    | -                        |
| 0.2963     | 80      | 0.7847        | -              | -          | -          | -                    | -                        |
| 0.3333     | 90      | 0.6801        | 1.0950         | 1.0942     | 0.0522     | 0.7810               | -                        |
| 0.3704     | 100     | 0.7837        | -              | -          | -          | -                    | -                        |
| 0.4074     | 110     | 0.7803        | -              | -          | -          | -                    | -                        |
| 0.4444     | 120     | 0.6756        | 1.0978         | 1.0929     | 0.0441     | 0.8157               | -                        |
| 0.4815     | 130     | 0.7829        | -              | -          | -          | -                    | -                        |
| 0.5185     | 140     | 0.7789        | -              | -          | -          | -                    | -                        |
| 0.5556     | 150     | 0.6756        | 1.0954         | 1.0911     | 0.0433     | 0.8215               | -                        |
| 0.5926     | 160     | 0.7802        | -              | -          | -          | -                    | -                        |
| 0.6296     | 170     | 0.7751        | -              | -          | -          | -                    | -                        |
| 0.6667     | 180     | 0.6679        | 1.0934         | 1.0885     | 0.0401     | 0.8235               | -                        |
| 0.7037     | 190     | 0.7755        | -              | -          | -          | -                    | -                        |
| 0.7407     | 200     | 0.775         | -              | -          | -          | -                    | -                        |
| **0.7778** | **210** | **0.6694**    | **1.0919**     | **1.0859** | **0.0377** | **0.8295**           | **-**                    |
| 0.8148     | 220     | 0.7733        | -              | -          | -          | -                    | -                        |
| 0.8519     | 230     | 0.772         | -              | -          | -          | -                    | -                        |
| 0.8889     | 240     | 0.6656        | 1.0891         | 1.0838     | 0.0365     | 0.8292               | -                        |
| 0.9259     | 250     | 0.7726        | -              | -          | -          | -                    | -                        |
| 0.9630     | 260     | 0.7731        | -              | -          | -          | -                    | -                        |
| 1.0        | 270     | 0.6674        | 1.0888         | 1.0833     | 0.0372     | 0.8295               | 0.7643                   |

* The bold row denotes the saved checkpoint.

### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.018 kWh
- **Carbon Emitted**: 0.007 kg of CO2
- **Hours Used**: 0.068 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: 3.1.0.dev0
- Transformers: 4.41.2
- PyTorch: 2.3.0+cu121
- Accelerate: 0.30.1
- Datasets: 2.19.1
- Tokenizers: 0.19.1

## Citation

### BibTeX

#### Sentence Transformers and SoftmaxLoss
```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",
}
```

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