---
language:
- en
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated
base_model: microsoft/mpnet-base
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: 'Really? No kidding! '
sentences:
- yeah really no kidding
- At the end of the fourth century was when baked goods flourished.
- The campaigns seem to reach a new pool of contributors.
- source_sentence: A sleeping man.
sentences:
- Two men are sleeping.
- Someone is selling oranges
- the family is young
- source_sentence: a guy on a bike
sentences:
- A tall person on a bike
- A man is on a frozen lake.
- The women throw food at the kids
- source_sentence: yeah really no kidding
sentences:
- oh uh-huh well no they wouldn't would they no
- yeah i mean just when uh the they military paid for her education
- The campaigns seem to reach a new pool of contributors.
- source_sentence: He ran like an athlete.
sentences:
- ' Then he ran.'
- yeah i mean just when uh the they military paid for her education
- Similarly, OIM revised the electronic Grant Renewal Application to accommodate
new information sought by LSC and to ensure greater ease for users.
pipeline_tag: sentence-similarity
co2_eq_emissions:
emissions: 17.515467907816664
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: 0.13
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: 0.7331234146933103
name: Pearson Cosine
- type: spearman_cosine
value: 0.7435439430716654
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7389474504545281
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7473580293303098
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7356264396007131
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7436137284782617
name: Spearman Euclidean
- type: pearson_dot
value: 0.7093073700072118
name: Pearson Dot
- type: spearman_dot
value: 0.7150453113301433
name: Spearman Dot
- type: pearson_max
value: 0.7389474504545281
name: Pearson Max
- type: spearman_max
value: 0.7473580293303098
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test
type: sts-test
metrics:
- type: pearson_cosine
value: 0.6750510843835755
name: Pearson Cosine
- type: spearman_cosine
value: 0.6615639695746663
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6718085205234632
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6589482932175834
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6693170762111229
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6578210069410166
name: Spearman Euclidean
- type: pearson_dot
value: 0.6490291380804283
name: Pearson Dot
- type: spearman_dot
value: 0.6335192601696299
name: Spearman Dot
- type: pearson_max
value: 0.6750510843835755
name: Pearson Max
- type: spearman_max
value: 0.6615639695746663
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)
- **Maximum Sequence Length:** 384 tokens
- **Output Dimensionality:** 768 tokens
- **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
### 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})
)
```
## 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("sentence_transformers_model_id")
# Run inference
sentences = [
"He ran like an athlete.",
" Then he ran.",
"yeah i mean just when uh the they military paid for her education",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
```
## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-dev`
* Evaluated with [EmbeddingSimilarityEvaluator
](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.7331 |
| **spearman_cosine** | **0.7435** |
| pearson_manhattan | 0.7389 |
| spearman_manhattan | 0.7474 |
| pearson_euclidean | 0.7356 |
| spearman_euclidean | 0.7436 |
| pearson_dot | 0.7093 |
| spearman_dot | 0.715 |
| pearson_max | 0.7389 |
| spearman_max | 0.7474 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [EmbeddingSimilarityEvaluator
](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.6751 |
| **spearman_cosine** | **0.6616** |
| pearson_manhattan | 0.6718 |
| spearman_manhattan | 0.6589 |
| pearson_euclidean | 0.6693 |
| spearman_euclidean | 0.6578 |
| pearson_dot | 0.649 |
| spearman_dot | 0.6335 |
| pearson_max | 0.6751 |
| spearman_max | 0.6616 |
## 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: 10,000 training samples
* Columns: premise
, hypothesis
, and label
* Approximate statistics based on the first 1000 samples:
| | premise | hypothesis | label |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------|
| type | string | string | int |
| details |
Conceptually cream skimming has two basic dimensions - product and geography.
| Product and geography are what make cream skimming work.
| 1
|
| 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
| You lose the things to the following level if the people recall.
| 0
|
| One of our number will carry out your instructions minutely.
| A member of my team will execute your orders with immense precision.
| 0
|
* Loss: [sentence_transformers.losses.SoftmaxLoss.SoftmaxLoss
](https://sbert.net/docs/package_reference/losses.html#softmaxloss)
#### snli
* Dataset: [snli](https://huggingface.co/datasets/stanfordnlp/snli) at [cdb5c3d](https://huggingface.co/datasets/stanfordnlp/snli/tree/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b)
* Size: 10,000 training samples
* Columns: snli_premise
, hypothesis
, and label
* Approximate statistics based on the first 1000 samples:
| | snli_premise | hypothesis | label |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------|
| type | string | string | int |
| details | A person on a horse jumps over a broken down airplane.
| A person is training his horse for a competition.
| 1
|
| A person on a horse jumps over a broken down airplane.
| A person is at a diner, ordering an omelette.
| 2
|
| A person on a horse jumps over a broken down airplane.
| A person is outdoors, on a horse.
| 0
|
* Loss: [sentence_transformers.losses.SoftmaxLoss.SoftmaxLoss
](https://sbert.net/docs/package_reference/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: sentence1
, sentence2
, and label
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | A plane is taking off.
| An air plane is taking off.
| 1.0
|
| A man is playing a large flute.
| A man is playing a flute.
| 0.76
|
| A man is spreading shreded cheese on a pizza.
| A man is spreading shredded cheese on an uncooked pizza.
| 0.76
|
* Loss: [sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss
](https://sbert.net/docs/package_reference/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: premise
, hypothesis
, and label
* Approximate statistics based on the first 1000 samples:
| | premise | hypothesis | label |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------|
| type | string | string | int |
| details | The new rights are nice enough
| Everyone really likes the newest benefits
| 1
|
| This site includes a list of all award winners and a searchable database of Government Executive articles.
| The Government Executive articles housed on the website are not able to be searched.
| 2
|
| 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
| I like him for the most part, but would still enjoy seeing someone beat him.
| 0
|
* Loss: [sentence_transformers.losses.SoftmaxLoss.SoftmaxLoss
](https://sbert.net/docs/package_reference/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: snli_premise
, hypothesis
, and label
* Approximate statistics based on the first 1000 samples:
| | snli_premise | hypothesis | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------|
| type | string | string | int |
| details | Two women are embracing while holding to go packages.
| The sisters are hugging goodbye while holding to go packages after just eating lunch.
| 1
|
| Two women are embracing while holding to go packages.
| Two woman are holding packages.
| 0
|
| Two women are embracing while holding to go packages.
| The men are fighting outside a deli.
| 2
|
* Loss: [sentence_transformers.losses.SoftmaxLoss.SoftmaxLoss
](https://sbert.net/docs/package_reference/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: sentence1
, sentence2
, and label
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | A man with a hard hat is dancing.
| A man wearing a hard hat is dancing.
| 1.0
|
| A young child is riding a horse.
| A child is riding a horse.
| 0.95
|
| A man is feeding a mouse to a snake.
| The man is feeding a mouse to the snake.
| 1.0
|
* Loss: [sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss
](https://sbert.net/docs/package_reference/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- per_device_train_batch_size: 128
- per_device_eval_batch_size: 128
- learning_rate: 2e-05
- num_train_epochs: 1
- warmup_ratio: 0.1
- seed: 33
- bf16: True
#### All Hyperparameters