---
base_model: microsoft/mpnet-base
datasets:
- sentence-transformers/all-nli
language:
- en
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:942
- loss:CoSENTLoss
widget:
- source_sentence: The entire city was surrounded by open countryside with a scattering
of small villages.
sentences:
- Let's leave it.
- It was proven that Mrs. Vandemeyer and the girl were hiding something.
- There is only one large village in the countryside.
- source_sentence: or just get out and walk uh or even jog a little although i don't
do that regularly but Washington's a great place to do that
sentences:
- '"Washington''s is a great place for a walk or a jog."'
- A man has some bananas.
- The sidewalk was deserted.
- source_sentence: A woman walks by a brick building that's covered with graffiti.
sentences:
- When I was in high school, my favorite author was Virginia Wolf.
- A woman is outside.
- A man in a photo booth at a carnival.
- source_sentence: A woman swinging a tennis racket on an outdoor court.
sentences:
- A woman walking on an old bridge near a mountain.
- A woman is playing basketball at the park.
- Yanomamo eats food.
- source_sentence: Several people with parachutes are overlooking a beautiful view
of fields and hills.
sentences:
- Your little girl wrote about how well your farewell activity went.
- The Crosethe Rue De Rivoli was built for Cardinal Richelieu to live in.
- Several people mow the grass.
---
# 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 [sentence-transformers/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)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
- **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': 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("amorfati/custom-emb-model")
# Run inference
sentences = [
'Several people with parachutes are overlooking a beautiful view of fields and hills.',
'Several people mow the grass.',
'Your little girl wrote about how well your farewell activity went.',
]
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]
```
## Training Details
### Training Dataset
#### sentence-transformers/all-nli
* Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
* Size: 942 training samples
* Columns: sentence1
, sentence2
, and score
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details |
somehow, somewhere.
| Someplace, in some way.
| 1.0
|
| A boy is sitting on a boat with two flags.
| A blonde person sitting.
| 0.5
|
| A asian male suit clad, uses a umbrella to shield himself from the rain.
| He is late for a meeting.
| 0.5
|
* Loss: [CoSENTLoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Evaluation Dataset
#### sentence-transformers/all-nli
* Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
* Size: 120 evaluation samples
* Columns: sentence1
, sentence2
, and score
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | When we leave the house we shall be followed again, but not molested, FOR IT IS Mr. BROWN'S PLAN THAT WE ARE TO LEAD HIM.
| Mr. Brown has made a plan for us to lead him.
| 1.0
|
| She hates me."
| She loves me.
| 0.0
|
| That, too, was locked or bolted on the inside.
| She didn't want anyone to enter the room.
| 0.5
|
* Loss: [CoSENTLoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
#### All Hyperparameters