
A Siamese network model trained for zero-shot and few-shot text classification.

The base model is [mpnet-base](https://huggingface.co/microsoft/mpnet-base).
It was trained on [SNLI](https://nlp.stanford.edu/projects/snli/) and [MNLI](https://cims.nyu.edu/~sbowman/multinli/).

This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space.

## Usage (Sentence-Transformers)

Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

```
pip install -U sentence-transformers
```

Then you can use the model like this:

```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```


## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

```python
from transformers import AutoTokenizer, AutoModel
import torch


#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)


# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)
```

{"language": ["en"], "tags": ["zero-shot-classification", "sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["SNLI", "MNLI"], "pipeline_tag": "sentence-similarity"}
sentence-transformers pytorch mpnet feature-extraction zero-shot-classification sentence-similarity transformers en dataset:SNLI dataset:MNLI endpoints_compatible has_space region:us
TAGS
#sentence-transformers #pytorch #mpnet #feature-extraction #zero-shot-classification #sentence-similarity #transformers #en #dataset-SNLI #dataset-MNLI #endpoints_compatible #has_space #region-us 


A Siamese network model trained for zero-shot and few-shot text classification.

The base model is mpnet-base.
It was trained on SNLI and MNLI.

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space.

## Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:



Then you can use the model like this:




## Usage (HuggingFace Transformers)
Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.



## Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:



Then you can use the model like this: ## Usage (HuggingFace Transformers)
Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
TAGS
#sentence-transformers #pytorch #mpnet #feature-extraction #zero-shot-classification #sentence-similarity #transformers #en #dataset-SNLI #dataset-MNLI #endpoints_compatible #has_space #region-us 
 ## Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:



Then you can use the model like this: ## Usage (HuggingFace Transformers)
Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
passage: TAGS
#sentence-transformers #pytorch #mpnet #feature-extraction #zero-shot-classification #sentence-similarity #transformers #en #dataset-SNLI #dataset-MNLI #endpoints_compatible #has_space #region-us 
## Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:



Then you can use the model like this:## Usage (HuggingFace Transformers)
Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.