
# sentence-BERTino

This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. It was trained on a dataset made from question/context pairs ([squad-it](https://github.com/crux82/squad-it)) and tags/news-article pairs (via scraping). 

If you like this project, consider supporting me with a cup of coffee! 🤖✨🌞
[![Buy me a coffee](https://badgen.net/badge/icon/Buy%20Me%20A%20Coffee?icon=buymeacoffee&label)](https://bmc.link/edoardofederici)

## 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 = ["Questo è un esempio di frase", "Questo è un ulteriore esempio"]

model = SentenceTransformer('efederici/sentence-BERTino')
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 = ["Questo è un esempio di frase", "Questo è un ulteriore esempio"]

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('efederici/sentence-BERTino')
model = AutoModel.from_pretrained('efederici/sentence-BERTino')

# 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, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

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

## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel 
  (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})
)
```
{"language": ["it"], "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"}
sentence-transformers pytorch distilbert feature-extraction sentence-similarity transformers it license:apache-2.0 endpoints_compatible region:us
TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #it #license-apache-2.0 #endpoints_compatible #region-us 


# sentence-BERTino

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. It was trained on a dataset made from question/context pairs (squad-it) and tags/news-article pairs (via scraping). 

If you like this project, consider supporting me with a cup of coffee! 
![Buy me a coffee](URL

## 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.



## Full Model Architecture

# sentence-BERTino

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. It was trained on a dataset made from question/context pairs (squad-it) and tags/news-article pairs (via scraping). 

If you like this project, consider supporting me with a cup of coffee! 
![Buy me a coffee](URL ## 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. ## Full Model Architecture
TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #it #license-apache-2.0 #endpoints_compatible #region-us 
 # sentence-BERTino

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. It was trained on a dataset made from question/context pairs (squad-it) and tags/news-article pairs (via scraping). 

If you like this project, consider supporting me with a cup of coffee! 
![Buy me a coffee](URL ## 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. ## Full Model Architecture
TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #it #license-apache-2.0 #endpoints_compatible #region-us 
# sentence-BERTino

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. It was trained on a dataset made from question/context pairs (squad-it) and tags/news-article pairs (via scraping). 

If you like this project, consider supporting me with a cup of coffee! 
![Buy me a coffee](URL## 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.## Full Model Architecture