
# Cross-Encoder for STSB-Multi
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
The original model is [dbmdz/bert-base-italian-uncased](https://huggingface.co/dbmdz/bert-base-italian-uncased).

## Training Data
This model was trained on the [STS benchmark dataset](http://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark), in particular the italian translation. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences. 


## Usage and Performance

Pre-trained models can be used like this:

```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name')
scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')])
```

The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`.

You can use this model also without sentence_transformers and by just using Transformers ``AutoModel`` class
{"language": ["it"], "license": "mit", "library_name": "sentence-transformers", "tags": ["cross-encoder"], "datasets": ["stsb_multi_mt"], "pipeline_tag": "text-classification"}
sentence-transformers pytorch bert cross-encoder text-classification it dataset:stsb_multi_mt license:mit region:us
TAGS
#sentence-transformers #pytorch #bert #cross-encoder #text-classification #it #dataset-stsb_multi_mt #license-mit #region-us 


# Cross-Encoder for STSB-Multi
This model was trained using SentenceTransformers Cross-Encoder class.
The original model is dbmdz/bert-base-italian-uncased.

## Training Data
This model was trained on the STS benchmark dataset, in particular the italian translation. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences. 


## Usage and Performance

Pre-trained models can be used like this:



The model will predict scores for the pairs '('Sentence 1', 'Sentence 2')' and '('Sentence 3', 'Sentence 4')'.

You can use this model also without sentence_transformers and by just using Transformers ''AutoModel'' class
# Cross-Encoder for STSB-Multi
This model was trained using SentenceTransformers Cross-Encoder class.
The original model is dbmdz/bert-base-italian-uncased. ## Training Data
This model was trained on the STS benchmark dataset, in particular the italian translation. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences. ## Usage and Performance

Pre-trained models can be used like this:



The model will predict scores for the pairs '('Sentence 1', 'Sentence 2')' and '('Sentence 3', 'Sentence 4')'.

You can use this model also without sentence_transformers and by just using Transformers ''AutoModel'' class
TAGS
#sentence-transformers #pytorch #bert #cross-encoder #text-classification #it #dataset-stsb_multi_mt #license-mit #region-us 
 # Cross-Encoder for STSB-Multi
This model was trained using SentenceTransformers Cross-Encoder class.
The original model is dbmdz/bert-base-italian-uncased. ## Training Data
This model was trained on the STS benchmark dataset, in particular the italian translation. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences. ## Usage and Performance

Pre-trained models can be used like this:



The model will predict scores for the pairs '('Sentence 1', 'Sentence 2')' and '('Sentence 3', 'Sentence 4')'.

You can use this model also without sentence_transformers and by just using Transformers ''AutoModel'' class