# Cross-Encoder

This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.

<p align="center">
    <img src="https://upload.wikimedia.org/wikipedia/commons/f/f6/Edouard_Vuillard%2C_1920c_-_Sunlit_Interior.jpg" width="400"> </br>
    Edouard Vuillard, Sunlit Interior
</p>

## Training Data

This model was trained on [stsb](https://huggingface.co/datasets/stsb_multi_mt/viewer/it/train). The model will predict a score between 0 and 1 how for the semantic similarity of two sentences. 

## Usage and Performance

```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('efederici/cross-encoder-umberto-stsb')
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')`.

{"language": ["it"], "tags": ["cross-encoder", "sentence-similarity", "transformers"], "datasets": ["stsb_multi_mt"], "pipeline_tag": "text-classification"}
transformers pytorch bert text-classification cross-encoder sentence-similarity it dataset:stsb_multi_mt autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #cross-encoder #sentence-similarity #it #dataset-stsb_multi_mt #autotrain_compatible #endpoints_compatible #region-us 

# Cross-Encoder

This model was trained using SentenceTransformers Cross-Encoder class.

<p align="center">
    <img src="URL width="400"> </br>
    Edouard Vuillard, Sunlit Interior
</p>

## Training Data

This model was trained on stsb. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences. 

## Usage and Performance



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

# Cross-Encoder

This model was trained using SentenceTransformers Cross-Encoder class.

<p align="center">
    <img src="URL width="400"> </br>
    Edouard Vuillard, Sunlit Interior
</p> ## Training Data

This model was trained on stsb. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences. ## Usage and Performance



The model will predict scores for the pairs '('Sentence 1', 'Sentence 2')' and '('Sentence 3', 'Sentence 4')'.
TAGS
#transformers #pytorch #bert #text-classification #cross-encoder #sentence-similarity #it #dataset-stsb_multi_mt #autotrain_compatible #endpoints_compatible #region-us 
 # Cross-Encoder

This model was trained using SentenceTransformers Cross-Encoder class.

<p align="center">
    <img src="URL width="400"> </br>
    Edouard Vuillard, Sunlit Interior
</p> ## Training Data

This model was trained on stsb. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences. ## Usage and Performance



The model will predict scores for the pairs '('Sentence 1', 'Sentence 2')' and '('Sentence 3', 'Sentence 4')'.
passage: TAGS
#transformers #pytorch #bert #text-classification #cross-encoder #sentence-similarity #it #dataset-stsb_multi_mt #autotrain_compatible #endpoints_compatible #region-us 
# Cross-Encoder

This model was trained using SentenceTransformers Cross-Encoder class.

<p align="center">
    <img src="URL width="400"> </br>
    Edouard Vuillard, Sunlit Interior
</p>## Training Data

This model was trained on stsb. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.## Usage and Performance



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