---
language:
- es
tags:
- es
- Sentence Similarity
license: "apache-2.0"
datasets:
- stsb_multi_mt(es)
metrics:
- Cosine-Similarity
- Manhattan-Distance
- Euclidean-Distance
- Dot-Product-Similarity
---
# Training
This model was built using Sentence Transformer.
## Model description
Input for the model: Any spanish text
Output for the model: encoded text
## Evaluation
```
- Cosine-Similarity :	Pearson: 0.8532 Spearman: 0.8517
- Manhattan-Distance:	Pearson: 0.8289	Spearman: 0.8333
- Euclidean-Distance:	Pearson: 0.8298	Spearman: 0.8340
- Dot-Product-Similarity:	Pearson: 0.8043	Spearman: 0.8063
```
#### How to use
Here is how to use this model to get the features of a given text in *PyTorch*:
```python
# You can include sample code which will be formatted
from sentence_transformers import SentenceTransformer
model = SentenceTransformer()
sentences = ["mi nombre es Siddhartha","¿viajas a kathmandu?"]

sentence_embeddings = model.encode(sentences)
print(sentence_embeddings)
```
## Training procedure
I trained on the dataset on the [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased).