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---
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: 0.8056 Spearman: 0.7993
- Manhattan-Distance: Pearson: 0.7986 Spearman: 0.7953
- Euclidean-Distance: Pearson: 0.7991 Spearman: 0.7960
- Dot-Product-Similarity: Pearson: 0.7658 Spearman: 0.7542
```
#### 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).
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