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--- |
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language: |
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- es |
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tags: |
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- es |
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- Sentence Similarity |
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license: "apache-2.0" |
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datasets: |
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- stsb_multi_mt(es) |
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metrics: |
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- Cosine-Similarity |
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- Manhattan-Distance |
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- Euclidean-Distance |
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- Dot-Product-Similarity |
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--- |
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# Training |
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This model was built using Sentence Transformer. |
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## Model description |
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Input for the model: Any spanish text |
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Output for the model: encoded text |
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## Evaluation |
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``` |
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- Cosine-Similarity : Pearson: 0.8056 Spearman: 0.7993 |
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- Manhattan-Distance: Pearson: 0.7986 Spearman: 0.7953 |
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- Euclidean-Distance: Pearson: 0.7991 Spearman: 0.7960 |
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- Dot-Product-Similarity: Pearson: 0.7658 Spearman: 0.7542 |
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``` |
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#### How to use |
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Here is how to use this model to get the features of a given text in *PyTorch*: |
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```python |
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# You can include sample code which will be formatted |
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from sentence_transformers import SentenceTransformer |
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model = SentenceTransformer() |
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sentences = ["mi nombre es Siddhartha","¿viajas a kathmandu?"] |
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sentence_embeddings = model.encode(sentences) |
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print(sentence_embeddings) |
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``` |
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## Training procedure |
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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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