This is a test model that was fine-tuned using the Spanish datasets from stsb_multi_mt in order to understand and benchmark STS models.
Model and training data description
This model was built taking distiluse-base-multilingual-cased-v1 and training it on a Semantic Textual Similarity task using a modified version of the training script for STS from Sentece Transformers (the modified script is included in the repo). It was trained using the Spanish datasets from stsb_multi_mt which are the STSBenchmark datasets automatically translated to other languages using deepl.com. Refer to the dataset repository for more details.
Intended uses & limitations
This model was built just as a proof-of-concept on STS fine-tuning using Spanish data and no specific use other than getting a sense on how this training works.
How to use
You may use it as any other STS trained model to extract sentence embeddings. Check Sentence Transformers documentation.
Training procedure
This model was trained using this Colab Notebook
Evaluation results
Evaluating distiluse-base-multilingual-cased-v1 on the Spanish test dataset before training results in:
2021-07-06 17:44:46 - EmbeddingSimilarityEvaluator: Evaluating the model on dataset:
2021-07-06 17:45:00 - Cosine-Similarity : Pearson: 0.7662 Spearman: 0.7583
2021-07-06 17:45:00 - Manhattan-Distance: Pearson: 0.7805 Spearman: 0.7772
2021-07-06 17:45:00 - Euclidean-Distance: Pearson: 0.7816 Spearman: 0.7778
2021-07-06 17:45:00 - Dot-Product-Similarity: Pearson: 0.6610 Spearman: 0.6536
While the fine-tuned version with the defaults of the training script and the Spanish training dataset results in:
2021-07-06 17:49:22 - EmbeddingSimilarityEvaluator: Evaluating the model on stsb-multi-mt-test dataset:
2021-07-06 17:49:24 - Cosine-Similarity : Pearson: 0.8265 Spearman: 0.8207
2021-07-06 17:49:24 - Manhattan-Distance: Pearson: 0.8131 Spearman: 0.8190
2021-07-06 17:49:24 - Euclidean-Distance: Pearson: 0.8129 Spearman: 0.8190
2021-07-06 17:49:24 - Dot-Product-Similarity: Pearson: 0.7773 Spearman: 0.7692
In our STS Evaluation repository we compare the performance of this model with other models from Sentence Transformers and Tensorflow Hub using the standard STSBenchmark and the 2017 STSBenchmark Task 3 for Spanish.
Resources
- Training dataset stsb_multi_mt
- Sentence Transformers Semantic Textual Similarity
- Check sts_eval for a comparison with Tensorflow and Sentence-Transformers models
- Check the development environment to run the scripts and evaluation
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