|
--- |
|
language: es |
|
datasets: |
|
- stsb_multi_mt |
|
tags: |
|
- sentence-similarity |
|
- sentence-transformers |
|
--- |
|
|
|
|
|
This is a test model that was fine-tuned using the Spanish datasets from [stsb_multi_mt](https://huggingface.co/datasets/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](https://huggingface.co/datasets/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](https://colab.research.google.com/drive/1ZNjDMFdy_lKhnD9BtbqzSbQ4LNz638ZA?usp=sharing) |
|
|
|
## 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](https://github.com/eduardofv/sts_eval) 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](https://huggingface.co/datasets/stsb_multi_mt) |
|
- Sentence Transformers [Semantic Textual Similarity](https://www.sbert.net/examples/training/sts/README.html) |
|
- Check [sts_eval](https://github.com/eduardofv/sts_eval) for a comparison with Tensorflow and Sentence-Transformers models |
|
- Check the [development environment to run the scripts and evaluation](https://github.com/eduardofv/ai-denv) |
|
|
