# distilbert-base-uncased trained for Semantic Textual Similarity in Catalan

This is a test model that was fine-tuned using the Catalan traduction of 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 `distilbert-base-uncased` 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. and salt.gva.es. 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 Catalan 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

Use the included script to train in Catalan the base model. You can also try to train another model passing it's reference as first argument. You can also train in some other language of those included in the training dataset.

## Evaluation results

Evaluating `distilbert-base-uncased` on the Catalan test dataset before training results in:

```
Cosine-Similarity :	Pearson: 0.3180	Spearman: 0.4014
```

While the fine-tuned version with the defaults of the training script and the Catalan training dataset results in:

```
Cosine-Similarity :	Pearson: 0.7368	Spearman: 0.7288
```

## 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)
{"language": "ca", "tags": ["sentence-similarity", "sentence-transformers"], "datasets": ["stsb_multi_mt"]}
sentence-transformers sentence-similarity ca dataset:stsb_multi_mt endpoints_compatible region:us
TAGS
#sentence-transformers #sentence-similarity #ca #dataset-stsb_multi_mt #endpoints_compatible #region-us 

# distilbert-base-uncased trained for Semantic Textual Similarity in Catalan

This is a test model that was fine-tuned using the Catalan traduction of Spanish datasets from stsb_multi_mt in order to understand and benchmark STS models.

## Model and training data description

This model was built taking 'distilbert-base-uncased' 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 URL. and URL. 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 Catalan 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

Use the included script to train in Catalan the base model. You can also try to train another model passing it's reference as first argument. You can also train in some other language of those included in the training dataset.

## Evaluation results

Evaluating 'distilbert-base-uncased' on the Catalan test dataset before training results in:



While the fine-tuned version with the defaults of the training script and the Catalan training dataset results in:



## 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
# distilbert-base-uncased trained for Semantic Textual Similarity in Catalan

This is a test model that was fine-tuned using the Catalan traduction of Spanish datasets from stsb_multi_mt in order to understand and benchmark STS models. ## Model and training data description

This model was built taking 'distilbert-base-uncased' 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 URL. and URL. 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 Catalan 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

Use the included script to train in Catalan the base model. You can also try to train another model passing it's reference as first argument. You can also train in some other language of those included in the training dataset. ## Evaluation results

Evaluating 'distilbert-base-uncased' on the Catalan test dataset before training results in:



While the fine-tuned version with the defaults of the training script and the Catalan training dataset results in: ## 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
TAGS
#sentence-transformers #sentence-similarity #ca #dataset-stsb_multi_mt #endpoints_compatible #region-us 
 # distilbert-base-uncased trained for Semantic Textual Similarity in Catalan

This is a test model that was fine-tuned using the Catalan traduction of Spanish datasets from stsb_multi_mt in order to understand and benchmark STS models. ## Model and training data description

This model was built taking 'distilbert-base-uncased' 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 URL. and URL. 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 Catalan 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

Use the included script to train in Catalan the base model. You can also try to train another model passing it's reference as first argument. You can also train in some other language of those included in the training dataset. ## Evaluation results

Evaluating 'distilbert-base-uncased' on the Catalan test dataset before training results in:



While the fine-tuned version with the defaults of the training script and the Catalan training dataset results in: ## 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
passage: TAGS
#sentence-transformers #sentence-similarity #ca #dataset-stsb_multi_mt #endpoints_compatible #region-us 
# distilbert-base-uncased trained for Semantic Textual Similarity in Catalan

This is a test model that was fine-tuned using the Catalan traduction of Spanish datasets from stsb_multi_mt in order to understand and benchmark STS models.## Model and training data description

This model was built taking 'distilbert-base-uncased' 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 URL. and URL. 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 Catalan 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

Use the included script to train in Catalan the base model. You can also try to train another model passing it's reference as first argument. You can also train in some other language of those included in the training dataset.## Evaluation results

Evaluating 'distilbert-base-uncased' on the Catalan test dataset before training results in:



While the fine-tuned version with the defaults of the training script and the Catalan training dataset results in:## 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