Sold by: Instituto Ingenieria Conocimiento
Deployed on AWS
RigoBERTa 2.0 is a state-of-the-art encoder language model for Spanish, developed through language-adaptive pretraining. This model significantly improves performance on every previous Spanish encoder model offering robust language understanding.
Overview
Easily fine-tune RigoBERTa-2.0 on your own data to develop highly accurate text classification models specifically tailored to your unique use case and labeling requirements.
This targeted Natural Language Understanding (NLU) approach delivers superior performance compared to traditional Natural Language Generation (NLG) methods when applied to structured classification tasks. Once fine-tuned, your customized model is ready for seamless deployment and real-time inference, allowing for direct integration into workflows and supporting faster, more reliable decision-making across healthcare applications
RigoBERTa 2.0 was built by further pretraining the general-purpose FacebookAI/xlm-roberta-large on a meticulously curated Spanish corpus. The pretraining leverages masked language modeling (MLM) to adapt the model's linguistic knowledge to the Spanish language.
An open-weight version of this model, intended solely for research and non-commercial use, is available on the public Hugging Face profile of IIC
Highlights
- RigoBERTa 2.0 is general encoder language model developed through domain-adaptive MLM pretraining on a meticulously curated Spanish corpus.
- We recommend using this model as a foundation for general NLP applications by fine-tuning it on your own data for text classification tasks.
Details
Delivery method
Latest version
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g5.8xlarge Training Recommended | Algorithm training on the ml.g5.8xlarge instance type | $1.00 |
ml.g5.8xlarge Inference (Batch) Recommended | Model inference on the ml.g5.8xlarge instance type, batch mode | $2.00 |
ml.g5.8xlarge Inference (Real-Time) Recommended | Model inference on the ml.g5.8xlarge instance type, real-time mode | $2.00 |
ml.g4dn.8xlarge Training | Algorithm training on the ml.g4dn.8xlarge instance type | $1.00 |
ml.g5.12xlarge Training | Algorithm training on the ml.g5.12xlarge instance type | $1.00 |
ml.g5.12xlarge Inference (Batch) | Model inference on the ml.g5.12xlarge instance type, batch mode | $2.00 |
ml.g5.12xlarge Inference (Real-Time) | Model inference on the ml.g5.12xlarge instance type, real-time mode | $2.00 |
ml.g5.16xlarge Training | Algorithm training on the ml.g5.16xlarge instance type | $1.00 |
ml.g5.16xlarge Inference (Batch) | Model inference on the ml.g5.16xlarge instance type, batch mode | $2.00 |
ml.g5.16xlarge Inference (Real-Time) | Model inference on the ml.g5.16xlarge instance type, real-time mode | $2.00 |
Dimensions summary
You pay by the hour based on the AWS instance type you run and the task you perform. This model is a Spanish-language text classification tool. Pricing splits into two activities: training the model and running inference. Training runs on ml.g4dn.8xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.12xlarge, and ml.g5.16xlarge instances. Inference runs on the ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.12xlarge, and ml.g5.16xlarge instances in either batch or real-time mode. Larger instances carry more compute capacity. Your cost scales with the instance size and the hours you use.
Top-of-mind questions for buyers
You pay per host-hour, meaning one hour of one running instance. The software meters running time only. Larger instance types like ml.g5.16xlarge carry more compute per hour than smaller ones like ml.g5.4xlarge. Multiple running instances each accrue their own hourly charge in parallel.
Both bill per host-hour on the same instance types. Batch mode processes grouped data on demand, so charges accrue during each processing run. Real-time mode keeps an endpoint running to serve live requests, so charges accrue continuously while the endpoint stays active, whether or not requests arrive.
They bill independently. Training host-hours and inference host-hours each meter their own running time. If you train the model and then run inference, both appear as separate line items. You are not required to run all activities; you pay only for the instances and modes you actually run.
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Delivery details
Amazon SageMaker algorithm
An Amazon SageMaker algorithm is a machine learning model that requires your training data to make predictions. Use the included training algorithm to generate your unique model artifact. Then deploy the model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
This is the first version of our Spanish general text classification model. This initial release demonstrates our commitment to making Spanish machine learning resources. Our model has been trained on a diverse Spanish dataset to ensure robust performance and accuracy in various scenarios. While this is just the beginning, we are excited about the potential applications and improvements that future iterations will bring. We look forward to refining and enhancing our model based on user feedback and continued research.
Additional details
Inputs
- Summary
The fine-tuned classification model accepts as input a JSON object containing a list of texts.
{ "inputs": [ "Aunque al principio tenía dudas por algunas reseñas negativas, el servicio terminó sorprendiéndome para bien: el envío fue rápido, el producto funciona perfectamente y la atención al cliente resolvió mis preguntas con mucha amabilidad.", "El informe describe las características principales del sistema, menciona algunas limitaciones conocidas y resume los resultados obtenidos durante las pruebas internas.", "La aplicación promete muchas funciones interesantes, pero en la práctica se cierra constantemente, tarda demasiado en cargar y el soporte técnico no ofreció ninguna solución útil." ] }
- Input MIME type
- application/json, text/csv
https://github.com/iiconocimiento/iic-aws/blob/main/notebooks/rigoberta-2/data/input/classification_input.json
https://github.com/iiconocimiento/iic-aws/blob/main/notebooks/rigoberta-2/data/input/classification_input.json
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
inputs | A list of strings to be classified by the model. | Each text must be less than 512 tokens. | Yes |
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