Sold by: Instituto Ingenieria ConocimientoÂ
Deployed on AWS
ERAS is a Spanish text analyzer that evaluates readability identifying areas for improvement in linguistic features.
Overview
ERAS is an easy reading analyzer for Spanish texts that serves to evaluate the degree of complexity a given text has in terms of readability. It uses NLP advanced analysis to automatically spot linguistic aspects to be improved for the sake of information accessibility.
ERAS can be used as an easy reading text writing assistant, reducing easy reading adaptation time and as an easy reading texts quality evaluator.
ERAS is specifically designed to facilitate information access for people with reading or comprehension difficulties. If you are interested in clear communication form a general perspective, have a look at LUCES. https://aws.amazon.com/marketplace/pp/prodview-4obg6a3wgyxaq?sr=0-4&ref_=beagle&applicationId=AWSMPContessaÂ
Moreover, you can find a more detailed description of ERAS at: https://www.iic.uam.es/procesamiento-del-lenguaje-natural/lanzamos-eras-analizador-de-lectura-facil-en-espanol/Â
Highlights
- Every day, we are inundated with information from various sources, yet 30% of people face reading or comprehension challenges (Distrito Propio, 2022). To address this, Easy Reading (ER) emerged to break down information barriers and improve access. Spanish legislation, like Article 14 of the Draft Criminal Procedure Law (2020) and Law 8/2021, supports the right of individuals with special needs to understand and be understood, particularly in the judicial sphere.
- ER aims to make information accessible for individuals with intellectual disabilities, language disorders, the elderly, immigrants, those with limited education, pre-birth deafness, or learning disorders.
- ERAS is designed for Spanish. ERAS is based on specific ER linguistic indexes. The indexes come from the state-of-the-art literature that researches the underlying mechanisms that contribute to easy text comprehension. Once the system is provided with a text, it evaluates it on the basis of length, punctuation, lexicon and syntax indexes. For each index it provides a score indicating areas for improvement.
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 |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $0.80/host/hour |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $1.80/host/hour |
ml.m5.xlarge Inference (Batch) | Model inference on the ml.m5.xlarge instance type, batch mode | $1.30/host/hour |
inference.count.m.i.c Inference Pricing | inference.count.m.i.c Inference Pricing | $0.10/request |
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a 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
We are excited to announce a new release of ERAS – Easy Reading Analyzer for Spanish, now in its 1.1 version. Additionally, the report generation function has been upgraded to enhance the quality of information visualization resources.
Additional details
Inputs
- Summary
ERAS configuration and data load is done through a JSON file. The file has three parameters.
- function: it defines the type of analysis to perform. It may acquire three values. It determines the way texts are ingested.
- /analyze: it analyzes and calculates metrics for a single text. When “/analyze” is applied, use the parameter “text” to include the text to be analyzed.
- /compare: it analyzes and calculates metrics for a couple of texts. When “/compare” is applied, use the parameters “text1”and “text2” to include the texts to be analyzed.
- /global_stat: it analyzes and calculates metrics for a list of texts. When “/global_stat” is applied, use the parameter “texts” to include the list of texts to be analyzed.
- mode: it may acquire two values.
- report: Generates a report in HTLM format.
- metrics: Generates sentence length, punctuation, lexicon, and sentence complexity metrics for each input text.
Example 1: analyze a single text and obtain the report.
input_eras = { "function": "/analyze", "text": “Esto es un ejemplo.”, "mode": "report" }
Example 2: analyze a couple of texts and obtain metrics.
input_eras = { "function": "/compare", "text1": “Esto es un ejemplo.”, "text2": “Esto es otro ejemplo.”, "mode": "metrics" }
Example 3: analyze a list of texts and obtain the report.
input_eras = { "function": "/global_stat", "texts": [“Este es el ejemplo 1.”, “Este es el ejemplo 2.”, ... “Este es el ejemplo n”], "mode": "report" }
- function: it defines the type of analysis to perform. It may acquire three values. It determines the way texts are ingested.
- Limitations for input type
- ERAS input JSON length is restricted by the type of instance where it is running: Instance type Maximum Input JSON character length ml.m5.large 75K ml.m5.xlarge 150K ml.m5.2xlarge 235K ERAS’ mode “report” allows a maximum of 100 texts, respecting character limitations mentioned in 1.
- Input MIME type
- application/json
https://github.com/iiconocimiento/iic-aws/blob/main/notebooks/easyReadingAnalyzer/data/input/input.json
https://github.com/iiconocimiento/iic-aws/blob/main/notebooks/easyReadingAnalyzer/data/input/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 |
|---|---|---|---|
function | defines the type of analysis to perform. It determines the way texts are ingested. | “function” may acquire three values:
* “/analyze”: to analyze a single text.
* “/compare”: to analyze a couple of texts.
* “/global_stat”: to analyze a list of texts. | Yes |
text | takes as its value the text to be analyzed when the value of the “function” parameter is “/analyze”. | “text” takes a string as its value. | Yes |
text1 | The “text1” parameter takes as its value the first text to be analyzed when the value of the “function” parameter is “/compare”. | “text1” takes a string as its value | Yes |
text2 | The “text2” parameter takes as its value the second text to be analyzed when the value of the “function” parameter is “/compare”. | “text2” takes a string as its value. | Yes |
texts | “texts” takes as its value a list of texts to be analyzed when the value of the “function” parameter is “/global_stat”. | “texts” takes a list of strings as its value. | Yes |
mode | “mode” determines the nature of the output. | “mode” may acquire two values:
* “report”: to generate a HTML report.
* “metrics”: to generate sentence length, punctuation, lexicon and sentence complexity metrics. | Yes |
Resources
Vendor resources
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