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README.md

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-library_name: transformers
+library_name: optimum
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-# Model Card for Model ID
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-<!-- Provide a quick summary of what the model is/does. -->
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-
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-## Model Details
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-### Model Description
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-<!-- Provide a longer summary of what this model is. -->
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
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-### Model Sources [optional]
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-<!-- Provide the basic links for the model. -->
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-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
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-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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-### Direct Use
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-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-[More Information Needed]
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-### Downstream Use [optional]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-[More Information Needed]
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-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-[More Information Needed]
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-## Bias, Risks, and Limitations
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-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-[More Information Needed]
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-### Recommendations
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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-## How to Get Started with the Model
-
-Use the code below to get started with the model.
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-[More Information Needed]
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-## Training Details
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-### Training Data
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-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-[More Information Needed]
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-### Training Procedure
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
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-[More Information Needed]
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-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-[More Information Needed]
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-## Evaluation
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-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-<!-- This should link to a Dataset Card if possible. -->
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-[More Information Needed]
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-[More Information Needed]
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
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-### Results
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-[More Information Needed]
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-#### Summary
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
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-[More Information Needed]
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-## Environmental Impact
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-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-[More Information Needed]
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-### Compute Infrastructure
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-[More Information Needed]
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-#### Hardware
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-[More Information Needed]
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-#### Software
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-[More Information Needed]
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-## Citation [optional]
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-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-**BibTeX:**
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-[More Information Needed]
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-**APA:**
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-[More Information Needed]
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-## Glossary [optional]
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-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-[More Information Needed]
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-## More Information [optional]
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-[More Information Needed]
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-## Model Card Authors [optional]
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-[More Information Needed]
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-## Model Card Contact
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+# Optimum RoBERTa-base-SQuAD2 Quantizado
+
+## Introdução
+
+Este repositório contém uma versão quantizada do modelo [`optimum/roberta-base-squad2`](https://huggingface.co/optimum/roberta-base-squad2), desenvolvido por Branden Chan et al. A quantização foi realizada utilizando a biblioteca Optimum ONNX para reduzir o tamanho do modelo e melhorar a eficiência, mantendo uma precisão aceitável.
+
+## Avaliação
+
+Os modelos foram testados utilizando 600 entradas do conjunto de validação da base de dados [rajpurkar/squad_v2](https://huggingface.co/datasets/rajpurkar/squad_v2).
+
+1. **Redução da Latência**:
+   - **Modelo Original**: 0.572 segundos por amostra
+   - **Modelo Quantizado**: 0.437 segundos por amostra
+   - **Análise**: A latência foi significativamente reduzida, tornando o modelo mais adequado para aplicações em tempo real.
+
+2. **Aumento da Eficiência**:
+   - **Tempo Total**:
+     - **Modelo Original**: 343.20 segundos
+     - **Modelo Quantizado**: 262.41 segundos
+     - **Análise**: O tempo total de execução foi consideravelmente reduzido.
+   - **Amostras por Segundo**:
+     - **Modelo Original**: 1.75 amostras/segundo
+     - **Modelo Quantizado**: 2.29 amostras/segundo
+     - **Análise**: A taxa de processamento aumentou, permitindo que mais amostras sejam processadas no mesmo período de tempo.
+
+3. **Manutenção de Precisão Razoável**:
+   - **Exact Score**:
+     - **Modelo Original**: 81.67
+     - **Modelo Quantizado**: 80.5
+     - **Análise**: Pequena queda na precisão, mas ainda em nível aceitável.
+   - **F1 Score**:
+     - **Modelo Original**: 83.75
+     - **Modelo Quantizado**: 82.49
+     - **Análise**: Queda ligeira no desempenho de F1 Score.
+
+4. **Comparação do Espaço Ocupado na Memória**:
+   - **Modelo Original**: 476.52 MB
+   - **Modelo Quantizado**: 122.41 MB
+   - **Análise**: A quantização resultou em uma redução significativa no espaço ocupado, com o modelo quantizado utilizando apenas cerca de 25.7% do tamanho do modelo original.
+
+Esses resultados indicam que a quantização foi bem-sucedida, alcançando uma redução significativa na latência, aumento na eficiência e uma economia substancial de espaço na memória, enquanto mantém uma precisão aceitável para tarefas de perguntas e respostas.