Update README.md

README.md

@@ -3,197 +3,145 @@ library_name: transformers
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-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Model Card for DistilBERT Text Classification
 
+This is a DistilBERT model fine-tuned for text classification tasks.
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **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]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
+This DistilBERT model is fine-tuned for text classification tasks. It is designed to classify texts into different categories based on the provided dataset.
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+- **Developed by:** Thiago Adriano
+- **Model type:** DistilBERT for Sequence Classification
+- **Language(s) (NLP):** Portuguese
+- **License:** MIT License
+- **Finetuned from model:** distilbert-base-uncased
 
-### Direct Use
+### Model Sources
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+- **Repository:** [Link to your repository](https://huggingface.co/tadrianonet/distilbert-text-classification)
 
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
-[More Information Needed]
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("tadrianonet/distilbert-text-classification")
+model = AutoModelForSequenceClassification.from_pretrained("tadrianonet/distilbert-text-classification")
+
+inputs = tokenizer("Sample text for classification", return_tensors="pt")
+outputs = model(**inputs)
+```
 
 ## Training Details
 
 ### Training Data
 
-<!-- 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. -->
-
-[More Information Needed]
+The training data consists of text-label pairs in Portuguese. The data is preprocessed to tokenize the text and convert labels to numerical format.
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
+The model is fine-tuned using the Hugging Face `Trainer` API with the following hyperparameters:
 
-[More Information Needed]
+- **Training regime:** fp32
+- **Learning rate:** 2e-5
+- **Batch size:** 16
+- **Epochs:** 3
 
+#### Speeds, Sizes, Times
 
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+- **Training time:** Approximately 10 minutes on a single GPU
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
 ### Testing Data, Factors & Metrics
 
 #### Testing Data
 
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
+The testing data is a separate set of text-label pairs used to evaluate the model's performance.
 
 #### Factors
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+The evaluation is disaggregated by accuracy and loss.
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+- **Accuracy:** Measures the proportion of correct predictions
+- **Loss:** Measures the error in the model's predictions
 
 ### Results
 
-[More Information Needed]
+- **Evaluation Results:**
+  - **Loss:** 0.692
+  - **Accuracy:** 50%
 
 #### Summary
 
+The model achieves 50% accuracy on the evaluation dataset, indicating that further fine-tuning and evaluation on a more diverse dataset may be necessary.
 
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
+## Model Examination
 
 [More Information Needed]
 
 ## Environmental Impact
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
 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).
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
+- **Hardware Type:** GPU
+- **Hours used:** 0.2 hours
 - **Cloud Provider:** [More Information Needed]
 - **Compute Region:** [More Information Needed]
 - **Carbon Emitted:** [More Information Needed]
 
-## Technical Specifications [optional]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+The model is based on DistilBERT, a smaller, faster, and cheaper version of BERT, designed for efficient text classification.
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
 #### Hardware
 
-[More Information Needed]
+- **Hardware Type:** Single GPU
+- **GPU Model:** [More Information Needed]
 
 #### Software
 
-[More Information Needed]
-
-## Citation [optional]
+- **Framework:** Transformers 4.x
+- **Library:** PyTorch
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+## Citation
 
 **BibTeX:**
 
-[More Information Needed]
+1 ```bibtex
+@misc{thiago_adriano_2024_distilbert,
+  author = {Thiago Adriano},
+  title = {DistilBERT Text Classification},
+  year = {2024},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/tadrianonet/distilbert-text-classification}},
+}
+1 ```
 
 **APA:**
 
-[More Information Needed]
-
-## Glossary [optional]
+Thiago Adriano. (2024). DistilBERT Text Classification. Hugging Face. https://huggingface.co/tadrianonet/distilbert-text-classification
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+## More Information
 
-[More Information Needed]
+For more details, visit the [Hugging Face model page](https://huggingface.co/tadrianonet/distilbert-text-classification).
 
-## More Information [optional]
+## Model Card Authors
 
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
+Thiago Adriano
 
 ## Model Card Contact
 
-[More Information Needed]
+For more information, contact Thiago Adriano at [[email protected]]