Update README.md

README.md

@@ -16,38 +16,68 @@ metrics:
 model-index:
 - name: dougtrajano/toxic-comment-classification
   results: []
+datasets:
+- dougtrajano/olid-br
+library_name: transformers
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+Toxic Comment Classification is a model that detects if the text is toxic or not.
 
-# dougtrajano/toxic-comment-classification
+This BERT model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the [OLID-BR dataset](https://huggingface.co/datasets/dougtrajano/olid-br).
 
-This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the OLID-BR dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.5590
-- Accuracy: 0.8578
-- F1: 0.8580
-- Precision: 0.8594
-- Recall: 0.8578
+## Overview
 
-## Model description
+**Input:** Text in Brazilian Portuguese
 
-More information needed
+**Output:** Binary classification (toxic or not toxic)
 
-## Intended uses & limitations
+## Usage
 
-More information needed
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-## Training and evaluation data
+tokenizer = AutoTokenizer.from_pretrained("dougtrajano/toxic-comment-classification")
 
-More information needed
+model = AutoModelForSequenceClassification.from_pretrained("dougtrajano/toxic-comment-classification")
+```
+
+## Limitations and bias
+
+The following factors may degrade the model’s performance.
+
+**Text Language**:  The model was trained on Brazilian Portuguese texts, so it may not work well with Portuguese dialects.
+
+**Text Origin**: The model was trained on texts from social media and a few texts from other sources, so it may not work well on other types of texts.
+
+## Trade-offs
+
+Sometimes models exhibit performance issues under particular circumstances. In this section, we'll discuss situations in which you might discover that the model performs less than optimally, and should plan accordingly.
+
+**Text Length**: The model was fine-tuned on texts with a word count between 1 and 178 words (average of 18 words). It may give poor results on texts with a word count outside this range.
+
+## Performance
+
+The model was evaluated on the test set of the [OLID-BR](https://dougtrajano.github.io/olid-br/) dataset.
+
+**Accuracy:** 0.8578
+
+**Precision:** 0.8594
+
+**Recall:** 0.8578
+
+**F1-Score:** 0.8580
+
+| Class | Precision | Recall | F1-Score | Support |
+| :---: | :-------: | :----: | :------: | :-----: |
+| `NOT-OFFENSIVE` | 0.8886 | 0.8490 | 0.8683 | 1,775 |
+| `OFFENSIVE` | 0.8233 | 0.8686 | 0.8453 | 1,438 |
 
 ## Training procedure
 
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
+
 - learning_rate: 3.255788747459486e-05
 - train_batch_size: 8
 - eval_batch_size: 8
@@ -57,24 +87,13 @@ The following hyperparameters were used during training:
 - num_epochs: 30
 - label_smoothing_factor: 0.07158711257743958
 
-### Training results
-
-| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
-|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
-| 0.4422        | 1.0   | 1408  | 0.4197          | 0.8466   | 0.8470 | 0.8505    | 0.8466 |
-| 0.3566        | 2.0   | 2816  | 0.4724          | 0.8413   | 0.8394 | 0.8453    | 0.8413 |
-| 0.3135        | 3.0   | 4224  | 0.4801          | 0.8447   | 0.8434 | 0.8470    | 0.8447 |
-| 0.2638        | 4.0   | 5632  | 0.5590          | 0.8578   | 0.8580 | 0.8594    | 0.8578 |
-| 0.2314        | 5.0   | 7040  | 0.5605          | 0.8491   | 0.8487 | 0.8489    | 0.8491 |
-| 0.2221        | 6.0   | 8448  | 0.6369          | 0.8416   | 0.8414 | 0.8414    | 0.8416 |
-| 0.1939        | 7.0   | 9856  | 0.6518          | 0.8400   | 0.8402 | 0.8405    | 0.8400 |
-| 0.2015        | 8.0   | 11264 | 0.6042          | 0.8462   | 0.8457 | 0.8465    | 0.8462 |
-| 0.1989        | 9.0   | 12672 | 0.6236          | 0.8500   | 0.8496 | 0.8499    | 0.8500 |
-
-
 ### Framework versions
 
 - Transformers 4.26.0
 - Pytorch 1.10.2+cu113
 - Datasets 2.9.0
 - Tokenizers 0.13.2
+
+## Provide Feedback
+
+If you have any feedback on this model, please [open an issue](https://github.com/DougTrajano/ToChiquinho/issues/new) on GitHub.