update model card README.md

README.md

@@ -10,73 +10,38 @@ metrics:
 model-index:
 - name: toxicity-target-type-identification
   results: []
-datasets:
-- dougtrajano/olid-br
-language:
-- pt
-library_name: transformers
 ---
 
-# toxicity-target-type-identification
-
-Toxicity Target Type Identification is a model that classifies the type (individual, group, or other) of a given targeted text.
-
-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).
-
-## Overview
-
-**Input:** Text in Brazilian Portuguese
-
-**Output:** Multiclass classification (individual, group, or other)
-
-## Usage
-
-```python
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-
-tokenizer = AutoTokenizer.from_pretrained("dougtrajano/toxicity-target-type-identification")
-
-model = AutoModelForSequenceClassification.from_pretrained("dougtrajano/toxicity-target-type-identification")
-```
-
-## Limitations and bias
-
-The following factors may degrade the model’s performance.
+<!-- 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. -->
 
-**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.
+# toxicity-target-type-identification
 
-## Performance
+This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.4281
+- Accuracy: 0.8002
+- F1: 0.7986
+- Precision: 0.7990
+- Recall: 0.8002
 
-The model was evaluated on the test set of the [OLID-BR](https://dougtrajano.github.io/olid-br/) dataset.
+## Model description
 
-**Accuracy:** 0.7505
+More information needed
 
-**Precision:** 0.7812
+## Intended uses & limitations
 
-**Recall:** 0.7505
+More information needed
 
-**F1-Score:** 0.7603
+## Training and evaluation data
 
-| Class | Precision | Recall | F1-Score | Support |
-| :---: | :-------: | :----: | :------: | :-----: |
-| `INDIVIDUAL` | 0.8850 | 0.7964 | 0.8384 | 609 |
-| `GROUP` | 0.6766 | 0.6385 | 0.6570 | 213 |
-| `OTHER` | 0.4518 | 0.7177 | 0.5545 | 124 |
+More information needed
 
 ## Training procedure
 
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-
 - learning_rate: 3.952388499692274e-05
 - train_batch_size: 8
 - eval_batch_size: 8
@@ -85,13 +50,20 @@ The following hyperparameters were used during training:
 - lr_scheduler_type: linear
 - num_epochs: 30
 
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| No log        | 1.0   | 355  | 0.7145          | 0.6903   | 0.7052 | 0.7528    | 0.6903 |
+| 0.8011        | 2.0   | 710  | 0.9930          | 0.7928   | 0.7840 | 0.7835    | 0.7928 |
+| 0.529         | 3.0   | 1065 | 1.4281          | 0.8002   | 0.7986 | 0.7990    | 0.8002 |
+| 0.529         | 4.0   | 1420 | 1.6783          | 0.7727   | 0.7753 | 0.7788    | 0.7727 |
+| 0.2706        | 5.0   | 1775 | 2.3904          | 0.7727   | 0.7683 | 0.7660    | 0.7727 |
+
+
 ### Framework versions
 
 - Transformers 4.26.1
 - 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.