Update README.md

README.md

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-This model is a fine-tuned version of roberta-base on the dirtycomputer/Toxic_Comment_Classification_Challenge dataset. It is designed for the task of classifying comments based on their toxicity levels.
-
-Model description
-This is a text classification model fine-tuned from RoBERTa for detecting toxic comments. It categorizes a comment into multiple toxicity categories including toxic, obscene, insult, and threat. The model achieved a high accuracy of 98.12% on the evaluation set, making it suitable for applications that require automated content moderation.
-
-Intended uses & limitations
-Intended Uses
-Toxicity Classification: This model can be applied in automated comment moderation systems, social media platforms, forums, and customer support to filter out toxic content.
-Text Analysis: It can be used for analyzing large datasets of user-generated content to detect harmful language.
-Limitations
-False Negatives: The model may still miss subtle forms of toxic content or new types of toxicity not well represented in the training data.
-Performance Variance: It may perform less well on short comments or on non-standard language use (e.g., slang or code-switching).
-Language: Currently, this model only supports English.
-Training and evaluation data
-The model was fine-tuned on the dirtycomputer/Toxic_Comment_Classification_Challenge dataset, which contains labeled examples of toxic and non-toxic comments. This dataset includes categories such as "toxic", "obscene", "insult", and "threat".
-
-Training procedure
-Training hyperparameters
+---
+library_name: transformers
+license: mit
+base_model: roberta-base
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: toxicity-classification-model
+  results: []
+datasets:
+- dirtycomputer/Toxic_Comment_Classification_Challenge
+language:
+- en
+pipeline_tag: text-classification
+---
+
+# toxicity-classification-model
+
+This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the dirtycomputer/Toxic_Comment_Classification_Challenge dataset. It achieves the following results on the evaluation set:
+- Loss: 0.0511
+- Accuracy: 0.9812
+
+## Model description
+
+Fine-tuned roberta-base model for detecting toxicity in comments. It categorizes a comment into different toxicity types, such as "toxic," "obscene," "insult," and "threat."
+
+## Intended uses & limitations
+
+### Intended Uses
+- **Content Moderation**: Automatically flagging or removing toxic comments on social media platforms, forums, and customer support.
+- **Toxicity Detection**: Classifying comments based on their toxicity level, such as harmful language or insults.
+
+### Limitations
+- **False Negatives**: May not always catch subtle toxic behavior.
+- **Limited Language Support**: Currently, the model is trained on English-only data.
+- **Context Sensitivity**: May struggle with ambiguous language or sarcasm.
+
+## Training and evaluation data
+
+This model was fine-tuned using the **dirtycomputer/Toxic_Comment_Classification_Challenge** dataset, which contains labeled comments for toxicity classification.
+
+## Training procedure
+
+### Training hyperparameters
+
 The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 8
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Use adamw_torch with betas=(0.9, 0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|
+| 0.1691        | 1.0   | 17952 | 0.1464          | 0.9617   |
+| 0.0892        | 2.0   | 35904 | 0.1456          | 0.9617   |
+| 0.0527        | 3.0   | 53856 | 0.0511          | 0.9812   |
+
+### Framework versions
 
-Learning rate: 5e-05
-Train batch size: 8
-Eval batch size: 16
-Seed: 42
-Optimizer: AdamW (betas=(0.9, 0.999), epsilon=1e-08)
-LR Scheduler Type: Linear
-LR Scheduler Warmup Steps: 500
-Number of epochs: 3
-Training results
-Training Loss	Epoch	Step	Validation Loss	Accuracy
-0.1691	1	17952	0.1464	0.9617
-0.0892	2	35904	0.1456	0.9617
-0.0527	3	53856	0.0511	0.9812
-Framework versions
-Transformers: 4.47.1
-PyTorch: 2.5.1+cu121
-Datasets: 3.2.0
-Tokenizers: 0.21.0
-Model metrics
-Accuracy: 98.12%
-Loss: 0.0511
-License
-This model is licensed under the MIT license.
-
-Tags
-Generated from Trainer
-Text Classification
-Toxicity Classification
+- Transformers 4.47.1
+- Pytorch 2.5.1+cu121
+- Datasets 3.2.0
+- Tokenizers 0.21.0