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distilbert-incoherence-classifier

Text Classification

Transformers

Safetensors

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distilbert

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CortexPE commited on Feb 25

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----
-library_name: transformers
-license: cc-by-sa-4.0
-tags:
-- text-classification
-- incoherence
-- text
-model_info: A custom fine-tuned model for classifying text as incoherent or coherent.
-description: This is a DistilBERT model fine-tuned to classify text based on its coherence.
-  The model can identify various types of incoherence, such as grammatical errors,
-  word soup, random words, and run-on sentences.
-usage: This model can be used for text classification tasks, specifically for detecting
-  and categorizing different types of text incoherence.
-limitations: The model has been trained on a generated dataset, so care must be taken
-  in evaluating it in the real world. More data may need to be collected before evaluating
-  this model in a real-world setting.
----
-# Model Card for Model ID
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 ## Model Details
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+# DistilBERT Incoherence Classifier
+This is a fine-tuned DistilBERT model for classifying text based on its coherence. It can identify various types of incoherence.
 ## Model Details
+-   **Model:** DistilBERT (distilbert-base-uncased)
+-   **Task:** Text Classification (Coherence Detection)
+-   **Fine-tuning:** The model was fine-tuned using a custom-generated dataset that features various types of incoherence.
+- **Training Dataset** The model was trained on the [incoherent-text-dataset](https://huggingface.co/datasets/your_huggingface_username/incoherent-text-dataset) dataset, located on Huggingface.
+## Training Metrics
+| Epoch | Training Loss | Validation Loss | Accuracy | Precision | Recall | F1       |
+| :---- | :------------ | :-------------- | :------- | :-------- | :----- | :------- |
+| 1     | 0.037500      | 0.071958        | 0.984995 | 0.985002  | 0.984995 | 0.984564 |
+| 2     | 0.008900      | 0.068670        | 0.985995 | 0.985973  | 0.985995 | 0.985603 |
+| 3     | 0.008500      | 0.058111        | 0.990330 | 0.990260  | 0.990330 | 0.990262 |
+## Evaluation Metrics
+The following metrics were measured on the test set:
+| Metric      | Value    |
+| :---------- | :------- |
+| Loss        | 0.049511 |
+| Accuracy    | 0.991    |
+| Precision   | 0.990958 |
+| Recall      | 0.991    |
+| F1-Score    | 0.990962 |
+## Classification Report:
+```
+                    precision    recall  f1-score   support
+          coherent       0.99      0.99      0.99      1500
+grammatical_errors       0.96      0.94      0.95       250
+      random_bytes       1.00      1.00      1.00       250
+     random_tokens       1.00      1.00      1.00       250
+      random_words       1.00      1.00      1.00       250
+            run_on       1.00      0.99      1.00       250
+         word_soup       1.00      1.00      1.00       250
+          accuracy                           0.99      3000
+         macro avg       0.99      0.99      0.99      3000
+      weighted avg       0.99      0.99      0.99      3000
+```
+## Confusion Matrix
+![Confusion Matrix](confusion_matrix.png)
+The confusion matrix above shows the performance of the model on each class.
+## Usage
+This model can be used for text classification tasks, specifically for detecting and categorizing different types of text incoherence. You can use the `inference_example` function provided in the notebook to test your own text.
+## Limitations
+The model has been trained on a generated dataset, so care must be taken in evaluating it in the real world. More data may need to be collected before evaluating this model in a real-world setting.
+## License
+CC-BY-SA 4.0