update model card README.md

README.md

@@ -15,11 +15,9 @@ model-index:
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-# Coding challenge
-The challenge involved building a fake news classifier using the huggingface library.
-
-This final model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an fake-and-real-news dataset. The link to the dataset is https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset.
+# FakevsRealNews
 
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.0000
 - Accuracy: 1.0
@@ -29,15 +27,17 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-Finetuned Distilbert
- 
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
 ## Training and evaluation data
 
-The training data was split into train-dev-test in the ratio 80-10-10.
+More information needed
 
 ## Training procedure
-The title and text of each news story was concatenated to form each datapoint. Then a model was finetuned to perform single label classification on each datapoint. The final prediction is the class with the highest probability.
-
 
 ### Training hyperparameters
 
@@ -53,16 +53,16 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
-| 0.0503        | 1.0   | 1956 | 0.0025          | 0.9995   | 0.9995 | 0.9995    | 0.9995 |
-| 0.001         | 2.0   | 3912 | 0.0001          | 1.0      | 1.0    | 1.0       | 1.0    |
-| 0.0007        | 3.0   | 5868 | 0.0000          | 1.0      | 1.0    | 1.0       | 1.0    |
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1  | Precision | Recall |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---:|:---------:|:------:|
+| 0.0554        | 1.0   | 1956 | 0.0000          | 1.0      | 1.0 | 1.0       | 1.0    |
+| 0.0006        | 2.0   | 3912 | 0.0000          | 1.0      | 1.0 | 1.0       | 1.0    |
+| 0.0           | 3.0   | 5868 | 0.0000          | 1.0      | 1.0 | 1.0       | 1.0    |
 
 
 ### Framework versions
 
-- Transformers 4.18.0
-- Pytorch 1.10.0+cu111
-- Datasets 2.1.0
-- Tokenizers 0.12.1
+- Transformers 4.27.2
+- Pytorch 1.13.1+cu116
+- Datasets 2.10.1
+- Tokenizers 0.13.2