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README.md

@@ -1,1032 +1,9 @@
 ---
 tags:
-- Diffusion
-- Data Generation
-language: en
-task: Data generation for computer vision tasks
-datasets: MNIST
-metrics:
-  epoch: 30
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-license: unknown
-model-index:
-- name: diffusion-practice-v1
-  results:
-  - task:
-      type: nlp
-      name: Data Generation with Diffusion Model
-    dataset:
-      name: MNIST
-      type: mnist
-    metrics:
-    - type: loss
-      value: '0.01'
-      name: Loss
-      verified: false
+- model_hub_mixin
+- pytorch_model_hub_mixin
 ---
 
-# NLI-FEVER Model
-
-This model is fine-tuned for Natural Language Inference (NLI) tasks using the FEVER dataset.
-
-## Model description
-
-## Intended uses & limitations
-
-This model is intended for use in NLI tasks, particularly those related to fact-checking and verifying information.
-It should not be used for tasks it wasn't explicitly trained for.
-
-## Training and evaluation data
-
-The model was trained on the FEVER (Fact Extraction and VERification) dataset.
-
-## Training procedure
-
-The model was trained for 30 epochs
-Train Losses of [0.012823267839848995, 0.01400498952716589, 0.013216765597462654, 0.018119243904948235, 0.009935321286320686, 0.016109945252537727, 0.017806213349103928, 0.019163455814123154, 0.029926661401987076, 0.01744312420487404, 0.014265676029026508, 0.013890810310840607, 0.013515425845980644, 0.015186883509159088, 0.014874613843858242, 0.014196827076375484, 0.016532881185412407, 0.012824700213968754, 0.021470380946993828, 0.013137794099748135, 0.01830434426665306, 0.013973318971693516, 0.017635220661759377, 0.018215475603938103, 0.014680536463856697, 0.013698616065084934, 0.01598021388053894, 0.020114580169320107, 0.01238455343991518, 0.015865936875343323, 0.01747105084359646, 0.009441102854907513, 0.01144119631499052, 0.016735738143324852, 0.012419845908880234, 0.011100444942712784, 0.01321379840373993, 0.015999609604477882, 0.01474534347653389, 0.014897378161549568, 0.017970088869333267, 0.015076511539518833, 0.012804070487618446, 0.01309606246650219, 0.02567661926150322, 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-
-## How to use
-
-You can use this model directly with a pipeline for text classification:
-
-```python
-from transformers import pipeline
-
-classifier = pipeline("text-classification", model="YusuphaJuwara/nli-fever")
-result = classifier("premise", "hypothesis")
-print(result)
-```
-
-## Saved Metrics
-
-This model repository includes a `metrics.json` file containing detailed training metrics.
-You can load these metrics using the following code:
-
-```python
-from huggingface_hub import hf_hub_download
-import json
-
-metrics_file = hf_hub_download(repo_id="YusuphaJuwara/nli-fever", filename="metrics.json")
-with open(metrics_file, 'r') as f:
-metrics = json.load(f)
-
-# Now you can access metrics like:
-print("Last epoch: ", metrics['last_epoch'])
-print("Final validation loss: ", metrics['val_losses'][-1])
-print("Final validation accuracy: ", metrics['val_accuracies'][-1])
-```
-
-These metrics can be useful for continuing training from the last epoch or for detailed analysis of the training process.
-
-## Training results
-![Include a plot of your training metrics here](training_plot.png)
-
-Limitations and bias
-## This model may exhibit biases present in the training data. Always validate results and use the model responsibly.
-
-## Plots
-![Labels distribution plots](label_distribution.png)
-![loss plots](loss_plot.png)
-![accuracy plots](accuracy_plot.png)
-![f1 score plots](f1_score_plot.png)
-![confusion matrix plots](confusion_matrix.png)
-![precision recall curve plots](precision_recall_curve.png)
-![roc curve plots](roc_curve.png)
+This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
+- Library: [More Information Needed]
+- Docs: [More Information Needed]

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