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README.md

@@ -1,9 +1,1032 @@
 ---
 tags:
-- model_hub_mixin
-- pytorch_model_hub_mixin
+- Diffusion
+- Data Generation
+language: en
+task: Data generation for computer vision tasks
+datasets: MNIST
+metrics:
+  epoch: 31
+  train_loss:
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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: '0.02'
+      name: Loss
+      verified: false
 ---
 
-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]
+# 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 31 epochs
+Train Losses of [0.012500390410423279, 0.018069975078105927, 0.01557229831814766, 0.011175226420164108, 0.011422666721045971, 0.011030735448002815, 0.01722785457968712, 0.01321368757635355, 0.014989392831921577, 0.012408463284373283, 0.012982162646949291, 0.012269439175724983, 0.018606187775731087, 0.011797977611422539, 0.013769544661045074, 0.01915244199335575, 0.0198991522192955, 0.01421719416975975, 0.015622736886143684, 0.015983033925294876, 0.01271776296198368, 0.012229482643306255, 0.014163941144943237, 0.008050181902945042, 0.013724284246563911, 0.01594972051680088, 0.015842918306589127, 0.012332979589700699, 0.013316147960722446, 0.016753198578953743, 0.018078463152050972, 0.011557980440557003, 0.01905280351638794, 0.016684118658304214, 0.021189577877521515, 0.010320262052118778, 0.008208866231143475, 0.01250820979475975, 0.009428021498024464, 0.014835948124527931, 0.010922946035861969, 0.008658873848617077, 0.014398415572941303, 0.013922883197665215, 0.014133173041045666, 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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)