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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: 29
+  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 29 epochs
+Train Losses of [0.017333630472421646, 0.013288658112287521, 0.014131959527730942, 0.01134562212973833, 0.015983732417225838, 0.01854863576591015, 0.01349550113081932, 0.013131790794432163, 0.014642741531133652, 0.021217338740825653, 0.012515627779066563, 0.012617889791727066, 0.01402036938816309, 0.010789299383759499, 0.013743193820118904, 0.014298714697360992, 0.014383658766746521, 0.01215966884046793, 0.012905692681670189, 0.016368890181183815, 0.00955274235457182, 0.015453857369720936, 0.017132369801402092, 0.014982742257416248, 0.016994431614875793, 0.01078822836279869, 0.023941243067383766, 0.020318197086453438, 0.01393840741366148, 0.019643334671854973, 0.018156161531805992, 0.01748468540608883, 0.013957047834992409, 0.014808719977736473, 0.015526026487350464, 0.009133009240031242, 0.014872062020003796, 0.011816459707915783, 0.008824722841382027, 0.014392831362783909, 0.016627607867121696, 0.016759013757109642, 0.019479580223560333, 0.011849241331219673, 0.020260635763406754, 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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)