| tags: | |
| - 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 | |
| # 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 | |
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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 | |
|  | |
| Limitations and bias | |
| ## This model may exhibit biases present in the training data. Always validate results and use the model responsibly. | |
| ## Plots | |
|  | |
|  | |