safetensors Diffusion Data Generation en dataset:MNIST license:unknown model-index region:us
---
datasets: MNIST
language: en
license: unknown
metrics:
  epoch: 31
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tags:
- Diffusion
- Data Generation
task: Data generation for computer vision tasks
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
![Include a plot of your training metrics here](loss_plot.png)

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

## Plots
![loss plots](loss_plot.png)

![Generated images](animation.gif)
