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---
tags:
- Diffusion
- Data Generation
language: en
task: Data generation for computer vision tasks
datasets: MNIST
metrics:
epoch: 31
train_loss:
- 0.0908
- 0.0245
- 0.0209
- 0.0194
- 0.0185
- 0.0178
- 0.0172
- 0.0169
- 0.0167
- 0.0161
- 0.0161
- 0.0159
- 0.0158
- 0.0154
- 0.0155
- 0.0154
- 0.0152
- 0.0151
- 0.015
- 0.0152
- 0.0151
- 0.0148
- 0.0148
- 0.0148
- 0.0147
- 0.0146
- 0.0147
- 0.0145
- 0.0146
- 0.0146
- 0.0146
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
---
# 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 [0.0908, 0.0245, 0.0209, 0.0194, 0.0185, 0.0178, 0.0172, 0.0169, 0.0167, 0.0161, 0.0161, 0.0159, 0.0158, 0.0154, 0.0155, 0.0154, 0.0152, 0.0151, 0.015, 0.0152, 0.0151, 0.0148, 0.0148, 0.0148, 0.0147, 0.0146, 0.0147, 0.0145, 0.0146, 0.0146, 0.0146].
## 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
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