---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: Nato-chat
  results: []

language: 
- en

widget:
- text: "What is the Full form of NATO?"
  example_title: "Full Form"
- text: "Name the NATO member countries."
  example_title: 'NATO Members'
- text: "What kind of support did Ukraine offer to NATO?"
  example_title: 'Example 1'
- text: 'Which country withdrew from the integrated military command of NATO in 1966?'
  example_title: 'Example 2'
- text: 'Who were the original members of NATO'
  example_title: 'OG Members'
- text: 'When was NATO established?'
  example_title: 'Example 3'
- text: 'How many NATO members are there currently?'
  example_title: 'Example 4'
- text: 'Who are the representatives of NATO member countries?'
  example_title: 'Example 5'
- text: 'Question: What is the aim of the Mediterranean Dialogue?'
  example_title: 'Example 6'



  

inference:
  parameters:
    max_length: 600
  

---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Nato-chat

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1764
- Rouge1: 0.6435
- Rouge2: 0.5596
- Rougel: 0.6287
- Rougelsum: 0.6312

## Model description
Flan-t5 Base

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear

### Framework versions

- Transformers 4.35.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0