---
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: flan-t5-base-nvidia
  results: []
---

<!-- 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. -->

# flan-t5-base-nvidia

This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) trained on [ajsbsd/datasets/nvidia-qa](https://huggingface.co/datasets/ajsbsd/nvidia-qa)

Imported from Kaggle (https://www.kaggle.com/datasets/gondimalladeepesh/nvidia-documentation-question-and-answer-pairs)

Q&A dataset for LLM finetuning about the NVIDIA about SDKs and blogs

This model is a fine-tuned version of google/flan-t5-small trained on 

It achieves the following results on the evaluation set:
- Loss: 1.7117
- Rouge1: 0.4290
- Rouge2: 0.2696
- Rougel: 0.3880
- Rougelsum: 0.3928

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 2.4618        | 1.0   | 711  | 1.9707          | 0.3886 | 0.2185 | 0.3472 | 0.3522    |
| 2.0575        | 2.0   | 1422 | 1.8104          | 0.4066 | 0.2407 | 0.3647 | 0.3701    |
| 1.5839        | 3.0   | 2133 | 1.7351          | 0.4185 | 0.2558 | 0.3770 | 0.3821    |
| 1.4314        | 4.0   | 2844 | 1.7079          | 0.4252 | 0.2655 | 0.3840 | 0.3892    |
| 1.2582        | 5.0   | 3555 | 1.7117          | 0.4290 | 0.2696 | 0.3880 | 0.3928    |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.14.7
- Tokenizers 0.15.0