---
license: apache-2.0
base_model: google/flan-t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: flan-t5-small-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-small-nvidia

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](https://huggingface.co/google/flan-t5-small) trained on [ajsbsd/datasets/nvidia-qa](https://huggingface.co/datasets/ajsbsd/nvidia-qa)

It achieves the following results on the evaluation set:

- Loss: 2.0857
- Rouge1: 0.3970
- Rouge2: 0.2295
- Rougel: 0.3537
- Rougelsum: 0.3593

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 2.8569        | 1.0   | 711  | 2.3454          | 0.3748 | 0.2036 | 0.3321 | 0.3375    |
| 2.5034        | 2.0   | 1422 | 2.2079          | 0.3841 | 0.2143 | 0.3417 | 0.3465    |
| 2.1886        | 3.0   | 2133 | 2.1342          | 0.3900 | 0.2227 | 0.3494 | 0.3543    |
| 2.0784        | 4.0   | 2844 | 2.0972          | 0.3951 | 0.2267 | 0.3522 | 0.3571    |
| 1.9843        | 5.0   | 3555 | 2.0857          | 0.3970 | 0.2295 | 0.3537 | 0.3593    |


### Framework versions

- Transformers 4.35.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1