---
license: apache-2.0
base_model: google-t5/t5-base
tags:
- generated_from_trainer
model-index:
- name: t5-base-mrqa-16
  results: []
datasets:
- enriquesaou/mrqa-squadded-sample
---

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# t5-base-mrqa-16

This model is a fine-tuned version of [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on an MRQA sample.
It achieves the following results on the evaluation set:
- Loss: 0.653221

## Model description

T5 base but trained at FP16 in the MRQA sample dataset. 
This model is the checkpoint at 3000 steps (3rd epoch), because there were instabilities during the late epochs.

## 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: 3e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 18
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3 (5) (we take model checkpoint at 3rd epoch)
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.7978        | 0.9996 | 833  | 0.6668          |
| 0.6516        | 1.9992 | 1666 | 0.6532          |
| 0.6275        | 3.0    | 2500 | 0.6532          |
|(0.6443)       |(3.9996)|(3333)|(0.6533)         |
|(2.0743)       |(4.998) |(4165 |(nan)            |

Note that this model is the checkpoint at 3000 steps (3rd epoch), because there were instabilities during the late epochs.

### Framework versions

- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1