metadata
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
t5-base-mrqa-16
This model is a fine-tuned version of 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