metadata

license: other
datasets:
  - Open-Orca/OpenOrca
  - ehartford/wizard_vicuna_70k_unfiltered
tags:
  - code
  - prompt
  - reverse prompt
widget:
  - text: >-
      The results on conditioned open-ended language generation are impressive,
      having shown to generalize to new tasks, handle code, or take non-text
      data as input. Besides the improved transformer architecture and massive
      unsupervised training data, better decoding methods have also played an
      important role.
       [REVERSED-PROMPT]  
    example_title: reverse prompt

core-prompt-reverser-opt-1.3b

This model is a fine-tuned version of ss5 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.2950
Accuracy: 0.7084

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: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 1.0

Training results

Framework versions

Transformers 4.33.0.dev0
Pytorch 2.1.0.dev20230605+cu121
Datasets 2.14.4
Tokenizers 0.13.3