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---
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.\n [REVERSED-PROMPT] "
example_title: "reverse prompt"
---
# core-prompt-reverser-opt-1.3b
This model is a fine-tuned version of [ss5](https://huggingface.co/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
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