|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_keras_callback |
|
model-index: |
|
- name: chatgpt-prompt-generator-v12 |
|
results: [] |
|
datasets: |
|
- fka/awesome-chatgpt-prompts |
|
--- |
|
|
|
|
|
# ChatGPT Prompt Generator v12 |
|
|
|
This model is a fine-tuned version of [BART-large](https://huggingface.co/facebook/bart-large) on a ChatGPT prompts dataset. |
|
It achieves the following results on the evaluation set: |
|
It achieves the following results on the evaluation set: |
|
- Train Loss: 2.4800 |
|
- Validation Loss: 2.7320 |
|
- Epoch: 4 |
|
|
|
|
|
## Intended uses & limitations |
|
|
|
You can use this to generate ChatGPT personas. Simply input a persona like below: |
|
|
|
``` |
|
from transformers import BartForConditionalGeneration, BartTokenizer |
|
|
|
example_english_phrase = "photographer" |
|
batch = tokenizer(example_english_phrase, return_tensors="pt") |
|
generated_ids = model.generate(batch["input_ids"], max_new_tokens=150) |
|
output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) |
|
``` |
|
|
|
## Training procedure |
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
|
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} |
|
- training_precision: float32 |
|
|
|
### Training results |
|
|
|
| Train Loss | Validation Loss | Epoch | |
|
|:----------:|:---------------:|:-----:| |
|
| 5.3808 | 3.3133 | 0 | |
|
| 3.2642 | 3.0104 | 1 | |
|
| 2.8886 | 2.8600 | 2 | |
|
| 2.6594 | 2.7949 | 3 | |
|
| 2.4800 | 2.7320 | 4 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.26.1 |
|
- TensorFlow 2.11.0 |
|
- Datasets 2.10.1 |
|
- Tokenizers 0.13.2 |
