from transformers import pipeline

model_tag = "postbot/distilgpt2-emailgen"
generator = pipeline(
              'text-generation', 
              model=model_tag, 
            )
            
prompt = """
Hello, 

Following up on the bubblegum shipment."""

result = generator(
    prompt,
    max_length=64,
    do_sample=False,
    early_stopping=True,
) # generate
print(result[0]['generated_text'])

try it in a Google Colab notebook
Use it in bash/cmd with this gist :)

For this model, formatting matters. The results may be (significantly) different between the structure outlined above and prompt = "Hey, just wanted to ..." etc.

Model description

This model is a fine-tuned version of distilgpt2 on a dataset of 50k emails, including the classic aeslc dataset.

It achieves the following results on the evaluation set:

Loss: 2.6247

Intended uses & limitations

The intended use of this model is to provide suggestions to "autocomplete" the rest of your email. Said another way, it should serve as a tool to write predictable emails faster. It is not intended to write entire emails; at least some input is required to guide the direction of the model.

Please verify any suggestions by the model for A) False claims and B) negation statements before accepting/sending something.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 6e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
gradient_accumulation_steps: 32
total_train_batch_size: 256
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.02
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss
2.8299	1.0	248	2.7971
2.6984	2.0	496	2.6826
2.7022	3.0	744	2.6361
2.6436	4.0	992	2.6245
2.6195	5.0	1240	2.6247

Framework versions

Transformers 4.21.1
Pytorch 1.12.0+cu113
Datasets 2.4.0
Tokenizers 0.12.1

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	24.89
ARC (25-shot)	21.76
HellaSwag (10-shot)	27.52
MMLU (5-shot)	25.97
TruthfulQA (0-shot)	46.17
Winogrande (5-shot)	51.62
GSM8K (5-shot)	0.0
DROP (3-shot)	1.16