distilgpt2-emailgen: V2
Why write the rest of your email when you can generate it?
from transformers import pipeline
model_tag = "postbot/distilgpt2-emailgen-V2"
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'])
Model description
This model is a fine-tuned version of distilgpt2
on the postbot/multi-emails-100k dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9126
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters (run 1/2)
TODO
Training hyperparameters (run 2/2)
The following hyperparameters were used during training:
- learning_rate: 0.0006
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 4
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
1.9045 | 1.0 | 789 | 2.0006 |
1.8115 | 2.0 | 1578 | 1.9557 |
1.8501 | 3.0 | 2367 | 1.9110 |
1.7376 | 4.0 | 3156 | 1.9126 |
Framework versions
- Transformers 4.22.2
- Pytorch 1.10.0+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 24.59 |
ARC (25-shot) | 20.99 |
HellaSwag (10-shot) | 26.78 |
MMLU (5-shot) | 25.53 |
TruthfulQA (0-shot) | 46.51 |
Winogrande (5-shot) | 52.01 |
GSM8K (5-shot) | 0.0 |
DROP (3-shot) | 0.31 |
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