|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
- distilgpt2 |
|
- email generation |
|
- email |
|
datasets: |
|
- aeslc |
|
- postbot/multi-emails-100k |
|
|
|
widget: |
|
- text: "Good Morning Professor Beans, |
|
|
|
Hope you are doing well. I just wanted to reach out and ask if differential calculus will be on the exam" |
|
example_title: "email to prof" |
|
- text: "Hey <NAME>,\n\nThank you for signing up for my weekly newsletter. Before we get started, you'll have to confirm your email address." |
|
example_title: "newsletter" |
|
- text: "Hi <NAME>,\n\nI hope this email finds you well. I wanted to reach out and ask about office hours" |
|
example_title: "office hours" |
|
- text: "Greetings <NAME>,\n\nI hope you had a splendid evening at the Company sausage eating festival. I am reaching out because" |
|
example_title: "festival" |
|
- text: "Good Morning Harold,\n\nI was wondering when the next" |
|
example_title: "event" |
|
- text: "URGENT - I need the TPS reports" |
|
example_title: "URGENT" |
|
- text: "Hi Archibald,\n\nI hope this email finds you extremely well." |
|
example_title: "emails that find you" |
|
- text: "Hello there.\n\nI just wanted to reach out and check in to" |
|
example_title: "checking in" |
|
- text: "Hello <NAME>,\n\nI hope this email finds you well. I wanted to reach out and see if you've enjoyed your time with us" |
|
example_title: "work well" |
|
- text: "Hi <NAME>,\n\nI hope this email finds you well. I wanted to reach out and see if we could catch up" |
|
example_title: "catch up" |
|
- text: "I'm <NAME> and I just moved into the area and wanted to reach out and get some details on where I could get groceries and" |
|
example_title: "grocery" |
|
parameters: |
|
min_length: 4 |
|
max_length: 128 |
|
length_penalty: 0.8 |
|
no_repeat_ngram_size: 2 |
|
do_sample: False |
|
num_beams: 8 |
|
early_stopping: True |
|
repetition_penalty: 5.5 |
|
--- |
|
|
|
|
|
# distilgpt2-emailgen: V2 |
|
|
|
|
|
[](https://colab.research.google.com/gist/pszemraj/d1c2d88b6120cca4ca7df078ea1d1e50/scratchpad.ipynb) |
|
|
|
Why write the rest of your email when you can generate it? |
|
|
|
```python |
|
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](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
|
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_postbot__distilgpt2-emailgen-V2) |
|
|
|
| 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 | |
|
|
