| --- |
| 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 |
| |
