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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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- email generation |
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- email |
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datasets: |
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- aeslc |
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- postbot/multi_emails |
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widget: |
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- 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." |
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example_title: "newsletter" |
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- text: "Hi <NAME>,\n\nI hope this email finds you well. Let me start by saying that I am a big fan of your work." |
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example_title: "fan" |
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- text: "Greetings <NAME>,\n\nI hope you had a splendid evening at the Company sausage eating festival. I am reaching out because" |
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example_title: "festival" |
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- text: "Good Morning <NAME>,\n\nI was just thinking to myself about how much I love creating value" |
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example_title: "value" |
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- text: "URGENT - I need the TPS reports" |
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example_title: "URGENT" |
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- text: "Hi <NAME>,\n\nI hope this email finds you extremely well." |
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example_title: "emails that find you" |
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parameters: |
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min_length: 4 |
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max_length: 96 |
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length_penalty: 0.7 |
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no_repeat_ngram_size: 3 |
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do_sample: False |
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num_beams: 4 |
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early_stopping: True |
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repetition_penalty: 4.5 |
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--- |
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# distilgpt2-emailgen |
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Why write the rest of your email when you can generate it? |
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```python |
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from transformers import pipeline |
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model_tag = "postbot/distilgpt2-emailgen" |
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generator = pipeline( |
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'text-generation', |
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model=model_tag, |
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do_sample=False, |
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early_stopping=True, |
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) |
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prompt = """ |
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Hello, |
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Following up on the bubblegum shipment.""" |
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generator( |
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prompt, |
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max_length=64, |
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) # generate |
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``` |
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A script to use this on CPU/command line can be found [here](https://gist.github.com/pszemraj/c1b0a76445418b6bbddd5f9633d1bb7f) :) |
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> For this model, formatting matters. The results may be (significantly) different between the structure outlined above and `prompt = "Hey, just wanted to ..."` etc. |
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## Model description |
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This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on a dataset of 50k emails, including the classic `aeslc` dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.6247 |
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## Intended uses & limitations |
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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, as at least *some* input is required to guide the direction of the model. |
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Please verify any suggestions by the model for A) False claims and B) negation statements before accepting/sending something. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 6e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 32 |
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- total_train_batch_size: 256 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.02 |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 2.8299 | 1.0 | 248 | 2.7971 | |
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| 2.6984 | 2.0 | 496 | 2.6826 | |
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| 2.7022 | 3.0 | 744 | 2.6361 | |
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| 2.6436 | 4.0 | 992 | 2.6245 | |
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| 2.6195 | 5.0 | 1240 | 2.6247 | |
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### Framework versions |
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- Transformers 4.21.1 |
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- Pytorch 1.12.0+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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