If you like the idea of wasting less time on emails, further work on this topic can be found on this hf org page
Why write the rest of your email when you can generate it?
from transformers import pipeline model_tag = "pszemraj/opt-350m-email-generation" generator = pipeline( 'text-generation', model=model_tag, use_fast=False, do_sample=False, early_stopping=True, ) prompt = """ Hello, Following up on the bubblegum shipment.""" generator( prompt, max_length=64, ) # generate
- Link to notebook on Colab
For this model, formatting matters. The results may be (significantly) different between the structure outlined above and
prompt = "Hey, just wanted to ..."etc.
- This model is a fine-tuned version of facebook/opt-350m on the aeslc dataset for six epochs.
- Emails, phone numbers, etc., were attempted to be excluded in a dataset preparation step using clean-text in Python.
- Note that API is restricted to generating 64 tokens - you can generate longer emails by using this in a text-generation
- in their everlasting wisdom, Facebook/Meta has decided to make a custom license for this, specifying several things. See facebook/opt-350m for details.
email_bodyfield of train + validation (get more data) from the aeslc dataset.
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: 16
- 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: 0.03
- num_epochs: 6
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1
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