

# opt for email generation - 350M

> If you like the idea of wasting less time on emails, further work on this topic can be found [on this hf org page](https://huggingface.co/postbot)

Why write the rest of your email when you can generate it?

```python
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](https://colab.research.google.com/gist/pszemraj/40c46deed730bfca553b8c4b257a7b77/email-autocomplete-demo.ipynb) 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.

## Model description

- This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the [aeslc](https://huggingface.co/datasets/aeslc) dataset for six epochs. 
- Emails, phone numbers, etc., were attempted to be excluded in a dataset preparation step using [clean-text](https://pypi.org/project/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 `pipeline` object

## Intended uses & limitations

- in their everlasting wisdom, Facebook/Meta has decided to make a custom license for this, specifying several things. See [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) for details.

## Training and evaluation data

- the `email_body` field of train + validation (get more data) from the [aeslc](https://huggingface.co/datasets/aeslc) dataset.

## Training procedure

### Training hyperparameters

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

### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1

{"license": "other", "tags": ["generated_from_trainer", "opt", "custom-license", "no-commercial", "email", "auto-complete"], "datasets": ["aeslc"], "widget": [{"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. Let me start by saying that I am a big fan of your work.", "example_title": "fan"}, {"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 <NAME>,\n\nI was just thinking to myself about how much I love creating value", "example_title": "value"}, {"text": "URGENT - I need", "example_title": "URGENT"}], "inference": {"parameters": {"min_length": 4, "max_length": 64, "length_penalty": 0.7, "no_repeat_ngram_size": 3, "do_sample": false, "num_beams": 4, "early_stopping": true, "repetition_penalty": 3.5}}}
transformers pytorch safetensors opt text-generation generated_from_trainer custom-license no-commercial email auto-complete dataset:aeslc license:other autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #opt #text-generation #generated_from_trainer #custom-license #no-commercial #email #auto-complete #dataset-aeslc #license-other #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 



# opt for email generation - 350M

> 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?


- 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.

## Model description

- 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 'pipeline' object

## Intended uses & limitations

- in their everlasting wisdom, Facebook/Meta has decided to make a custom license for this, specifying several things. See facebook/opt-350m for details.

## Training and evaluation data

- the 'email_body' field of train + validation (get more data) from the aeslc dataset.

## Training procedure

### Training hyperparameters

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

### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1

# opt for email generation - 350M

> 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?


- 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. ## Model description

- 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 'pipeline' object ## Intended uses & limitations

- in their everlasting wisdom, Facebook/Meta has decided to make a custom license for this, specifying several things. See facebook/opt-350m for details. ## Training and evaluation data

- the 'email_body' field of train + validation (get more data) from the aeslc dataset. ## Training procedure ### Training hyperparameters

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 ### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #safetensors #opt #text-generation #generated_from_trainer #custom-license #no-commercial #email #auto-complete #dataset-aeslc #license-other #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 # opt for email generation - 350M

> 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?


- 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. ## Model description

- 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 'pipeline' object ## Intended uses & limitations

- in their everlasting wisdom, Facebook/Meta has decided to make a custom license for this, specifying several things. See facebook/opt-350m for details. ## Training and evaluation data

- the 'email_body' field of train + validation (get more data) from the aeslc dataset. ## Training procedure ### Training hyperparameters

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 ### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #safetensors #opt #text-generation #generated_from_trainer #custom-license #no-commercial #email #auto-complete #dataset-aeslc #license-other #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
# opt for email generation - 350M

> 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?


- 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.## Model description

- 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 'pipeline' object## Intended uses & limitations

- in their everlasting wisdom, Facebook/Meta has decided to make a custom license for this, specifying several things. See facebook/opt-350m for details.## Training and evaluation data

- the 'email_body' field of train + validation (get more data) from the aeslc dataset.## Training procedure