---
license: apache-2.0
tags:
- generated_from_trainer
- email generation
- email
datasets:
- aeslc
- postbot/multi_emails_kw
widget:
- text: "Thursday pay invoice need asap thanks Pierre good morning dear Harold"
  example_title: "invoice"
- text: "dear elia when will space be ready need urgently regards ronald"
  example_title: "space ready"
- text: "Tuesday I need review document before leaves our company need know when leave"
  example_title: "review document"
- text: "dear bob will back wednesday need urgently regards elena"
  example_title: "return wednesday"
- text: "dear mary thanks for your last invoice need know when payment be"
  example_title: "last invoice"
- text: "dear william I out yesterday received message today will get back today"
  example_title: "message"
- text: "dear joseph have all invoices ready Monday next invoice in 30 days have great weekend"
  example_title: "next invoice"
- text: "dear mary I have couple questions on new contract we agreed on need know thoughts regarding contract"
  example_title: "contract"
- text: "Friday will make report due soon please thanks dear john"
  example_title: "report due soon"
- text: "need take photos sunday want finish thursday photo exhibition need urgent help thanks dear john"
  example_title: "photo exhibition"
- text: "Tuesday need talk with you important stuff"
  example_title: "important talk"
- text: "dear maria how are you doing thanks very much"
  example_title: "thanks"
- text: "dear james tomorrow will prepare file for june report before leave need know when leave"
  example_title: "file for june report"
  
parameters:
  min_length: 16
  max_length: 256
  no_repeat_ngram_size: 2
  do_sample: False
  num_beams: 8
  early_stopping: True
  repetition_penalty: 2.5
  length_penalty: 0.9
---


# t5-small-kw2email-v2

This model is a fine-tuned version of [postbot/t5-small-kw2email](https://huggingface.co/postbot/t5-small-kw2email) on the None dataset.

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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



### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1