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postbot
/
t5-base-kw2email-v4

Text2Text Generation
Transformers
PyTorch
t5
Generated from Trainer
email generation
email
Inference Endpoints
text-generation-inference
Model card Files Community
1
t5-base-kw2email-v4
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---
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 need talk with you important stuff dear jonathan status war in Syria"
  example_title: "war status"
- 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: "pct1_dropremainder rounding may truncate the last examples in a dataset if the number of examples in your dataset don’t divide evenly by 100 dear bob"
  example_title: "pct1_dropremainder"
- 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 dear reginald"
  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: 5.5
  length_penalty: 0.9
---
# t5-base-kw2email-v4


This version **improves on prior "base" versions** by using training hyperparameters more closely aligned with [bigscience/T0](https://huggingface.co/bigscience/T0)

This model is a fine-tuned version of [pszemraj/t5-base-kw2email-v3.5](https://huggingface.co/pszemraj/t5-base-kw2email-v3.5) 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: 0.001
- train_batch_size: 8
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2

### Training results



### Framework versions

- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1