| | --- |
| | 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 |
| | base_model: pszemraj/t5-base-kw2email-v3.5 |
| | --- |
| | # 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 |
| | |
