# 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

{"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\u2019t 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"}
transformers pytorch t5 text2text-generation generated_from_trainer email generation email dataset:aeslc dataset:postbot/multi_emails_kw base_model:pszemraj/t5-base-kw2email-v3.5 license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #email generation #email #dataset-aeslc #dataset-postbot/multi_emails_kw #base_model-pszemraj/t5-base-kw2email-v3.5 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

# t5-base-kw2email-v4


This version improves on prior "base" versions by using training hyperparameters more closely aligned with bigscience/T0

This model is a fine-tuned version of 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

# t5-base-kw2email-v4


This version improves on prior "base" versions by using training hyperparameters more closely aligned with bigscience/T0

This model is a fine-tuned version of 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
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #email generation #email #dataset-aeslc #dataset-postbot/multi_emails_kw #base_model-pszemraj/t5-base-kw2email-v3.5 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # t5-base-kw2email-v4


This version improves on prior "base" versions by using training hyperparameters more closely aligned with bigscience/T0

This model is a fine-tuned version of 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
passage: TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #email generation #email #dataset-aeslc #dataset-postbot/multi_emails_kw #base_model-pszemraj/t5-base-kw2email-v3.5 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# t5-base-kw2email-v4


This version improves on prior "base" versions by using training hyperparameters more closely aligned with bigscience/T0

This model is a fine-tuned version of 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