Transformers
PyTorch
Safetensors
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use egorishti/email-summarization-model-t5-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use egorishti/email-summarization-model-t5-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("egorishti/email-summarization-model-t5-v2") model = AutoModelForSeq2SeqLM.from_pretrained("egorishti/email-summarization-model-t5-v2") - Notebooks
- Google Colab
- Kaggle
output-test
This model is a fine-tuned version of t5-base 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: 0.0003
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.5
- training_steps: 200
Training results
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
- Downloads last month
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Model tree for egorishti/email-summarization-model-t5-v2
Base model
google-t5/t5-base