Summarization
Transformers
PyTorch
TensorBoard
Safetensors
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use chanifrusydi/t5-dialogue-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chanifrusydi/t5-dialogue-summarization with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="chanifrusydi/t5-dialogue-summarization")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("chanifrusydi/t5-dialogue-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("chanifrusydi/t5-dialogue-summarization") - Notebooks
- Google Colab
- Kaggle
t5-dialogue-summarization
This model is a fine-tuned version of t5-small on the samsum dataset.
dataset:
type: {summarization}
name: {samsum}
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
- Downloads last month
- 8
Model tree for chanifrusydi/t5-dialogue-summarization
Base model
google-t5/t5-small