Transformers
TensorBoard
Safetensors
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use chamdentimem/ViT5_Dialogue_Summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chamdentimem/ViT5_Dialogue_Summarization with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("chamdentimem/ViT5_Dialogue_Summarization") model = AutoModelForSeq2SeqLM.from_pretrained("chamdentimem/ViT5_Dialogue_Summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from chamdentimem/ViT5_Dialogue_Summarization: direct link, hf CLI and curl.
- Browser
- Download file 1.56 kB
-
https://huggingface.co/chamdentimem/ViT5_Dialogue_Summarization/resolve/main/README.md
- Command line
-
hf download hf://chamdentimem/ViT5_Dialogue_Summarization/README.md
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curl -L -o README.md https://huggingface.co/chamdentimem/ViT5_Dialogue_Summarization/resolve/main/README.md
1.56 kB
metadata
license: mit
base_model: VietAI/vit5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: ViT5_Dialogue_Summarization
results: []
ViT5_Dialogue_Summarization
This model is a fine-tuned version of VietAI/vit5-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6377
- Rouge1: 51.4583
- Rouge2: 28.5387
- Rougel: 40.9957
- Rougelsum: 45.4297
- Gen Len: 16.1661
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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 0.5898 | 1.0 | 7366 | 0.6377 | 51.4583 | 28.5387 | 40.9957 | 45.4297 | 16.1661 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2