Transformers
TensorBoard
Safetensors
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use Mu7annad/t5-samsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mu7annad/t5-samsum with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Mu7annad/t5-samsum") model = AutoModelForSeq2SeqLM.from_pretrained("Mu7annad/t5-samsum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from Mu7annad/t5-samsum: direct link, hf CLI and curl.
- Browser
- Download file 1.11 kB
-
https://huggingface.co/Mu7annad/t5-samsum/resolve/main/README.md
- Command line
-
hf download hf://Mu7annad/t5-samsum/README.md
-
curl -L -o README.md https://huggingface.co/Mu7annad/t5-samsum/resolve/main/README.md
1.11 kB
metadata
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
model-index:
- name: t5-samsum
results: []
t5-samsum
This model is a fine-tuned version of google/flan-t5-base on an unknown 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: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1