Instructions to use Ayham/robertagpt2_xsum4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ayham/robertagpt2_xsum4 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Ayham/robertagpt2_xsum4") model = AutoModelForSeq2SeqLM.from_pretrained("Ayham/robertagpt2_xsum4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from Ayham/robertagpt2_xsum4: direct link, hf CLI and curl.
- Browser
- Download file 1.05 kB
-
https://huggingface.co/Ayham/robertagpt2_xsum4/resolve/refs%2Fpr%2F1/README.md
- Command line
-
hf download hf://Ayham/robertagpt2_xsum4@refs/pr/1/README.md
-
curl -L -o README.md https://huggingface.co/Ayham/robertagpt2_xsum4/resolve/refs%2Fpr%2F1/README.md
1.05 kB
metadata
tags:
- generated_from_trainer
model-index:
- name: robertagpt2_xsum4
results: []
robertagpt2_xsum4
This model is a fine-tuned version of 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.13.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3