Instructions to use Samavia/prompts_summarization_model_trained_on_reduced_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Samavia/prompts_summarization_model_trained_on_reduced_data with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Samavia/prompts_summarization_model_trained_on_reduced_data") model = AutoModelForSeq2SeqLM.from_pretrained("Samavia/prompts_summarization_model_trained_on_reduced_data") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: transformers
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
model-index:
- name: results
results: []
results
This model is a fine-tuned version of facebook/bart-large-cnn 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: 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
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1