Instructions to use Nubletz/bart-text-simplification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nubletz/bart-text-simplification with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Nubletz/bart-text-simplification") model = AutoModelForSeq2SeqLM.from_pretrained("Nubletz/bart-text-simplification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Nubletz/bart-text-simplification: direct link, hf CLI and curl.
- Browser
- Download file 1.43 kB
-
https://huggingface.co/Nubletz/bart-text-simplification/resolve/main/README.md
- Command line
-
hf download hf://Nubletz/bart-text-simplification/README.md
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curl -L -o README.md https://huggingface.co/Nubletz/bart-text-simplification/resolve/main/README.md
1.43 kB
metadata
library_name: transformers
base_model:
- facebook/bart-large-cnn
tags:
- generated_from_trainer
model-index:
- name: bart-text-simplification
results: []
datasets:
- facebook/asset
- turk/simplification
language:
- en
metrics:
- sari
bart-text-simplification
Notes: the model is still under improvement
This model is a fine-tuned version of facebook/bart-large-cnn on ASSET and TurkCorpus dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.6558
- eval_model_preparation_time: 0.0082
- eval_sari: 59.0183
- eval_runtime: 670.1131
- eval_samples_per_second: 1.343
- eval_steps_per_second: 1.343
- step: 0
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: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Tokenizers 0.19.1