Instructions to use Kafaite24/bart-large-mlsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kafaite24/bart-large-mlsum with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("summarization", model="Kafaite24/bart-large-mlsum")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Kafaite24/bart-large-mlsum") model = AutoModelForSeq2SeqLM.from_pretrained("Kafaite24/bart-large-mlsum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Kafaite24/bart-large-mlsum: direct link, hf CLI and curl.
- Browser
- Download file 1.17 kB
-
https://huggingface.co/Kafaite24/bart-large-mlsum/resolve/main/README.md
- Command line
-
hf download hf://Kafaite24/bart-large-mlsum/README.md
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curl -L -H "Authorization: Bearer $HF_TOKEN" -o README.md https://huggingface.co/Kafaite24/bart-large-mlsum/resolve/main/README.md
1.17 kB
metadata
license: mit
tags:
- generated_from_trainer
datasets:
- mlsum
model-index:
- name: bart-large-mlsum
results: []
metrics:
- rouge
- bertscore
pipeline_tag: summarization
bart-large-mlsum
This model is a fine-tuned version of Kafaite24/bart-large-mlsum on the mlsum 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: 3e-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
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
- label_smoothing_factor: 0.1
Training results
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2