Instructions to use whu9/multi_doc_sum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use whu9/multi_doc_sum with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, NetSum tokenizer = AutoTokenizer.from_pretrained("whu9/multi_doc_sum") model = NetSum.from_pretrained("whu9/multi_doc_sum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from whu9/multi_doc_sum: direct link, hf CLI and curl.
- Browser
- Download file 1.04 kB
-
https://huggingface.co/whu9/multi_doc_sum/resolve/main/README.md
- Command line
-
hf download hf://whu9/multi_doc_sum/README.md
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curl -L -o README.md https://huggingface.co/whu9/multi_doc_sum/resolve/main/README.md
1.04 kB
metadata
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: multi_doc_sum
results: []
multi_doc_sum
This model is a fine-tuned version of on the cnn_dailymail 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.17.0
- Pytorch 1.10.2+cu113
- Datasets 2.10.1
- Tokenizers 0.13.0