Instructions to use Ayham/bert_gpt2_summarization_cnndm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ayham/bert_gpt2_summarization_cnndm with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Ayham/bert_gpt2_summarization_cnndm") model = AutoModelForSeq2SeqLM.from_pretrained("Ayham/bert_gpt2_summarization_cnndm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Ayham/bert_gpt2_summarization_cnndm: direct link, hf CLI and curl.
- Browser
- Download file 1.11 kB
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https://huggingface.co/Ayham/bert_gpt2_summarization_cnndm/resolve/main/README.md
- Command line
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hf download hf://Ayham/bert_gpt2_summarization_cnndm/README.md
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curl -L -o README.md https://huggingface.co/Ayham/bert_gpt2_summarization_cnndm/resolve/main/README.md
1.11 kB
metadata
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: bert_gpt2_summarization_cnndm
results: []
bert_gpt2_summarization_cnndm
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: 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
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3