Summarization
Transformers
PyTorch
gpt2
text-generation
Generated from Trainer
text-generation-inference
Instructions to use gavin124/gpt2-finetuned-cnn-summarization-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gavin124/gpt2-finetuned-cnn-summarization-v1 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="gavin124/gpt2-finetuned-cnn-summarization-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("gavin124/gpt2-finetuned-cnn-summarization-v1") model = AutoModelForCausalLM.from_pretrained("gavin124/gpt2-finetuned-cnn-summarization-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
gpt2-finetuned-cnn-summarization-v1
This model is a fine-tuned version of gpt2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.1709
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: 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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.2025 | 1.0 | 5742 | 2.1636 |
| 2.0428 | 2.0 | 11484 | 2.1659 |
| 1.9681 | 3.0 | 17226 | 2.1709 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
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