Transformers
PyTorch
English
gpt2
Generated from Trainer
Eval Results (legacy)
text-generation-inference
Instructions to use KarelDO/gpt2.CEBaB_confounding.food_service_positive.absa.5-class.seed_43 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KarelDO/gpt2.CEBaB_confounding.food_service_positive.absa.5-class.seed_43 with Transformers:
# Load model directly from transformers import AutoTokenizer, GPT2ForNonlinearSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KarelDO/gpt2.CEBaB_confounding.food_service_positive.absa.5-class.seed_43") model = GPT2ForNonlinearSequenceClassification.from_pretrained("KarelDO/gpt2.CEBaB_confounding.food_service_positive.absa.5-class.seed_43") - Notebooks
- Google Colab
- Kaggle
gpt2.CEBaB_confounding.food_service_positive.absa.5-class.seed_43
This model is a fine-tuned version of gpt2 on the OpenTable OPENTABLE-ABSA dataset. It achieves the following results on the evaluation set:
- Loss: 0.8080
- Accuracy: 0.7689
- Macro-f1: 0.7651
- Weighted-macro-f1: 0.7692
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: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
Training results
Framework versions
- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 2.5.2
- Tokenizers 0.12.1
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Evaluation results
- Accuracy on OpenTable OPENTABLE-ABSAself-reported0.769