Instructions to use eunyounglee/degree-bert-finetuning-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eunyounglee/degree-bert-finetuning-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eunyounglee/degree-bert-finetuning-1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eunyounglee/degree-bert-finetuning-1") model = AutoModelForSequenceClassification.from_pretrained("eunyounglee/degree-bert-finetuning-1") - Notebooks
- Google Colab
- Kaggle
degree-bert-finetuning-1
This model is a fine-tuned version of klue/bert-base on the None 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
Framework versions
- Transformers 4.38.1
- Pytorch 2.2.0
- Datasets 2.17.1
- Tokenizers 0.15.2
- Downloads last month
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Model tree for eunyounglee/degree-bert-finetuning-1
Base model
klue/bert-base