Instructions to use eunyounglee/degreemotion-bert-finetuning-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eunyounglee/degreemotion-bert-finetuning-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eunyounglee/degreemotion-bert-finetuning-2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eunyounglee/degreemotion-bert-finetuning-2") model = AutoModelForSequenceClassification.from_pretrained("eunyounglee/degreemotion-bert-finetuning-2") - Notebooks
- Google Colab
- Kaggle
degreemotion-bert-finetuning-2
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: 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: 15
Training results
Framework versions
- Transformers 4.32.1
- Pytorch 2.2.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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Model tree for eunyounglee/degreemotion-bert-finetuning-2
Base model
klue/bert-base