Instructions to use Yotta/XpCoDir2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Yotta/XpCoDir2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Yotta/XpCoDir2")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Yotta/XpCoDir2") model = AutoModel.from_pretrained("Yotta/XpCoDir2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
XpCoDir2
This model is a fine-tuned version of bert-base-uncased on the XpCoDataset 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
- num_epochs: 3.0
Framework versions
- Transformers 4.16.2
- Pytorch 1.9.0
- Datasets 2.0.0
- Tokenizers 0.10.3
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