Instructions to use ZZ99/deberta-v3-large-tapt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZZ99/deberta-v3-large-tapt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ZZ99/deberta-v3-large-tapt")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ZZ99/deberta-v3-large-tapt") model = AutoModelForMaskedLM.from_pretrained("ZZ99/deberta-v3-large-tapt") - Notebooks
- Google Colab
- Kaggle
test-mlm
This model is a fine-tuned version of /root/autodl-tmp/nbme/tmp/test-mlm/deberta-v3-large-tapt on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.3251
- Accuracy: 0.7285
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: 1e-05
- train_batch_size: 4
- 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
Training results
Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
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