Instructions to use HamdanXI/bert-base-uncased-paradetox-1Token-Split-MASK with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HamdanXI/bert-base-uncased-paradetox-1Token-Split-MASK with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="HamdanXI/bert-base-uncased-paradetox-1Token-Split-MASK")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("HamdanXI/bert-base-uncased-paradetox-1Token-Split-MASK") model = AutoModelForMaskedLM.from_pretrained("HamdanXI/bert-base-uncased-paradetox-1Token-Split-MASK") - Notebooks
- Google Colab
- Kaggle
bert-base-uncased-paradetox-1Token-Split-MASK
This model is a fine-tuned version of bert-base-uncased on an unknown 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
Training results
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
- Downloads last month
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Model tree for HamdanXI/bert-base-uncased-paradetox-1Token-Split-MASK
Base model
google-bert/bert-base-uncased