# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("fill-mask", model="AnonymousCS/populism_multilingual_bert_uncased")# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("AnonymousCS/populism_multilingual_bert_uncased")
model = AutoModelForMaskedLM.from_pretrained("AnonymousCS/populism_multilingual_bert_uncased")Quick Links
populism_multilingual_bert_uncased
This model is a fine-tuned version of google-bert/bert-base-multilingual-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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
Framework versions
- Transformers 4.48.0.dev0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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Base model
google-bert/bert-base-multilingual-uncased
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