# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("ArhamNaeem/fine_tuned_bert_model")
model = AutoModelForMaskedLM.from_pretrained("ArhamNaeem/fine_tuned_bert_model")Quick Links
fine_tuned_bert_model
This model is a fine-tuned version of dkleczek/bert-base-polish-uncased-v1 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: 2e-05
- train_batch_size: 16
- 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
Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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Model tree for ArhamNaeem/fine_tuned_bert_model
Base model
dkleczek/bert-base-polish-uncased-v1
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ArhamNaeem/fine_tuned_bert_model")