# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("dany0407/MLM_rotten_tomatoes")
model = AutoModelForMaskedLM.from_pretrained("dany0407/MLM_rotten_tomatoes")Quick Links
MLM_rotten_tomatoes
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.2232
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: 16
- seed: 42
- optimizer: Use OptimizerNames.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: 5
Training results
Framework versions
- Transformers 4.53.3
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.21.4
- Downloads last month
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Model tree for dany0407/MLM_rotten_tomatoes
Base model
google-bert/bert-base-cased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="dany0407/MLM_rotten_tomatoes")