Instructions to use xiangmingming/imdb-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xiangmingming/imdb-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xiangmingming/imdb-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xiangmingming/imdb-bert") model = AutoModelForSequenceClassification.from_pretrained("xiangmingming/imdb-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
imdb-bert
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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: 1
Training results
Framework versions
- Transformers 4.57.1
- Pytorch 2.3.1
- Datasets 4.4.1
- Tokenizers 0.22.1
- Downloads last month
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Model tree for xiangmingming/imdb-bert
Base model
google-bert/bert-base-uncased