Instructions to use fnaen-15/imdb-sentiment-getstarted with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fnaen-15/imdb-sentiment-getstarted with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fnaen-15/imdb-sentiment-getstarted")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fnaen-15/imdb-sentiment-getstarted") model = AutoModelForSequenceClassification.from_pretrained("fnaen-15/imdb-sentiment-getstarted", device_map="auto") - Notebooks
- Google Colab
- Kaggle
imdb-sentiment-getstarted
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4013
- Accuracy: 0.89
- F1: 0.8904
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_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 188 | 0.3795 | 0.8867 | 0.8882 |
| No log | 2.0 | 376 | 0.4013 | 0.89 | 0.8904 |
Framework versions
- Transformers 5.16.1
- Pytorch 2.11.0+cu128
- Datasets 5.0.1
- Tokenizers 0.23.1
- Downloads last month
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Model tree for fnaen-15/imdb-sentiment-getstarted
Base model
google-bert/bert-base-uncased