Text Classification
Transformers
Safetensors
modernbert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Haakim/imdb-modernbert")
model = AutoModelForSequenceClassification.from_pretrained("Haakim/imdb-modernbert")Quick Links
imdb-modernbert
This model is a fine-tuned version of answerdotai/ModernBERT-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1728
- Accuracy: 0.957
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: 32
- 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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.1520 | 1.0 | 1563 | 0.1251 | 0.9548 |
| 0.0759 | 2.0 | 3126 | 0.1728 | 0.957 |
Framework versions
- Transformers 5.13.0
- Pytorch 2.11.0+cu128
- Datasets 5.0.0
- Tokenizers 0.22.2
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Model tree for Haakim/imdb-modernbert
Base model
answerdotai/ModernBERT-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Haakim/imdb-modernbert")