Text Classification
Transformers
TensorBoard
Safetensors
modernbert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("exusiaiw/assignment6_model")
model = AutoModelForSequenceClassification.from_pretrained("exusiaiw/assignment6_model")Quick Links
assignment6_model
This model is a fine-tuned version of answerdotai/ModernBERT-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2158
- Accuracy: 0.9291
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: 128
- eval_batch_size: 128
- 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
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2429 | 1.0 | 4292 | 0.2069 | 0.9279 |
| 0.1565 | 2.0 | 8584 | 0.2158 | 0.9291 |
Framework versions
- Transformers 4.57.6
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for exusiaiw/assignment6_model
Base model
answerdotai/ModernBERT-large
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="exusiaiw/assignment6_model")