Text Classification
Transformers
Safetensors
modernbert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("ATL1978/modernbert-emotion")
model = AutoModelForSequenceClassification.from_pretrained("ATL1978/modernbert-emotion")Quick Links
modernbert-emotion
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: 0.2534
- Accuracy: 0.932
- F1 Macro: 0.8871
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 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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
|---|---|---|---|---|---|
| 0.2147 | 1.0 | 2000 | 0.1959 | 0.9325 | 0.9062 |
| 0.1252 | 2.0 | 4000 | 0.1597 | 0.934 | 0.9034 |
| 0.0573 | 3.0 | 6000 | 0.2126 | 0.939 | 0.9107 |
Framework versions
- Transformers 5.3.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.3
- Tokenizers 0.22.2
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Model tree for ATL1978/modernbert-emotion
Base model
answerdotai/ModernBERT-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ATL1978/modernbert-emotion")