Text Classification
Transformers
TensorBoard
Safetensors
modernbert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("drcoool/modernbert-acceptance-classifier-final")
model = AutoModelForSequenceClassification.from_pretrained("drcoool/modernbert-acceptance-classifier-final")Quick Links
modernbert-acceptance-classifier-final
This model is a fine-tuned version of answerdotai/ModernBERT-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6494
- F1: 0.8156
- Precision: 0.8160
- Recall: 0.8157
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: 1.2448932804037876e-05
- train_batch_size: 10
- eval_batch_size: 8
- 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
- lr_scheduler_warmup_ratio: 0.17030843157226483
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall |
|---|---|---|---|---|---|---|
| 0.4105 | 1.0 | 1609 | 0.3929 | 0.8180 | 0.8180 | 0.8180 |
| 0.375 | 2.0 | 3218 | 0.4030 | 0.8222 | 0.8262 | 0.8229 |
| 0.185 | 3.0 | 4827 | 0.6494 | 0.8156 | 0.8160 | 0.8157 |
Framework versions
- Transformers 4.48.0.dev0
- Pytorch 2.2.2
- Datasets 3.1.0
- Tokenizers 0.21.0
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Model tree for drcoool/modernbert-acceptance-classifier-final
Base model
answerdotai/ModernBERT-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="drcoool/modernbert-acceptance-classifier-final")