Text Classification
Transformers
TensorBoard
Safetensors
modernbert
Generated from Trainer
text-embeddings-inference
Instructions to use bulkbeings/modernbert-suicidal-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bulkbeings/modernbert-suicidal-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bulkbeings/modernbert-suicidal-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bulkbeings/modernbert-suicidal-classification") model = AutoModelForSequenceClassification.from_pretrained("bulkbeings/modernbert-suicidal-classification") - Notebooks
- Google Colab
- Kaggle
modernbert-classif
This model is a fine-tuned version of answerdotai/ModernBERT-large on an unknown dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.6874
- eval_f1: 0.8824
- eval_runtime: 41.2596
- eval_samples_per_second: 277.875
- eval_steps_per_second: 17.378
- epoch: 4.0
- step: 2868
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: 5e-05
- train_batch_size: 64
- 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: 12
Framework versions
- Transformers 4.48.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.21.0
- Downloads last month
- 3
Model tree for bulkbeings/modernbert-suicidal-classification
Base model
answerdotai/ModernBERT-large