Text Classification
Transformers
Safetensors
modernbert
Generated from Trainer
text-embeddings-inference
Instructions to use rchrdwllm/BioClinical-ModernBERT-base-Symptom2Disease_WITHOUT-DROPOUT-123 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rchrdwllm/BioClinical-ModernBERT-base-Symptom2Disease_WITHOUT-DROPOUT-123 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rchrdwllm/BioClinical-ModernBERT-base-Symptom2Disease_WITHOUT-DROPOUT-123")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rchrdwllm/BioClinical-ModernBERT-base-Symptom2Disease_WITHOUT-DROPOUT-123") model = AutoModelForSequenceClassification.from_pretrained("rchrdwllm/BioClinical-ModernBERT-base-Symptom2Disease_WITHOUT-DROPOUT-123") - Notebooks
- Google Colab
- Kaggle
BioClinical-ModernBERT-base-Symptom2Disease_WITHOUT-DROPOUT-123
This model is a fine-tuned version of thomas-sounack/BioClinical-ModernBERT-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3844
- Accuracy: 1.0
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: 6.840039921359585e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 123
- 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: cosine
- lr_scheduler_warmup_steps: 5
- num_epochs: 8
- label_smoothing_factor: 0.1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.2514 | 1.0 | 6 | 0.6219 | 0.9545 |
| 0.5759 | 2.0 | 12 | 0.5212 | 0.9545 |
| 0.4414 | 3.0 | 18 | 0.4739 | 0.9318 |
| 0.3855 | 4.0 | 24 | 0.4289 | 0.9545 |
| 0.3685 | 5.0 | 30 | 0.3981 | 1.0 |
| 0.3583 | 6.0 | 36 | 0.3917 | 1.0 |
| 0.3561 | 7.0 | 42 | 0.3852 | 1.0 |
| 0.3542 | 8.0 | 48 | 0.3844 | 1.0 |
Framework versions
- Transformers 4.56.2
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
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Model tree for rchrdwllm/BioClinical-ModernBERT-base-Symptom2Disease_WITHOUT-DROPOUT-123
Base model
answerdotai/ModernBERT-base