Text Classification
Transformers
Safetensors
modernbert
Generated from Trainer
text-embeddings-inference
Instructions to use regagent-ai/RiskClassificationModel_20260217_060204_092ad356 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use regagent-ai/RiskClassificationModel_20260217_060204_092ad356 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="regagent-ai/RiskClassificationModel_20260217_060204_092ad356")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("regagent-ai/RiskClassificationModel_20260217_060204_092ad356") model = AutoModelForSequenceClassification.from_pretrained("regagent-ai/RiskClassificationModel_20260217_060204_092ad356", device_map="auto") - Notebooks
- Google Colab
- Kaggle
RiskClassificationModel_20260217_060204_092ad356
This model is a fine-tuned version of regagent-ai/MultiLabel-Risk-Extended-Classifier-BERT-v1 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0092
- Micro F1: 1.0
- Macro F1: 0.75
- Weighted F1: 1.0
- Accuracy: 1.0
- Roc Auc: nan
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 with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Micro F1 | Macro F1 | Weighted F1 | Accuracy | Roc Auc |
|---|---|---|---|---|---|---|---|---|
| 0.0075 | 0.5682 | 50 | 0.0092 | 1.0 | 0.75 | 1.0 | 1.0 | nan |
Framework versions
- Transformers 4.57.6
- Pytorch 2.6.0+cu124
- Datasets 4.4.2
- Tokenizers 0.22.1
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