Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("OliverHeine/roberta-base_fold_1")
model = AutoModelForSequenceClassification.from_pretrained("OliverHeine/roberta-base_fold_1")Quick Links
roberta-base_fold_1
This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0365
- Accuracy: 0.9933
- F1: 0.9876
- Precision: 0.9964
- Recall: 0.9789
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: 38
- eval_batch_size: 38
- 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
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.0432 | 1.0 | 3983 | 0.0559 | 0.9870 | 0.9762 | 0.9681 | 0.9844 |
| 0.0353 | 2.0 | 7966 | 0.0340 | 0.9918 | 0.9848 | 0.9864 | 0.9833 |
| 0.0294 | 3.0 | 11949 | 0.0365 | 0.9933 | 0.9876 | 0.9964 | 0.9789 |
Framework versions
- Transformers 5.3.0
- Pytorch 2.10.0+cu128
- Datasets 4.6.1
- Tokenizers 0.22.2
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Model tree for OliverHeine/roberta-base_fold_1
Base model
FacebookAI/roberta-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="OliverHeine/roberta-base_fold_1")