Text Classification
Transformers
TensorBoard
Safetensors
modernbert
Generated from Trainer
text-embeddings-inference
Instructions to use Andesha/hello-yes-recall with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Andesha/hello-yes-recall with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Andesha/hello-yes-recall")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Andesha/hello-yes-recall") model = AutoModelForSequenceClassification.from_pretrained("Andesha/hello-yes-recall", device_map="auto") - Notebooks
- Google Colab
- Kaggle
hello-yes-recall
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: 0.8338
- F1: 0.6895
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: 32
- 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.9868 | 1.0 | 238 | 0.9431 | 0.5981 |
| 0.8795 | 2.0 | 476 | 0.8704 | 0.6700 |
| 0.7862 | 3.0 | 714 | 0.8379 | 0.6825 |
| 0.7416 | 4.0 | 952 | 0.8342 | 0.6878 |
| 0.7368 | 5.0 | 1190 | 0.8338 | 0.6895 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.7.1
- Datasets 4.3.0
- Tokenizers 0.22.1
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Model tree for Andesha/hello-yes-recall
Base model
answerdotai/ModernBERT-base