Text Classification
Transformers
Safetensors
modernbert
Generated from Trainer
text-embeddings-inference
Instructions to use ntr2026/analysis-reviews with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ntr2026/analysis-reviews with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ntr2026/analysis-reviews")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ntr2026/analysis-reviews") model = AutoModelForSequenceClassification.from_pretrained("ntr2026/analysis-reviews") - Notebooks
- Google Colab
- Kaggle
analysis-reviews
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: 1.0765
- Accuracy: 0.8940
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_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 | Accuracy |
|---|---|---|---|---|
| 0.3523 | 1.0 | 1067 | 0.3494 | 0.8884 |
| 0.2005 | 2.0 | 2134 | 0.4926 | 0.8884 |
| 0.0693 | 3.0 | 3201 | 0.7751 | 0.8846 |
| 0.0102 | 4.0 | 4268 | 0.9839 | 0.8874 |
| 0.0000 | 5.0 | 5335 | 1.0765 | 0.8940 |
Framework versions
- Transformers 5.6.2
- Pytorch 2.10.0+cu128
- Datasets 4.8.4
- Tokenizers 0.22.2
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Model tree for ntr2026/analysis-reviews
Base model
answerdotai/ModernBERT-base