Text Classification
Transformers
Safetensors
modernbert
Generated from Trainer
text-embeddings-inference
Instructions to use mhr2004/ModernBERT-base-atomic.train.no.negation.true.irrelevant1e-06-64-stsb-lr2e-05-bs32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mhr2004/ModernBERT-base-atomic.train.no.negation.true.irrelevant1e-06-64-stsb-lr2e-05-bs32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mhr2004/ModernBERT-base-atomic.train.no.negation.true.irrelevant1e-06-64-stsb-lr2e-05-bs32")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mhr2004/ModernBERT-base-atomic.train.no.negation.true.irrelevant1e-06-64-stsb-lr2e-05-bs32") model = AutoModelForSequenceClassification.from_pretrained("mhr2004/ModernBERT-base-atomic.train.no.negation.true.irrelevant1e-06-64-stsb-lr2e-05-bs32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
ModernBERT-base-atomic.train.no.negation.true.irrelevant1e-06-64-stsb-lr2e-05-bs32
This model is a fine-tuned version of mhr2004/answerdotai-ModernBERT-basezijiewang-atomic.train.no.negation.true.irrelevant1e-06-64 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0182
- Pearson: 0.8940
- Spearman: 0.8930
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use 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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Pearson | Spearman |
|---|---|---|---|---|---|
| 0.0298 | 1.0 | 180 | 0.0329 | 0.8709 | 0.8719 |
| 0.0167 | 2.0 | 360 | 0.0206 | 0.8918 | 0.8916 |
| 0.0135 | 3.0 | 540 | 0.0182 | 0.8948 | 0.8940 |
| 0.0084 | 4.0 | 720 | 0.0182 | 0.8940 | 0.8930 |
Framework versions
- Transformers 4.49.0
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
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