Text Classification
Transformers
Safetensors
modernbert
Generated from Trainer
text-embeddings-inference
Instructions to use PracticalWork/ModernBERT-large-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PracticalWork/ModernBERT-large-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="PracticalWork/ModernBERT-large-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("PracticalWork/ModernBERT-large-classifier") model = AutoModelForSequenceClassification.from_pretrained("PracticalWork/ModernBERT-large-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from PracticalWork/ModernBERT-large-classifier: direct link, hf CLI and curl.
- Browser
- Download file 1.69 kB
-
https://huggingface.co/PracticalWork/ModernBERT-large-classifier/resolve/main/README.md
- Command line
-
hf download hf://PracticalWork/ModernBERT-large-classifier/README.md
-
curl -L -o README.md https://huggingface.co/PracticalWork/ModernBERT-large-classifier/resolve/main/README.md
1.69 kB
metadata
library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-large
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: ModernBERT-large-classifier
results: []
ModernBERT-large-classifier
This model is a fine-tuned version of answerdotai/ModernBERT-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3071
- Accuracy: 0.9001
- F1: 0.8191
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 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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 0 | 0 | 0.7754 | 0.4796 | 0.5011 |
| No log | 0.6006 | 188 | 0.3604 | 0.8633 | 0.7299 |
| No log | 1.2013 | 376 | 0.2724 | 0.8897 | 0.7844 |
| 0.2959 | 1.8019 | 564 | 0.2549 | 0.9073 | 0.8343 |
| 0.2959 | 2.4026 | 752 | 0.3005 | 0.9033 | 0.8274 |
Framework versions
- Transformers 4.53.3
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.2