Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use outspark/news-bert-binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use outspark/news-bert-binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="outspark/news-bert-binary")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("outspark/news-bert-binary") model = AutoModelForSequenceClassification.from_pretrained("outspark/news-bert-binary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from outspark/news-bert-binary: direct link, hf CLI and curl.
- Browser
- Download file 1.17 kB
-
https://huggingface.co/outspark/news-bert-binary/resolve/main/README.md
- Command line
-
hf download hf://outspark/news-bert-binary/README.md
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curl -L -o README.md https://huggingface.co/outspark/news-bert-binary/resolve/main/README.md
1.17 kB
metadata
library_name: transformers
license: cc-by-sa-4.0
base_model: klue/bert-base
tags:
- generated_from_trainer
model-index:
- name: news-bert-binary
results: []
news-bert-binary
This model is a fine-tuned version of klue/bert-base on an unknown dataset.
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: 16
- eval_batch_size: 8
- 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: 1
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1