Text Classification
Transformers
Safetensors
modernbert
Generated from Trainer
text-embeddings-inference
Instructions to use evamxb/aiss-modern-bert-combined with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use evamxb/aiss-modern-bert-combined with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="evamxb/aiss-modern-bert-combined")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("evamxb/aiss-modern-bert-combined") model = AutoModelForSequenceClassification.from_pretrained("evamxb/aiss-modern-bert-combined", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from evamxb/aiss-modern-bert-combined: direct link, hf CLI and curl.
- Browser
- Download file 1.25 kB
-
https://huggingface.co/evamxb/aiss-modern-bert-combined/resolve/main/README.md
- Command line
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hf download hf://evamxb/aiss-modern-bert-combined/README.md
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curl -L -o README.md https://huggingface.co/evamxb/aiss-modern-bert-combined/resolve/main/README.md
1.25 kB
metadata
library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-base
tags:
- generated_from_trainer
model-index:
- name: aiss-modern-bert-combined
results: []
aiss-modern-bert-combined
This model is a fine-tuned version of answerdotai/ModernBERT-base on the None 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 12
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
Training results
Framework versions
- Transformers 4.57.6
- Pytorch 2.13.0+cu130
- Datasets 4.8.5
- Tokenizers 0.22.2