Instructions to use Aadithyak/deberta-final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aadithyak/deberta-final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Aadithyak/deberta-final")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Aadithyak/deberta-final") model = AutoModelForSequenceClassification.from_pretrained("Aadithyak/deberta-final", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Aadithyak/deberta-final: direct link, hf CLI and curl.
- Browser
- Download file 1.43 kB
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https://huggingface.co/Aadithyak/deberta-final/resolve/main/README.md
- Command line
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hf download hf://Aadithyak/deberta-final/README.md
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curl -L -o README.md https://huggingface.co/Aadithyak/deberta-final/resolve/main/README.md
1.43 kB
metadata
library_name: transformers
license: mit
base_model: microsoft/deberta-large
tags:
- generated_from_trainer
model-index:
- name: deberta-final
results: []
deberta-final
This model is a fine-tuned version of microsoft/deberta-large on an unknown dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.4335
- eval_accuracy: 0.8762
- eval_f1: 0.8762
- eval_runtime: 1227.0064
- eval_samples_per_second: 85.273
- eval_steps_per_second: 2.843
- epoch: 2.6816
- step: 26500
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: 30
- eval_batch_size: 30
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 60
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 600
- num_epochs: 7
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0
- Datasets 3.0.0
- Tokenizers 0.19.1