Text Classification
Transformers
TensorBoard
Safetensors
deberta-v2
trl
reward-trainer
Generated from Trainer
text-embeddings-inference
Instructions to use SiMajid/deberta_value with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SiMajid/deberta_value with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SiMajid/deberta_value")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SiMajid/deberta_value") model = AutoModelForSequenceClassification.from_pretrained("SiMajid/deberta_value") - Notebooks
- Google Colab
- Kaggle
deberta_value
This model is a fine-tuned version of microsoft/deberta-v3-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: 1.41e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7.0
Training results
Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
- Downloads last month
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Model tree for SiMajid/deberta_value
Base model
microsoft/deberta-v3-base