Text Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("voroninip/session-classifier")
model = AutoModelForSequenceClassification.from_pretrained("voroninip/session-classifier")Quick Links
session-classifier
This model is a fine-tuned version of microsoft/deberta-v3-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.0067
- Accuracy: 0.7914
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: 128
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 384
- optimizer: Use OptimizerNames.ADAFACTOR and the args are: No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 5
Training results
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
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Model tree for voroninip/session-classifier
Base model
microsoft/deberta-v3-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="voroninip/session-classifier")