Text Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("rockerritesh/deberta-v3-small-askdocs")
model = AutoModelForSequenceClassification.from_pretrained("rockerritesh/deberta-v3-small-askdocs")Quick Links
deberta-v3-small-askdocs
This model is a fine-tuned version of microsoft/deberta-v3-small 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: 3e-05
- train_batch_size: 18
- eval_batch_size: 18
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
- Downloads last month
- 6
Model tree for rockerritesh/deberta-v3-small-askdocs
Base model
microsoft/deberta-v3-small
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rockerritesh/deberta-v3-small-askdocs")