How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-classification", model="taskydata/deberta-v3-base_10xp3_10xc4_512")
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("taskydata/deberta-v3-base_10xp3_10xc4_512")
model = AutoModelForSequenceClassification.from_pretrained("taskydata/deberta-v3-base_10xp3_10xc4_512", device_map="auto")
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Hyperparameters:

  • learning rate: 2e-5
  • weight decay: 0.01
  • per_device_train_batch_size: 8
  • per_device_eval_batch_size: 8
  • gradient_accumulation_steps:1
  • eval steps: 6000
  • max_length: 512
  • num_epochs: 2

Dataset version:

  • “craffel/tasky_or_not”, “10xp3_10xc4”, “15f88c8”

Checkpoint:

  • 48000 steps

Results on Validation set:

Step Training Loss Validation Loss Accuracy Precision Recall F1
6000 0.031900 0.163412 0.982194 0.999211 0.980462 0.989748
12000 0.014700 0.106132 0.976666 0.999639 0.973733 0.986516
18000 0.010700 0.043012 0.995743 0.999223 0.995918 0.997568
24000 0.007400 0.095047 0.984724 0.999857 0.982714 0.991211
30000 0.004100 0.087274 0.990400 0.999829 0.989217 0.994495
36000 0.003100 0.162909 0.981972 1.000000 0.979434 0.989610
42000 0.002200 0.148721 0.980454 0.999986 0.977717 0.988726
48000 0.001000 0.094455 0.990437 0.999943 0.989147 0.994516
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Dataset used to train taskydata/deberta-v3-base_10xp3_10xc4_512