# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("taskydata/deberta-v3-base_10xp3nirstbbflanse_5xc4")
model = AutoModelForSequenceClassification.from_pretrained("taskydata/deberta-v3-base_10xp3nirstbbflanse_5xc4")Quick Links
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: 24000
- max_length: 512
- num_epochs: 2
- hidden_dropout_prob: 0.3
- attention_probs_dropout_prob: 0.25
Dataset version:
- taskydata/10xp3nirstbbflanse_5xc4
Checkpoint:
- 48000 steps
Results on Validation set:
| Step | Training Loss | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|
| 24000 | 0.052000 | 0.071572 | 0.988261 | 0.999752 | 0.987852 | 0.993767 |
| 48000 | 0.015100 | 0.026952 | 0.995925 | 0.999564 | 0.996132 | 0.997846 |
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="taskydata/deberta-v3-base_10xp3nirstbbflanse_5xc4")