Instructions to use taskydata/deberta-v3-base_10xp3nirstbbflanse_5xc4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use taskydata/deberta-v3-base_10xp3nirstbbflanse_5xc4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="taskydata/deberta-v3-base_10xp3nirstbbflanse_5xc4")# 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") - Notebooks
- Google Colab
- Kaggle
metadata
license: mit
datasets:
- taskydata/tasky_or_not
language:
- en
metrics:
- accuracy
- f1
- recall
- precision
pipeline_tag: text-classification
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 |
Wandb logs: