Instructions to use ElMad/adaptable-fly-271 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ElMad/adaptable-fly-271 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ElMad/adaptable-fly-271")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ElMad/adaptable-fly-271") model = AutoModelForSequenceClassification.from_pretrained("ElMad/adaptable-fly-271") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("ElMad/adaptable-fly-271")
model = AutoModelForSequenceClassification.from_pretrained("ElMad/adaptable-fly-271")Quick Links
adaptable-fly-271
This model is a fine-tuned version of microsoft/deberta-v3-xsmall on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1619
- Hamming Loss: 0.0439
- Zero One Loss: 0.886
- Jaccard Score: 0.8648
- Hamming Loss Optimised: 0.0437
- Hamming Loss Threshold: 0.5933
- Zero One Loss Optimised: 0.839
- Zero One Loss Threshold: 0.1736
- Jaccard Score Optimised: 0.7401
- Jaccard Score Threshold: 0.1334
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: 5.379556445396376e-05
- train_batch_size: 20
- eval_batch_size: 20
- seed: 2024
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Hamming Loss | Zero One Loss | Jaccard Score | Hamming Loss Optimised | Hamming Loss Threshold | Zero One Loss Optimised | Zero One Loss Threshold | Jaccard Score Optimised | Jaccard Score Threshold |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 160 | 0.1853 | 0.0497 | 1.0 | 1.0 | 0.0497 | 0.9000 | 1.0 | 0.9000 | 1.0 | 0.9000 |
| No log | 2.0 | 320 | 0.1830 | 0.0497 | 1.0 | 1.0 | 0.0497 | 0.9000 | 1.0 | 0.9000 | 1.0 | 0.9000 |
| No log | 3.0 | 480 | 0.1690 | 0.0442 | 0.8738 | 0.8604 | 0.0456 | 0.4236 | 0.8588 | 0.2889 | 0.8410 | 0.2889 |
| 0.2169 | 4.0 | 640 | 0.1621 | 0.0441 | 0.8712 | 0.8560 | 0.0438 | 0.5821 | 0.8225 | 0.1746 | 0.7466 | 0.1429 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu118
- Datasets 3.1.0
- Tokenizers 0.20.3
- Downloads last month
- -
Model tree for ElMad/adaptable-fly-271
Base model
microsoft/deberta-v3-xsmall
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ElMad/adaptable-fly-271")