Text Classification
Transformers
TensorFlow
Safetensors
English
deberta-v2
generated_from_keras_callback
AVeriTec
Eval Results (legacy)
text-embeddings-inference
Instructions to use ctu-aic/deberta-v3-large-AVeriTeC-nli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ctu-aic/deberta-v3-large-AVeriTeC-nli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ctu-aic/deberta-v3-large-AVeriTeC-nli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ctu-aic/deberta-v3-large-AVeriTeC-nli") model = AutoModelForSequenceClassification.from_pretrained("ctu-aic/deberta-v3-large-AVeriTeC-nli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
deberta-v3-large-AVeriTeC-nli
This model was finetuned from microsoft/deberta-v3-large on an AVeriTec dataset. It achieves the following results on the evaluation set:
Intended uses & limitations
This model is intended for usage in a pipeline for open-domain fact-checking task.
Training and evaluation data
See chenxwh/AVeriTeC
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- optimizer: adamw_torch
- training_precision: float16
- learning_rate: 1e-5
- per_device_train_batch_size: 32
- num_train_epochs: 10
- weight_decay: 0.01
- load_best_model_at_end: True #early stopping!
- warmup_ratio: 0.06
Training results
Framework versions
- Transformers 4.43.0
- TensorFlow 2.17.0
- Datasets 2.20.0
- Tokenizers 0.19.1
- Downloads last month
- 54
Model tree for ctu-aic/deberta-v3-large-AVeriTeC-nli
Base model
microsoft/deberta-v3-largeEvaluation results
- dev macro F1 score on chenxwh/AVeriTeCself-reported0.710
- dev macro recall on chenxwh/AVeriTeCself-reported0.730
- dev macro precision on chenxwh/AVeriTeCself-reported0.710
- dev accuracy on chenxwh/AVeriTeCself-reported0.820