Text Classification
Transformers
PyTorch
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use Elron/deberta-v3-large-irony with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Elron/deberta-v3-large-irony with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Elron/deberta-v3-large-irony")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Elron/deberta-v3-large-irony") model = AutoModelForSequenceClassification.from_pretrained("Elron/deberta-v3-large-irony", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 654 Bytes
27e031e | 1 2 | jbsub -queue x86_1h -cores 4+1 -mem 30g -require a100 -o outputs/train/tweet_eval2/irony/deberta-v3-large-irony-lr8e-6-gas2-ls0.1/test.log /dccstor/tslm/envs/anaconda3/envs/tslm-gen/bin/python train_clf.py --model_name_or_path outputs/train/tweet_eval2/irony/deberta-v3-large-irony-lr8e-6-gas2-ls0.1/best_checkpoint --train_file data/tweet_eval/irony/train.csv --validation_file data/tweet_eval/irony/validation.csv --test_file data/tweet_eval/irony/test.csv --do_eval --do_predict --report_to none --per_device_eval_batch_size 16 --max_seq_length 256 --output_dir outputs/train/tweet_eval2/irony/deberta-v3-large-irony-lr8e-6-gas2-ls0.1/best_checkpoint |