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: 1,175 Bytes
27e031e | 1 2 | jbsub -queue x86_6h -cores 4+1 -mem 30g -require a100 -o outputs/train/tweet_eval2/irony/deberta-v3-large-irony-lr8e-6-gas2-ls0.1/train.log /dccstor/tslm/envs/anaconda3/envs/tslm-gen/bin/python train_clf.py --model_name_or_path microsoft/deberta-v3-large --train_file data/tweet_eval/irony/train.csv --validation_file data/tweet_eval/irony/validation.csv --do_train --do_eval --per_device_train_batch_size 16 --per_device_eval_batch_size 16 --max_seq_length 256 --learning_rate 8e-6 --output_dir outputs/train/tweet_eval2/irony/deberta-v3-large-irony-lr8e-6-gas2-ls0.1 --evaluation_strategy steps --save_strategy no --warmup_steps 50 --num_train_epochs 10 --overwrite_output_dir --logging_steps 100 --gradient_accumulation_steps 2 --label_smoothing_factor 0.1 --report_to clearml --metric_for_best_model accuracy --logging_dir outputs/train/tweet_eval2/irony/deberta-v3-large-irony-lr8e-6-gas2-ls0.1/tb \; rm -rf outputs/train/tweet_eval2/irony/deberta-v3-large-irony-lr8e-6-gas2-ls0.1/tb \; rm -rf outputs/train/tweet_eval2/irony/deberta-v3-large-irony-lr8e-6-gas2-ls0.1/checkpoint-* \; . outputs/train/tweet_eval2/irony/deberta-v3-large-irony-lr8e-6-gas2-ls0.1/run_test.sh
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