# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("nlp04/ES_roberta_30")
model = AutoModelForQuestionAnswering.from_pretrained("nlp04/ES_roberta_30")Quick Links
ES_roberta_30
This model is a fine-tuned version of klue/roberta-large on the None dataset. It achieves the following results on the evaluation set:
- Exact Match: 66.6667
- F1: 74.9008
- Loss: 1.0138
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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Exact Match | F1 | Validation Loss |
|---|---|---|---|---|---|
| 0.7813 | 1.63 | 500 | 66.6667 | 74.9008 | 1.0138 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="nlp04/ES_roberta_30")