Text Classification
Transformers
TensorFlow
roberta
generated_from_keras_callback
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("madatnlp/rob-large-krmath")
model = AutoModelForSequenceClassification.from_pretrained("madatnlp/rob-large-krmath")Quick Links
madatnlp/rob-large-krmath
This model is a fine-tuned version of klue/roberta-large on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.2249
- Validation Loss: 0.1952
- Epoch: 3
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:
- optimizer: {'name': 'Adam', 'learning_rate': 1e-04, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
Training results
| Train Loss | Validation Loss | Epoch |
|---|---|---|
| 0.7547 | 0.3385 | 0 |
| 0.3233 | 0.2132 | 1 |
| 0.2540 | 0.2434 | 2 |
| 0.2249 | 0.1952 | 3 |
Framework versions
- Transformers 4.18.0
- TensorFlow 2.8.0
- Datasets 2.2.1
- Tokenizers 0.12.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="madatnlp/rob-large-krmath")