Text Classification
Transformers
TensorFlow
roberta
generated_from_keras_callback
text-embeddings-inference
Instructions to use xinyixiuxiu/roberta-base-SST2-finetuned-try with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xinyixiuxiu/roberta-base-SST2-finetuned-try with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xinyixiuxiu/roberta-base-SST2-finetuned-try")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xinyixiuxiu/roberta-base-SST2-finetuned-try") model = AutoModelForSequenceClassification.from_pretrained("xinyixiuxiu/roberta-base-SST2-finetuned-try") - Notebooks
- Google Colab
- Kaggle
xinyixiuxiu/roberta-base-SST2-finetuned-try
This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.1172
- Train Accuracy: 0.9690
- Validation Loss: 0.7077
- Validation Accuracy: 0.8555
- Epoch: 2
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', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|---|---|---|---|---|
| 0.1493 | 0.9510 | 0.3429 | 0.8911 | 0 |
| 0.1810 | 0.9470 | 0.6590 | 0.8108 | 1 |
| 0.1172 | 0.9690 | 0.7077 | 0.8555 | 2 |
Framework versions
- Transformers 4.27.1
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
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