Instructions to use saketh-chervu/distilroberta-base-finetuned-distilroberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use saketh-chervu/distilroberta-base-finetuned-distilroberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="saketh-chervu/distilroberta-base-finetuned-distilroberta")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("saketh-chervu/distilroberta-base-finetuned-distilroberta") model = AutoModelForMaskedLM.from_pretrained("saketh-chervu/distilroberta-base-finetuned-distilroberta") - Notebooks
- Google Colab
- Kaggle
saketh-chervu/distilroberta-base-finetuned-distilroberta
This model is a fine-tuned version of distilroberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 3.1462
- Epoch: 0
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': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
Training results
| Train Loss | Epoch |
|---|---|
| 3.1462 | 0 |
Framework versions
- Transformers 4.24.0
- TensorFlow 2.9.2
- Datasets 2.6.1
- Tokenizers 0.13.2
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