Instructions to use EricPeter/xlm-roberta-base-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EricPeter/xlm-roberta-base-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="EricPeter/xlm-roberta-base-finetuned")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("EricPeter/xlm-roberta-base-finetuned") model = AutoModelForQuestionAnswering.from_pretrained("EricPeter/xlm-roberta-base-finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
EricPeter/xlm-roberta-base-finetuned
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 5.9607
- Epoch: 7
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': False, 'is_legacy_optimizer': False, 'learning_rate': 2e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: mixed_float16
Training results
| Train Loss | Epoch |
|---|---|
| 4.3219 | 0 |
| 2.5545 | 1 |
| 2.1750 | 2 |
| 1.9064 | 3 |
| 1.5885 | 4 |
| 1.3327 | 5 |
| 3.0905 | 6 |
| 5.9607 | 7 |
Framework versions
- Transformers 4.31.0
- TensorFlow 2.12.0
- Datasets 2.14.0
- Tokenizers 0.13.3
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FacebookAI/xlm-roberta-base