Instructions to use FuuToru/XLM-processed2-squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FuuToru/XLM-processed2-squad with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="FuuToru/XLM-processed2-squad")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("FuuToru/XLM-processed2-squad") model = AutoModelForQuestionAnswering.from_pretrained("FuuToru/XLM-processed2-squad") - Notebooks
- Google Colab
- Kaggle
XLM-processed2-squad
This model is a fine-tuned version of FuuToru/XLM-processed-squad on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2469
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: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.3427 | 1.0 | 1078 | 0.2449 |
| 0.3 | 2.0 | 2156 | 0.2469 |
Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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