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
| license: cc-by-4.0 | |
| base_model: FuuToru/XLM-processed-squad | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: XLM-processed2-squad | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # XLM-processed2-squad | |
| This model is a fine-tuned version of [FuuToru/XLM-processed-squad](https://huggingface.co/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 | |