Instructions to use guldasta/xtreme-MLQA.hi.hi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use guldasta/xtreme-MLQA.hi.hi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="guldasta/xtreme-MLQA.hi.hi")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("guldasta/xtreme-MLQA.hi.hi") model = AutoModelForQuestionAnswering.from_pretrained("guldasta/xtreme-MLQA.hi.hi") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: mit | |
| base_model: timpal0l/mdeberta-v3-base-squad2 | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: xtreme-MLQA.hi.hi | |
| 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. --> | |
| # xtreme-MLQA.hi.hi | |
| This model is a fine-tuned version of [timpal0l/mdeberta-v3-base-squad2](https://huggingface.co/timpal0l/mdeberta-v3-base-squad2) on an unknown dataset. | |
| ## 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: 2e-05 | |
| - train_batch_size: 16 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - num_epochs: 1 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | No log | 1.0 | 481 | 1.1670 | | |
| ### Framework versions | |
| - Transformers 4.47.1 | |
| - Pytorch 2.5.1+cu121 | |
| - Datasets 3.2.0 | |
| - Tokenizers 0.21.0 | |