Instructions to use badokorach/qa_afroxml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use badokorach/qa_afroxml with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="badokorach/qa_afroxml")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("badokorach/qa_afroxml") model = AutoModelForQuestionAnswering.from_pretrained("badokorach/qa_afroxml", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from badokorach/qa_afroxml: direct link, hf CLI and curl.
- Browser
- Download file 1.7 kB
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https://huggingface.co/badokorach/qa_afroxml/resolve/main/README.md
- Command line
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hf download hf://badokorach/qa_afroxml/README.md
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curl -L -o README.md https://huggingface.co/badokorach/qa_afroxml/resolve/main/README.md
1.7 kB
| license: mit | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: qa_afroxml | |
| 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. --> | |
| # qa_afroxml | |
| This model is a fine-tuned version of [Davlan/afro-xlmr-large](https://huggingface.co/Davlan/afro-xlmr-large) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 5.8436 | |
| ## 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: 4e-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: 10 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | No log | 1.0 | 261 | 2.7576 | | |
| | 2.9898 | 2.0 | 522 | 2.8418 | | |
| | 2.9898 | 3.0 | 783 | 2.6659 | | |
| | 1.9119 | 4.0 | 1044 | 2.8548 | | |
| | 1.9119 | 5.0 | 1305 | 3.2987 | | |
| | 1.0452 | 6.0 | 1566 | 4.3218 | | |
| | 1.0452 | 7.0 | 1827 | 4.8747 | | |
| | 0.4662 | 8.0 | 2088 | 5.3515 | | |
| | 0.4662 | 9.0 | 2349 | 5.6930 | | |
| | 0.1931 | 10.0 | 2610 | 5.8436 | | |
| ### Framework versions | |
| - Transformers 4.28.0 | |
| - Pytorch 2.0.1+cu118 | |
| - Datasets 2.14.4 | |
| - Tokenizers 0.13.3 | |