Instructions to use StaAhmed/Model_QA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use StaAhmed/Model_QA 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="StaAhmed/Model_QA")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("StaAhmed/Model_QA") model = AutoModelForQuestionAnswering.from_pretrained("StaAhmed/Model_QA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from StaAhmed/Model_QA: direct link, hf CLI and curl.
- Browser
- Download file 1.43 kB
-
https://huggingface.co/StaAhmed/Model_QA/resolve/main/README.md
- Command line
-
hf download hf://StaAhmed/Model_QA/README.md
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curl -L -o README.md https://huggingface.co/StaAhmed/Model_QA/resolve/main/README.md
1.43 kB
| license: cc-by-4.0 | |
| base_model: deepset/minilm-uncased-squad2 | |
| tags: | |
| - generated_from_keras_callback | |
| model-index: | |
| - name: StaAhmed/Model_QA | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information Keras had access to. You should | |
| probably proofread and complete it, then remove this comment. --> | |
| # StaAhmed/Model_QA | |
| This model is a fine-tuned version of [deepset/minilm-uncased-squad2](https://huggingface.co/deepset/minilm-uncased-squad2) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Train Loss: 6.2438 | |
| - Epoch: 0 | |
| ## 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': 0.001, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} | |
| - training_precision: float32 | |
| ### Training results | |
| | Train Loss | Epoch | | |
| |:----------:|:-----:| | |
| | 6.2438 | 0 | | |
| ### Framework versions | |
| - Transformers 4.38.1 | |
| - TensorFlow 2.15.0 | |
| - Datasets 2.17.1 | |
| - Tokenizers 0.15.2 | |