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
metadata
license: cc-by-4.0
base_model: deepset/minilm-uncased-squad2
tags:
- generated_from_keras_callback
model-index:
- name: StaAhmed/Model_QA
results: []
StaAhmed/Model_QA
This model is a fine-tuned version of 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