Instructions to use erwannd/question_answer_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use erwannd/question_answer_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="erwannd/question_answer_model")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("erwannd/question_answer_model") model = AutoModelForQuestionAnswering.from_pretrained("erwannd/question_answer_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from erwannd/question_answer_model: direct link, hf CLI and curl.
- Browser
- Download file 1.83 kB
-
https://huggingface.co/erwannd/question_answer_model/resolve/main/README.md
- Command line
-
hf download hf://erwannd/question_answer_model/README.md
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curl -L -o README.md https://huggingface.co/erwannd/question_answer_model/resolve/main/README.md
1.83 kB
| license: apache-2.0 | |
| base_model: distilbert-base-uncased | |
| tags: | |
| - generated_from_keras_callback | |
| model-index: | |
| - name: erwannd/question_answer_model | |
| 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. --> | |
| # erwannd/question_answer_model | |
| This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Train Loss: 1.6269 | |
| - Validation Loss: 1.7794 | |
| - Epoch: 2 | |
| ## 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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} | |
| - training_precision: float32 | |
| ### Training results | |
| | Train Loss | Validation Loss | Epoch | | |
| |:----------:|:---------------:|:-----:| | |
| | 3.5835 | 2.2227 | 0 | | |
| | 1.9397 | 1.7794 | 1 | | |
| | 1.6269 | 1.7794 | 2 | | |
| ### Framework versions | |
| - Transformers 4.40.2 | |
| - TensorFlow 2.15.0 | |
| - Datasets 2.19.1 | |
| - Tokenizers 0.19.1 | |