Instructions to use LinYuting/atgx-roberta-base-squad2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LinYuting/atgx-roberta-base-squad2 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="LinYuting/atgx-roberta-base-squad2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("LinYuting/atgx-roberta-base-squad2") model = AutoModelForQuestionAnswering.from_pretrained("LinYuting/atgx-roberta-base-squad2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from LinYuting/atgx-roberta-base-squad2: direct link, hf CLI and curl.
- Browser
- Download file 1.78 kB
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https://huggingface.co/LinYuting/atgx-roberta-base-squad2/resolve/main/README.md
- Command line
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hf download hf://LinYuting/atgx-roberta-base-squad2/README.md
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curl -L -o README.md https://huggingface.co/LinYuting/atgx-roberta-base-squad2/resolve/main/README.md
1.78 kB
metadata
license: cc-by-4.0
base_model: deepset/roberta-base-squad2
tags:
- generated_from_keras_callback
model-index:
- name: LinYuting/atgx-roberta-base-squad2
results: []
LinYuting/atgx-roberta-base-squad2
This model is a fine-tuned version of deepset/roberta-base-squad2 on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.2567
- Validation Loss: 1.1199
- 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': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 192, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, '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 |
|---|---|---|
| 0.5804 | 1.0234 | 0 |
| 0.3338 | 1.0925 | 1 |
| 0.2567 | 1.1199 | 2 |
Framework versions
- Transformers 4.32.1
- TensorFlow 2.11.1
- Datasets 2.14.5
- Tokenizers 0.13.3