Instructions to use guo1006/transformers-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use guo1006/transformers-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="guo1006/transformers-qa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("guo1006/transformers-qa") model = AutoModelForQuestionAnswering.from_pretrained("guo1006/transformers-qa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from guo1006/transformers-qa: direct link, hf CLI and curl.
- Browser
- Download file 1.52 kB
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https://huggingface.co/guo1006/transformers-qa/resolve/main/README.md
- Command line
-
hf download hf://guo1006/transformers-qa/README.md
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curl -L -o README.md https://huggingface.co/guo1006/transformers-qa/resolve/main/README.md
1.52 kB
metadata
library_name: transformers
license: apache-2.0
base_model: distilbert-base-cased
tags:
- generated_from_keras_callback
model-index:
- name: transformers-qa
results: []
transformers-qa
This model is a fine-tuned version of distilbert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 1.5071
- Validation Loss: 1.1436
- 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': True, 'is_legacy_optimizer': False, 'learning_rate': 5e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: mixed_float16
Training results
| Train Loss | Validation Loss | Epoch |
|---|---|---|
| 1.5071 | 1.1436 | 0 |
Framework versions
- Transformers 4.47.0
- TensorFlow 2.17.1
- Datasets 3.3.1
- Tokenizers 0.21.0