Instructions to use ShynBui/s19 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ShynBui/s19 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="ShynBui/s19")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("ShynBui/s19") model = AutoModelForQuestionAnswering.from_pretrained("ShynBui/s19", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from ShynBui/s19: direct link, hf CLI and curl.
- Browser
- Download file 1.05 kB
-
https://huggingface.co/ShynBui/s19/resolve/main/README.md
- Command line
-
hf download hf://ShynBui/s19/README.md
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curl -L -o README.md https://huggingface.co/ShynBui/s19/resolve/main/README.md
1.05 kB
metadata
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: s19
results: []
s19
This model is a fine-tuned version of bert-base-cased on the squad_v2 dataset.
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:
- learning_rate: 0.0004
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3