Instructions to use Kimty/final_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kimty/final_test 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="Kimty/final_test")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Kimty/final_test") model = AutoModelForQuestionAnswering.from_pretrained("Kimty/final_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Kimty/final_test: direct link, hf CLI and curl.
- Browser
- Download file 1.11 kB
-
https://huggingface.co/Kimty/final_test/resolve/main/README.md
- Command line
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hf download hf://Kimty/final_test/README.md
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curl -L -o README.md https://huggingface.co/Kimty/final_test/resolve/main/README.md
1.11 kB
metadata
license: cc-by-4.0
base_model: deepset/roberta-base-squad2
tags:
- generated_from_trainer
model-index:
- name: final_test
results: []
final_test
This model is a fine-tuned version of deepset/roberta-base-squad2 on the None 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: 2e-05
- 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: 10
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2