Instructions to use Yannis98/tmp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Yannis98/tmp 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="Yannis98/tmp")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Yannis98/tmp") model = AutoModelForQuestionAnswering.from_pretrained("Yannis98/tmp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Yannis98/tmp: direct link, hf CLI and curl.
- Browser
- Download file 1.17 kB
-
https://huggingface.co/Yannis98/tmp/resolve/main/README.md
- Command line
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hf download hf://Yannis98/tmp/README.md
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curl -L -o README.md https://huggingface.co/Yannis98/tmp/resolve/main/README.md
1.17 kB
metadata
library_name: transformers
license: apache-2.0
base_model: Yannis98/squad_albert_XXL_finetuned
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: tmp
results: []
tmp
This model is a fine-tuned version of Yannis98/squad_albert_XXL_finetuned on the squad 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 128
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2.0
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 2.21.0
- Tokenizers 0.21.0