Instructions to use Yannis98/tmp_natqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Yannis98/tmp_natqa 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_natqa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Yannis98/tmp_natqa") model = AutoModelForQuestionAnswering.from_pretrained("Yannis98/tmp_natqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,228 Bytes
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library_name: transformers
base_model: Yannis98/natqa_albert_XXL_finetuned
tags:
- generated_from_trainer
datasets:
- lucadiliello/naturalquestionsshortqa
model-index:
- name: tmp_natqa
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tmp_natqa
This model is a fine-tuned version of [Yannis98/natqa_albert_XXL_finetuned](https://huggingface.co/Yannis98/natqa_albert_XXL_finetuned) on the lucadiliello/naturalquestionsshortqa 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: 512
- 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
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