Instructions to use felipe93/test_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use felipe93/test_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="felipe93/test_trainer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("felipe93/test_trainer") model = AutoModelForSequenceClassification.from_pretrained("felipe93/test_trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from felipe93/test_trainer: direct link, hf CLI and curl.
- Browser
- Download file 1.05 kB
-
https://huggingface.co/felipe93/test_trainer/resolve/main/README.md
- Command line
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hf download hf://felipe93/test_trainer/README.md
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curl -L -o README.md https://huggingface.co/felipe93/test_trainer/resolve/main/README.md
1.05 kB
metadata
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: test_trainer
results: []
test_trainer
This model was trained from scratch on an unknown 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: 8
- eval_batch_size: 8
- 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
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cpu
- Datasets 3.2.0
- Tokenizers 0.21.0