Instructions to use gyeoldere/test_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gyeoldere/test_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gyeoldere/test_trainer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gyeoldere/test_trainer") model = AutoModelForSequenceClassification.from_pretrained("gyeoldere/test_trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from gyeoldere/test_trainer: direct link, hf CLI and curl.
- Browser
- Download file 1.12 kB
-
https://huggingface.co/gyeoldere/test_trainer/resolve/main/README.md
- Command line
-
hf download hf://gyeoldere/test_trainer/README.md
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curl -L -o README.md https://huggingface.co/gyeoldere/test_trainer/resolve/main/README.md
1.12 kB
metadata
license: mit
tags:
- generated_from_trainer
datasets:
- snli
model-index:
- name: test_trainer
results: []
test_trainer
This model is a fine-tuned version of microsoft/deberta-base on the snli dataset. It achieves the following results on the evaluation set:
- Loss: 0.1932
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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2