Instructions to use radned/test-trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use radned/test-trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="radned/test-trainer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("radned/test-trainer") model = AutoModelForSequenceClassification.from_pretrained("radned/test-trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from radned/test-trainer: direct link, hf CLI and curl.
- Browser
- Download file 1.03 kB
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https://huggingface.co/radned/test-trainer/resolve/refs%2Fpr%2F2/README.md
- Command line
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hf download hf://radned/test-trainer@refs/pr/2/README.md
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curl -L -o README.md https://huggingface.co/radned/test-trainer/resolve/refs%2Fpr%2F2/README.md
1.03 kB
metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
model-index:
- name: test-trainer
results: []
test-trainer
This model is a fine-tuned version of bert-base-uncased on the glue 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
Framework versions
- Transformers 4.27.3
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.2