Instructions to use lavokvv/test_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lavokvv/test_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lavokvv/test_trainer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lavokvv/test_trainer") model = AutoModelForSequenceClassification.from_pretrained("lavokvv/test_trainer") - Notebooks
- Google Colab
- Kaggle
update model card README.md
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README.md
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# test_trainer
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This model is a fine-tuned version of [flaubert/
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## Model description
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs:
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### Training results
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# test_trainer
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This model is a fine-tuned version of [flaubert/flaubert_base_cased](https://huggingface.co/flaubert/flaubert_base_cased) on the None dataset.
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## Model description
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 3
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### Training results
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