Instructions to use TuringGame/flan-t5-small-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TuringGame/flan-t5-small-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TuringGame/flan-t5-small-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TuringGame/flan-t5-small-classifier") model = AutoModelForSequenceClassification.from_pretrained("TuringGame/flan-t5-small-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from TuringGame/flan-t5-small-classifier: direct link, hf CLI and curl.
- Browser
- Download file 2.18 kB
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https://huggingface.co/TuringGame/flan-t5-small-classifier/resolve/main/README.md
- Command line
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hf download hf://TuringGame/flan-t5-small-classifier/README.md
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curl -L -o README.md https://huggingface.co/TuringGame/flan-t5-small-classifier/resolve/main/README.md
2.18 kB
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: google/flan-t5-small | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| - f1 | |
| model-index: | |
| - name: flan-t5-small-classifier | |
| 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. --> | |
| # flan-t5-small-classifier | |
| This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.3960 | |
| - Accuracy: 0.8372 | |
| - F1: 0.6246 | |
| ## 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: 32 | |
| - eval_batch_size: 32 | |
| - seed: 42 | |
| - optimizer: Use OptimizerNames.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 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | | |
| |:-------------:|:------:|:----:|:---------------:|:--------:|:------:| | |
| | No log | 0 | 0 | 0.8605 | 0.2622 | 0.3991 | | |
| | No log | 0.2020 | 79 | 0.5689 | 0.7262 | 0.0 | | |
| | No log | 0.4041 | 158 | 0.5251 | 0.7464 | 0.0 | | |
| | No log | 0.6061 | 237 | 0.4921 | 0.7565 | 0.0765 | | |
| | No log | 0.8082 | 316 | 0.4664 | 0.7752 | 0.2427 | | |
| | No log | 1.0102 | 395 | 0.4418 | 0.8012 | 0.4344 | | |
| | No log | 1.2123 | 474 | 0.4187 | 0.8199 | 0.5283 | | |
| | 0.5394 | 1.4143 | 553 | 0.4065 | 0.8242 | 0.5643 | | |
| | 0.5394 | 1.6164 | 632 | 0.4005 | 0.8372 | 0.6221 | | |
| | 0.5394 | 1.8184 | 711 | 0.3960 | 0.8372 | 0.6246 | | |
| ### Framework versions | |
| - Transformers 4.53.3 | |
| - Pytorch 2.6.0+cu124 | |
| - Datasets 4.0.0 | |
| - Tokenizers 0.21.2 | |