Instructions to use rorschach-40/flan-t5-xl-dpa-text-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rorschach-40/flan-t5-xl-dpa-text-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rorschach-40/flan-t5-xl-dpa-text-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rorschach-40/flan-t5-xl-dpa-text-classification") model = AutoModelForSequenceClassification.from_pretrained("rorschach-40/flan-t5-xl-dpa-text-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
flan-t5-xl-dpa-text-classification
This model is a fine-tuned version of google/flan-t5-xl on the None 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: 0.0003
- train_batch_size: 3
- eval_batch_size: 3
- 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 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
- Downloads last month
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Model tree for rorschach-40/flan-t5-xl-dpa-text-classification
Base model
google/flan-t5-xl