Instructions to use somya-kr/flan-t5-small-sentiment-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use somya-kr/flan-t5-small-sentiment-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="somya-kr/flan-t5-small-sentiment-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("somya-kr/flan-t5-small-sentiment-classification") model = AutoModelForSequenceClassification.from_pretrained("somya-kr/flan-t5-small-sentiment-classification") - Notebooks
- Google Colab
- Kaggle
flan-t5-small-sentiment-classification
This model is a fine-tuned version of google/flan-t5-small 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: 8
- eval_batch_size: 8
- 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: 10
Training results
Framework versions
- Transformers 4.46.1
- Pytorch 2.5.0+cu124
- Datasets 3.1.0
- Tokenizers 0.20.1
- Downloads last month
- 7
Model tree for somya-kr/flan-t5-small-sentiment-classification
Base model
google/flan-t5-small