Instructions to use namesarnav/causalbench_text-t5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use namesarnav/causalbench_text-t5-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="namesarnav/causalbench_text-t5-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("namesarnav/causalbench_text-t5-base") model = AutoModelForSequenceClassification.from_pretrained("namesarnav/causalbench_text-t5-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
causalbench_text-t5-base
This model is a fine-tuned version of t5-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4554
- Accuracy: 0.7696
- Macro F1: 0.6958
- Micro F1: 0.7696
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: 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
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 | Micro F1 |
|---|---|---|---|---|---|---|
| 0.55 | 1.0 | 940 | 0.5021 | 0.7507 | 0.6469 | 0.7507 |
| 0.4761 | 2.0 | 1880 | 0.4815 | 0.7608 | 0.6386 | 0.7608 |
| 0.4402 | 3.0 | 2820 | 0.4554 | 0.7696 | 0.6958 | 0.7696 |
Framework versions
- Transformers 4.50.3
- Pytorch 2.11.0+cu130
- Datasets 3.6.0
- Tokenizers 0.21.4
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Model tree for namesarnav/causalbench_text-t5-base
Base model
google-t5/t5-base