# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("thrunlab/t5-base_cola_dense_collected-stats")
model = AutoModelForSequenceClassification.from_pretrained("thrunlab/t5-base_cola_dense_collected-stats")Quick Links
t5-base_cola_dense_collected-stats
This model is a fine-tuned version of t5-base on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.5092
- Accuracy: 0.8044
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.5892 | 0.37 | 50 | 0.5679 | 0.6913 |
| 0.488 | 0.75 | 100 | 0.5486 | 0.7948 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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Model tree for thrunlab/t5-base_cola_dense_collected-stats
Base model
google-t5/t5-baseDataset used to train thrunlab/t5-base_cola_dense_collected-stats
Evaluation results
- Accuracy on gluevalidation set self-reported0.804
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="thrunlab/t5-base_cola_dense_collected-stats")