nyu-mll/glue
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How to use thrunlab/t5-base_cola_moe_ex38_epochs-0_decoder_all_sparsity10_mare_mlp with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="thrunlab/t5-base_cola_moe_ex38_epochs-0_decoder_all_sparsity10_mare_mlp") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("thrunlab/t5-base_cola_moe_ex38_epochs-0_decoder_all_sparsity10_mare_mlp")
model = AutoModelForSequenceClassification.from_pretrained("thrunlab/t5-base_cola_moe_ex38_epochs-0_decoder_all_sparsity10_mare_mlp", device_map="auto")This model is a fine-tuned version of t5-base on the glue dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
Base model
google-t5/t5-base