nyu-mll/glue
Viewer • Updated • 1.49M • 440k • 524
How to use Carlos31/platzi-distilroberta-base-mrpc-glue-Carlos-Moreno with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Carlos31/platzi-distilroberta-base-mrpc-glue-Carlos-Moreno") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Carlos31/platzi-distilroberta-base-mrpc-glue-Carlos-Moreno")
model = AutoModelForSequenceClassification.from_pretrained("Carlos31/platzi-distilroberta-base-mrpc-glue-Carlos-Moreno", device_map="auto")YAML Metadata Error:"widget[0].text" must be a string
YAML Metadata Error:"widget[1].text" must be a string
This model is a fine-tuned version of distilroberta-base on the glue and the mrpc datasets. It achieves the following results on the evaluation set:
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.5508 | 1.09 | 500 | 0.6319 | 0.7794 | 0.8529 |
| 0.3888 | 2.18 | 1000 | 0.8226 | 0.8211 | 0.8765 |
Base model
distilbert/distilroberta-base