nyu-mll/glue
Viewer โข Updated โข 1.49M โข 668k โข 783
How to use VitaliiVrublevskyi/ibert-roberta-base-finetuned-mrpc with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="VitaliiVrublevskyi/ibert-roberta-base-finetuned-mrpc") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("VitaliiVrublevskyi/ibert-roberta-base-finetuned-mrpc")
model = AutoModelForSequenceClassification.from_pretrained("VitaliiVrublevskyi/ibert-roberta-base-finetuned-mrpc", device_map="auto")This model is a fine-tuned version of kssteven/ibert-roberta-base on the glue dataset. 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 |
|---|---|---|---|---|---|
| No log | 1.0 | 115 | 0.4043 | 0.8309 | 0.8821 |
| No log | 2.0 | 230 | 0.3885 | 0.8456 | 0.8927 |
| No log | 3.0 | 345 | 0.3775 | 0.8578 | 0.8953 |