nyu-mll/glue
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How to use vicl/distilbert-base-uncased-finetuned-stsb with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="vicl/distilbert-base-uncased-finetuned-stsb") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("vicl/distilbert-base-uncased-finetuned-stsb")
model = AutoModelForSequenceClassification.from_pretrained("vicl/distilbert-base-uncased-finetuned-stsb", device_map="auto")This model is a fine-tuned version of distilbert-base-uncased on the glue dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr |
|---|---|---|---|---|---|
| No log | 1.0 | 360 | 0.6366 | 0.8537 | 0.8516 |
| 1.0464 | 2.0 | 720 | 0.6171 | 0.8632 | 0.8626 |
| 0.4002 | 3.0 | 1080 | 0.6082 | 0.8663 | 0.8643 |
| 0.4002 | 4.0 | 1440 | 0.5644 | 0.8666 | 0.8636 |
| 0.2479 | 5.0 | 1800 | 0.5780 | 0.8654 | 0.8624 |