nyu-mll/glue
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How to use JeremiahZ/bert-base-uncased-stsb with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="JeremiahZ/bert-base-uncased-stsb") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("JeremiahZ/bert-base-uncased-stsb")
model = AutoModelForSequenceClassification.from_pretrained("JeremiahZ/bert-base-uncased-stsb")This model is a fine-tuned version of bert-base-uncased on the GLUE STSB 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 | Combined Score |
|---|---|---|---|---|---|---|
| No log | 1.0 | 180 | 0.5179 | 0.8806 | 0.8735 | 0.8771 |
| No log | 2.0 | 360 | 0.5145 | 0.8850 | 0.8820 | 0.8835 |
| 0.7868 | 3.0 | 540 | 0.5144 | 0.8875 | 0.8843 | 0.8859 |
Base model
google-bert/bert-base-uncased