nyu-mll/glue
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How to use philschmid/MiniLMv2-L12-H384-sst2 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="philschmid/MiniLMv2-L12-H384-sst2") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("philschmid/MiniLMv2-L12-H384-sst2")
model = AutoModelForSequenceClassification.from_pretrained("philschmid/MiniLMv2-L12-H384-sst2")This model is a fine-tuned version of nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large 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 | Accuracy |
|---|---|---|---|---|
| 0.5576 | 1.0 | 264 | 0.2690 | 0.8979 |
| 0.2854 | 2.0 | 528 | 0.2077 | 0.9117 |
| 0.2158 | 3.0 | 792 | 0.2195 | 0.9209 |
| 0.1789 | 4.0 | 1056 | 0.2260 | 0.9163 |