clinc/clinc_oos
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How to use optimum/MiniLMv2-L12-H384-finetuned-clinc with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="optimum/MiniLMv2-L12-H384-finetuned-clinc") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("optimum/MiniLMv2-L12-H384-finetuned-clinc")
model = AutoModelForSequenceClassification.from_pretrained("optimum/MiniLMv2-L12-H384-finetuned-clinc", device_map="auto")This model is a fine-tuned version of nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large on the clinc_oos 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 |
|---|---|---|---|---|
| No log | 1.0 | 60 | 4.6555 | 0.1887 |
| No log | 2.0 | 120 | 3.8771 | 0.4784 |
| No log | 3.0 | 180 | 3.2507 | 0.7352 |
| 3.9668 | 4.0 | 240 | 2.7445 | 0.8365 |
| 3.9668 | 5.0 | 300 | 2.3475 | 0.8865 |
| 3.9668 | 6.0 | 360 | 2.0370 | 0.8926 |
| 3.9668 | 7.0 | 420 | 1.8099 | 0.9145 |
| 2.0924 | 8.0 | 480 | 1.6433 | 0.9190 |
| 2.0924 | 9.0 | 540 | 1.5563 | 0.9281 |
| 2.0924 | 10.0 | 600 | 1.5252 | 0.9319 |