dair-ai/emotion
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How to use philschmid/MiniLMv2-L6-H384-emotion with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="philschmid/MiniLMv2-L6-H384-emotion") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("philschmid/MiniLMv2-L6-H384-emotion")
model = AutoModelForSequenceClassification.from_pretrained("philschmid/MiniLMv2-L6-H384-emotion")This model is a fine-tuned version of nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large on the emotion 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 |
|---|---|---|---|---|
| 1.432 | 1.0 | 500 | 0.9992 | 0.6805 |
| 0.8073 | 2.0 | 1000 | 0.5437 | 0.846 |
| 0.4483 | 3.0 | 1500 | 0.3018 | 0.909 |
| 0.2833 | 4.0 | 2000 | 0.2412 | 0.915 |
| 0.2169 | 5.0 | 2500 | 0.2140 | 0.9215 |
| 0.1821 | 6.0 | 3000 | 0.2159 | 0.917 |
| 0.154 | 7.0 | 3500 | 0.2084 | 0.919 |
| 0.1461 | 8.0 | 4000 | 0.2047 | 0.92 |