dair-ai/emotion
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How to use philschmid/MiniLMv2-L12-H384-emotion with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="philschmid/MiniLMv2-L12-H384-emotion") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("philschmid/MiniLMv2-L12-H384-emotion")
model = AutoModelForSequenceClassification.from_pretrained("philschmid/MiniLMv2-L12-H384-emotion")This model is a fine-tuned version of nreimers/MiniLMv2-L12-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 |
|---|---|---|---|---|
| 0.8745 | 1.0 | 1000 | 0.6673 | 0.81 |
| 0.3466 | 2.0 | 2000 | 0.2816 | 0.918 |
| 0.2201 | 3.0 | 3000 | 0.2367 | 0.9215 |
| 0.1761 | 4.0 | 4000 | 0.2069 | 0.925 |
| 0.1435 | 5.0 | 5000 | 0.2089 | 0.922 |
| 0.1454 | 6.0 | 6000 | 0.2168 | 0.923 |
| 0.1041 | 7.0 | 7000 | 0.2081 | 0.924 |
| 0.0953 | 8.0 | 8000 | 0.2133 | 0.9245 |