dair-ai/emotion
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How to use gokuls/hbertv1-emotion_48_KD with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokuls/hbertv1-emotion_48_KD") # Load model directly
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("gokuls/hbertv1-emotion_48_KD", device_map="auto")This model is a fine-tuned version of gokuls/bert_12_layer_model_v1_complete_training_new_48_KD 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 |
|---|---|---|---|---|
| 1.2276 | 1.0 | 250 | 0.9254 | 0.6315 |
| 0.8699 | 2.0 | 500 | 0.9379 | 0.6515 |
| 0.5863 | 3.0 | 750 | 0.4565 | 0.8645 |
| 0.3741 | 4.0 | 1000 | 0.4251 | 0.8705 |
| 0.3141 | 5.0 | 1250 | 0.4311 | 0.8805 |
| 0.2734 | 6.0 | 1500 | 0.3519 | 0.8905 |
| 0.2301 | 7.0 | 1750 | 0.3530 | 0.8895 |
| 0.2017 | 8.0 | 2000 | 0.3558 | 0.8935 |
| 0.1745 | 9.0 | 2250 | 0.3538 | 0.887 |
| 0.1556 | 10.0 | 2500 | 0.3617 | 0.891 |