dair-ai/emotion
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How to use gokuls/hbertv1-mini-wt-48-emotion with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokuls/hbertv1-mini-wt-48-emotion") # Load model directly
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("gokuls/hbertv1-mini-wt-48-emotion", device_map="auto")This model is a fine-tuned version of gokuls/model_v1_complete_training_wt_init_48_mini 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 |
|---|---|---|---|---|
| 1.0852 | 1.0 | 250 | 0.5567 | 0.8195 |
| 0.4522 | 2.0 | 500 | 0.3409 | 0.8775 |
| 0.3152 | 3.0 | 750 | 0.3007 | 0.8885 |
| 0.2646 | 4.0 | 1000 | 0.2999 | 0.9045 |
| 0.23 | 5.0 | 1250 | 0.2842 | 0.8945 |
| 0.205 | 6.0 | 1500 | 0.2658 | 0.9035 |
| 0.1871 | 7.0 | 1750 | 0.2674 | 0.902 |
| 0.1623 | 8.0 | 2000 | 0.2561 | 0.908 |
| 0.1488 | 9.0 | 2250 | 0.2529 | 0.9075 |
| 0.1379 | 10.0 | 2500 | 0.2523 | 0.908 |