dair-ai/emotion
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How to use gokuls/hbertv1-tiny-wt-48-emotion with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokuls/hbertv1-tiny-wt-48-emotion") # Load model directly
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("gokuls/hbertv1-tiny-wt-48-emotion", dtype="auto")This model is a fine-tuned version of gokuls/model_v1_complete_training_wt_init_48_tiny 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 |
|---|---|---|---|---|
| 1.4321 | 1.0 | 250 | 1.0203 | 0.6475 |
| 0.8329 | 2.0 | 500 | 0.5954 | 0.814 |
| 0.5347 | 3.0 | 750 | 0.4146 | 0.8645 |
| 0.398 | 4.0 | 1000 | 0.3496 | 0.8805 |
| 0.3418 | 5.0 | 1250 | 0.3091 | 0.889 |
| 0.2932 | 6.0 | 1500 | 0.2864 | 0.8945 |
| 0.2646 | 7.0 | 1750 | 0.2782 | 0.8965 |
| 0.2532 | 8.0 | 2000 | 0.2695 | 0.8985 |
| 0.2342 | 9.0 | 2250 | 0.2632 | 0.898 |
| 0.225 | 10.0 | 2500 | 0.2617 | 0.897 |