dair-ai/emotion
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How to use colingao/bert-base-uncased_emotion_ft with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="colingao/bert-base-uncased_emotion_ft") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("colingao/bert-base-uncased_emotion_ft")
model = AutoModelForSequenceClassification.from_pretrained("colingao/bert-base-uncased_emotion_ft", device_map="auto")This model was trained from scratch 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 | F1 | Precision |
|---|---|---|---|---|---|---|
| 0.82 | 1.0 | 250 | 0.2697 | 0.9105 | 0.9112 | 0.8759 |
| 0.2002 | 2.0 | 500 | 0.1846 | 0.9325 | 0.9331 | 0.9059 |
| 0.1237 | 3.0 | 750 | 0.1562 | 0.9365 | 0.9368 | 0.9120 |
| 0.097 | 4.0 | 1000 | 0.1553 | 0.9355 | 0.9357 | 0.9108 |