google-research-datasets/go_emotions
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How to use jungealexander/distilbert-base-uncased-finetuned-go_emotions_20220608_1 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="jungealexander/distilbert-base-uncased-finetuned-go_emotions_20220608_1") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("jungealexander/distilbert-base-uncased-finetuned-go_emotions_20220608_1")
model = AutoModelForSequenceClassification.from_pretrained("jungealexander/distilbert-base-uncased-finetuned-go_emotions_20220608_1")This model is a fine-tuned version of distilbert-base-uncased on the go_emotions 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 | F1 | Roc Auc | Accuracy |
|---|---|---|---|---|---|---|
| 0.173 | 1.0 | 679 | 0.1074 | 0.4245 | 0.6455 | 0.2976 |
| 0.0989 | 2.0 | 1358 | 0.0903 | 0.5199 | 0.6974 | 0.3972 |
| 0.0865 | 3.0 | 2037 | 0.0868 | 0.5504 | 0.7180 | 0.4263 |
| 0.0806 | 4.0 | 2716 | 0.0860 | 0.5472 | 0.7160 | 0.4233 |
| 0.0771 | 5.0 | 3395 | 0.0857 | 0.5575 | 0.7242 | 0.4364 |