google-research-datasets/go_emotions
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How to use IsaacZhy/roberta-large-goemotions with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="IsaacZhy/roberta-large-goemotions") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("IsaacZhy/roberta-large-goemotions")
model = AutoModelForSequenceClassification.from_pretrained("IsaacZhy/roberta-large-goemotions")This model is a fine-tuned version of roberta-large 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 |
|---|---|---|---|---|---|---|
| 0.1205 | 1.0 | 679 | 0.0865 | 0.5632 | 0.7347 | 0.4458 |
| 0.0859 | 2.0 | 1358 | 0.0829 | 0.5717 | 0.7378 | 0.4521 |
| 0.0727 | 3.0 | 2037 | 0.0827 | 0.5897 | 0.7523 | 0.4753 |
| 0.0629 | 4.0 | 2716 | 0.0857 | 0.5808 | 0.7535 | 0.4652 |
| 0.0568 | 5.0 | 3395 | 0.0904 | 0.5868 | 0.7616 | 0.4821 |
| 0.0423 | 6.0 | 4074 | 0.0989 | 0.5806 | 0.7682 | 0.4724 |
| 0.0344 | 7.0 | 4753 | 0.1079 | 0.5736 | 0.7657 | 0.4650 |
| 0.0296 | 8.0 | 5432 | 0.1158 | 0.5637 | 0.7649 | 0.4504 |
| 0.0206 | 9.0 | 6111 | 0.1200 | 0.5674 | 0.7689 | 0.4486 |
| 0.0177 | 10.0 | 6790 | 0.1240 | 0.5728 | 0.7737 | 0.4547 |