dair-ai/emotion
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How to use bergum/xtremedistil-emotion with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="bergum/xtremedistil-emotion") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("bergum/xtremedistil-emotion")
model = AutoModelForSequenceClassification.from_pretrained("bergum/xtremedistil-emotion")# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("bergum/xtremedistil-emotion")
model = AutoModelForSequenceClassification.from_pretrained("bergum/xtremedistil-emotion")This model is a fine-tuned version of microsoft/xtremedistil-l6-h256-uncased on the emotion dataset. It achieves the following results on the evaluation set:
The following hyperparameters were used during training:
Epoch Training Loss Validation Loss Accuracy 1 No log 1.238589 0.609000 2 No log 0.934423 0.714000 3 No log 0.768701 0.742000 4 1.074800 0.638208 0.805500 5 1.074800 0.551363 0.851500 6 1.074800 0.476291 0.875500 7 1.074800 0.427313 0.883500 8 0.531500 0.392633 0.886000 9 0.531500 0.357979 0.892000 10 0.531500 0.330304 0.899500 11 0.531500 0.304529 0.907000 12 0.337200 0.287447 0.918000 13 0.337200 0.277067 0.921000 14 0.337200 0.259483 0.921000 15 0.337200 0.257564 0.916500 16 0.246200 0.241970 0.919500 17 0.246200 0.241537 0.921500 18 0.246200 0.235705 0.924500 19 0.246200 0.237325 0.920500 20 0.201400 0.229699 0.923500 21 0.201400 0.227426 0.923000 22 0.201400 0.228554 0.924000 23 0.201400 0.226941 0.925500 24 0.184300 0.225816 0.926500
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bergum/xtremedistil-emotion")