Text Classification
Transformers
TensorBoard
Safetensors
deberta-v2
Trained with AutoTrain
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("idobn/twitter-mbti-v2")
model = AutoModelForSequenceClassification.from_pretrained("idobn/twitter-mbti-v2")Quick Links
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 1.9677170515060425
f1_macro: 0.43647747790260216
f1_micro: 0.40711847879083374
f1_weighted: 0.3874051890698862
precision_macro: 0.49034231056721467
precision_micro: 0.40711847879083374
precision_weighted: 0.4284233711977137
recall_macro: 0.4407702395816866
recall_micro: 0.40711847879083374
recall_weighted: 0.40711847879083374
accuracy: 0.40711847879083374
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Model tree for idobn/twitter-mbti-v2
Base model
microsoft/deberta-v3-large
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="idobn/twitter-mbti-v2")