# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("eprasad/distilled-t5-small")
model = AutoModelForSequenceClassification.from_pretrained("eprasad/distilled-t5-small", device_map="auto")Quick Links
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.01294114999473095
f1_macro: 0.9911675113805504
f1_micro: 0.9939879759519038
f1_weighted: 0.9939977082681383
precision_macro: 0.9867234959827553
precision_micro: 0.9939879759519038
precision_weighted: 0.9940423542558368
recall_macro: 0.9957796643247464
recall_micro: 0.9939879759519038
recall_weighted: 0.9939879759519038
accuracy: 0.9939879759519038
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eprasad/distilled-t5-small")