# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("rv2307/quora_duplicate_distilbert_model")
model = AutoModelForSequenceClassification.from_pretrained("rv2307/quora_duplicate_distilbert_model", device_map="auto")distilbert-base-uncased Quora Duplicate Pair Detection
This model is fine tuned of version of distilbert-base-uncased on quora dataset for detecting duplicate sentences or questions.
Loss :: 0.111300 Accuracy :: 0.900740 F1 Score :: 0.868633
Model Description: DistilBERT model is a distilled form of the BERT model. The size of a BERT model was reduced by 40% via knowledge distillation during the pre-training phase while retaining 97% of its language understanding abilities and being 60% faster.
Training and evaluation data: "quora"
Training Hyper Parameters: learning_rate = 3e-4, per_device_train_batch_size=32, per_device_eval_batch_size=32, num_train_epochs=4, evaluation_strategy="epoch", seed: 42, optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
Training Results:
Epoch Training Loss Validation Loss Accuracy F1 1 0.271500 0.264808 0.884909 0.844402 2 0.191200 0.258109 0.896399 0.866099 3 0.111300 0.315554 0.900740 0.868633
Label 0 = Not Duplicate Label 1 = Duplicate
- Downloads last month
- 12
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rv2307/quora_duplicate_distilbert_model")