# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("ivanlau/distil-bert-uncased-finetuned-github-issues")
model = AutoModelForSequenceClassification.from_pretrained("ivanlau/distil-bert-uncased-finetuned-github-issues")Quick Links
YAML Metadata Error:"model-index[0].results[0].dataset.type" with value "ticket tagger" fails to match the required pattern: /^(?:[\w-]+\/)?[\w-.]+$/
Model Description
This model is a fine-tuned version of distilbert-base-uncased and fine-tuning it on the github ticket tagger dataset. It classifies issue into 3 common categories: Bug, Enhancement, Questions.
It achieves the following results on the evaluation set:
- Accuracy: 0.7862
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-5
- train_batch_size: 16
- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0
- num_epochs: 5
Codes
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ivanlau/distil-bert-uncased-finetuned-github-issues")