Text Classification
Transformers
Safetensors
English
distilbert
requirements-engineering
software-engineering
user-stories
qus
quality-assessment
natural-language-processing
text-embeddings-inference
Instructions to use devleoespinosa/DistilBERT-AUSQ-SL-Problem-Oriented with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devleoespinosa/DistilBERT-AUSQ-SL-Problem-Oriented with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="devleoespinosa/DistilBERT-AUSQ-SL-Problem-Oriented")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("devleoespinosa/DistilBERT-AUSQ-SL-Problem-Oriented") model = AutoModelForSequenceClassification.from_pretrained("devleoespinosa/DistilBERT-AUSQ-SL-Problem-Oriented", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Update README.md
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Tokenizer for DistilBERT-AUSQ-SL-Problem-Oriented
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Problem Oriented Fine-tuned DistilBERT model for Automated User Story Quality Assessment
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initial commit
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