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
Download tokenizer.json from devleoespinosa/DistilBERT-AUSQ-SL-Problem-Oriented: direct link, hf CLI and curl.
- Browser
- Download file 712 kB
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https://huggingface.co/devleoespinosa/DistilBERT-AUSQ-SL-Problem-Oriented/resolve/main/tokenizer.json
- Command line
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hf download hf://devleoespinosa/DistilBERT-AUSQ-SL-Problem-Oriented/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/devleoespinosa/DistilBERT-AUSQ-SL-Problem-Oriented/resolve/main/tokenizer.json
712 kB
File too large to display, you can check the raw version instead.