Instructions to use vedalken/ML6-interview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vedalken/ML6-interview with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vedalken/ML6-interview")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vedalken/ML6-interview") model = AutoModelForSequenceClassification.from_pretrained("vedalken/ML6-interview") - Notebooks
- Google Colab
- Kaggle
Roberta-base trained on the dataset for job description classification.
Repo of the interview: https://bitbucket.org/ml6team/challenge-classify-job-descriptions.git/src
For the training code just send me a message or comment in this repo.
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Model tree for vedalken/ML6-interview
Base model
FacebookAI/roberta-base