Instructions to use Anwaarma/QGgood with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Anwaarma/QGgood with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="Anwaarma/QGgood")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Anwaarma/QGgood", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Configuration Parsing Warning:Config file config.json cannot be fetched (too big)
Configuration Parsing Warning:Config file tokenizer_config.json cannot be fetched (too big)
Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 2381374622
- CO2 Emissions (in grams): 34.4020
Validation Metrics
- Loss: 3.281
- Rouge1: 0.717
- Rouge2: 0.000
- RougeL: 0.717
- RougeLsum: 0.717
- Gen Len: 8.369
Usage
You can use cURL to access this model:
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/Anwaarma/autotrain-arcd-qg-2381374622
- Downloads last month
- 4