Instructions to use fernanda-dionello/good-reads-string with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fernanda-dionello/good-reads-string with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fernanda-dionello/good-reads-string")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("fernanda-dionello/good-reads-string", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from fernanda-dionello/good-reads-string: direct link, hf CLI and curl.
- Browser
- Download file 1.39 kB
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https://huggingface.co/fernanda-dionello/good-reads-string/resolve/main/README.md
- Command line
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hf download hf://fernanda-dionello/good-reads-string/README.md
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curl -L -o README.md https://huggingface.co/fernanda-dionello/good-reads-string/resolve/main/README.md
1.39 kB
| tags: | |
| - autotrain | |
| - text-classification | |
| language: | |
| - en | |
| widget: | |
| - text: "I love AutoTrain 🤗" | |
| datasets: | |
| - fernanda-dionello/autotrain-data-autotrain_goodreads_string | |
| co2_eq_emissions: | |
| emissions: 0.04700680417595474 | |
| # Model Trained Using AutoTrain | |
| - Problem type: Multi-class Classification | |
| - Model ID: 2164069744 | |
| - CO2 Emissions (in grams): 0.0470 | |
| ## Validation Metrics | |
| - Loss: 0.806 | |
| - Accuracy: 0.686 | |
| - Macro F1: 0.534 | |
| - Micro F1: 0.686 | |
| - Weighted F1: 0.678 | |
| - Macro Precision: 0.524 | |
| - Micro Precision: 0.686 | |
| - Weighted Precision: 0.673 | |
| - Macro Recall: 0.551 | |
| - Micro Recall: 0.686 | |
| - Weighted Recall: 0.686 | |
| ## Usage | |
| You can use cURL to access this model: | |
| ``` | |
| $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/fernanda-dionello/autotrain-autotrain_goodreads_string-2164069744 | |
| ``` | |
| Or Python API: | |
| ``` | |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer | |
| model = AutoModelForSequenceClassification.from_pretrained("fernanda-dionello/autotrain-autotrain_goodreads_string-2164069744", use_auth_token=True) | |
| tokenizer = AutoTokenizer.from_pretrained("fernanda-dionello/autotrain-autotrain_goodreads_string-2164069744", use_auth_token=True) | |
| inputs = tokenizer("I love AutoTrain", return_tensors="pt") | |
| outputs = model(**inputs) | |
| ``` |