Instructions to use datasciencemmw/old-beta2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use datasciencemmw/old-beta2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="datasciencemmw/old-beta2")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("datasciencemmw/old-beta2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - autotrain | |
| - text-classification | |
| language: | |
| - en | |
| widget: | |
| - text: "I love AutoTrain 🤗" | |
| datasets: | |
| - LiveEvil/autotrain-data-copuml-la-beta-demo | |
| co2_eq_emissions: | |
| emissions: 1.2815143214785873 | |
| # Model Trained Using AutoTrain | |
| - Problem type: Multi-class Classification | |
| - Model ID: 2205770755 | |
| - CO2 Emissions (in grams): 1.2815 | |
| ## Validation Metrics | |
| - Loss: 1.085 | |
| - Accuracy: 0.747 | |
| - Macro F1: 0.513 | |
| - Micro F1: 0.747 | |
| - Weighted F1: 0.715 | |
| - Macro Precision: 0.533 | |
| - Micro Precision: 0.747 | |
| - Weighted Precision: 0.691 | |
| - Macro Recall: 0.515 | |
| - Micro Recall: 0.747 | |
| - Weighted Recall: 0.747 | |
| ## 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/LiveEvil/autotrain-copuml-la-beta-demo-2205770755 | |
| ``` | |
| Or Python API: | |
| ``` | |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer | |
| model = AutoModelForSequenceClassification.from_pretrained("LiveEvil/autotrain-copuml-la-beta-demo-2205770755", use_auth_token=True) | |
| tokenizer = AutoTokenizer.from_pretrained("LiveEvil/autotrain-copuml-la-beta-demo-2205770755", use_auth_token=True) | |
| inputs = tokenizer("I love AutoTrain", return_tensors="pt") | |
| outputs = model(**inputs) | |
| ``` |