Instructions to use datasciencemmw/old-beta1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use datasciencemmw/old-beta1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="datasciencemmw/old-beta1")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("datasciencemmw/old-beta1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - autotrain | |
| - text-classification | |
| language: | |
| - en | |
| widget: | |
| - text: "I love AutoTrain 🤗" | |
| datasets: | |
| - LiveEvil/autotrain-data-copuml-production | |
| co2_eq_emissions: | |
| emissions: 0.9758714074673083 | |
| # Model Trained Using AutoTrain | |
| - Problem type: Multi-class Classification | |
| - Model ID: 2205570752 | |
| - CO2 Emissions (in grams): 0.9759 | |
| ## Validation Metrics | |
| - Loss: 1.092 | |
| - Accuracy: 0.701 | |
| - Macro F1: 0.416 | |
| - Micro F1: 0.701 | |
| - Weighted F1: 0.670 | |
| - Macro Precision: 0.399 | |
| - Micro Precision: 0.701 | |
| - Weighted Precision: 0.643 | |
| - Macro Recall: 0.436 | |
| - Micro Recall: 0.701 | |
| - Weighted Recall: 0.701 | |
| ## 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-production-2205570752 | |
| ``` | |
| Or Python API: | |
| ``` | |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer | |
| model = AutoModelForSequenceClassification.from_pretrained("LiveEvil/autotrain-copuml-production-2205570752", use_auth_token=True) | |
| tokenizer = AutoTokenizer.from_pretrained("LiveEvil/autotrain-copuml-production-2205570752", use_auth_token=True) | |
| inputs = tokenizer("I love AutoTrain", return_tensors="pt") | |
| outputs = model(**inputs) | |
| ``` |