Instructions to use alex2awesome/stance-detection-classification-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alex2awesome/stance-detection-classification-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="alex2awesome/stance-detection-classification-model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("alex2awesome/stance-detection-classification-model") model = AutoModelForSequenceClassification.from_pretrained("alex2awesome/stance-detection-classification-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download train_results.json from alex2awesome/stance-detection-classification-model: direct link, hf CLI and curl.
- Browser
- Download file 197 Bytes
-
https://huggingface.co/alex2awesome/stance-detection-classification-model/resolve/main/train_results.json
- Command line
-
hf download hf://alex2awesome/stance-detection-classification-model/train_results.json
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curl -L -o train_results.json https://huggingface.co/alex2awesome/stance-detection-classification-model/resolve/main/train_results.json
197 Bytes
| { | |
| "epoch": 10.0, | |
| "train_loss": 0.5760954068351747, | |
| "train_runtime": 42398.6115, | |
| "train_samples": 96270, | |
| "train_samples_per_second": 22.706, | |
| "train_steps_per_second": 2.838 | |
| } |