Text Classification
Transformers
ONNX
Safetensors
bert
social-media
content-filtering
engagement-bait
distillation
text-embeddings-inference
Instructions to use selftaughtdev/engagement-farm-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use selftaughtdev/engagement-farm-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="selftaughtdev/engagement-farm-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("selftaughtdev/engagement-farm-classifier") model = AutoModelForSequenceClassification.from_pretrained("selftaughtdev/engagement-farm-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download metrics.json from selftaughtdev/engagement-farm-classifier: direct link, hf CLI and curl.
- Browser
- Download file 216 Bytes
-
https://huggingface.co/selftaughtdev/engagement-farm-classifier/resolve/main/metrics.json
- Command line
-
hf download hf://selftaughtdev/engagement-farm-classifier/metrics.json
-
curl -L -o metrics.json https://huggingface.co/selftaughtdev/engagement-farm-classifier/resolve/main/metrics.json
216 Bytes
| { | |
| "eval_loss": 0.17980128526687622, | |
| "eval_accuracy": 0.972972972972973, | |
| "eval_f1": 0.9433962264150944, | |
| "eval_runtime": 2.8042, | |
| "eval_samples_per_second": 277.086, | |
| "eval_steps_per_second": 4.636, | |
| "epoch": 6.0 | |
| } |