Text Classification
Transformers
TensorBoard
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use NurErtug/crowd_sourced_web_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NurErtug/crowd_sourced_web_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NurErtug/crowd_sourced_web_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NurErtug/crowd_sourced_web_classifier") model = AutoModelForSequenceClassification.from_pretrained("NurErtug/crowd_sourced_web_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
crowd_sourced_web_classifier / runs /Dec02_17-35-36_tsf-508-wpa-2-088.epfl.ch /events.out.tfevents.1764693337.tsf-508-wpa-2-088.epfl.ch.8916.3
- Xet hash:
- 24d0528b79363d210df77636e6ff7218eb5b343c6a6ff8c653f99aeadc68a8a3
- Size of remote file:
- 21.2 kB
- SHA256:
- 96cb3ecbed3e9519afc0452f7ac652ab10c48e017d78ddd3a7cad5d181e70f69
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.