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 /Nov25_08-53-16_tsf-508-wpa-1-223.epfl.ch /events.out.tfevents.1764057200.tsf-508-wpa-1-223.epfl.ch.1964.1
- Xet hash:
- 06e0def7fb2a5892fa12c0b5e19559607c068178a5217655310800f15f6c77c3
- Size of remote file:
- 7.07 kB
- SHA256:
- 7065e24b2098f0fc05903525b58c1ad9fcd57f71e5a0be864155b2f8c9055151
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.