Text Classification
Transformers
PyTorch
TensorBoard
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use intanm/clickbait_classification_xlm_roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use intanm/clickbait_classification_xlm_roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="intanm/clickbait_classification_xlm_roberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("intanm/clickbait_classification_xlm_roberta") model = AutoModelForSequenceClassification.from_pretrained("intanm/clickbait_classification_xlm_roberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from intanm/clickbait_classification_xlm_roberta: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/intanm/clickbait_classification_xlm_roberta/resolve/main/tokenizer.json
- Command line
-
hf download hf://intanm/clickbait_classification_xlm_roberta/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/intanm/clickbait_classification_xlm_roberta/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 3ee1e89f6db1588a812a416efd721d637e525d2a66c47f30c4f5bfbadab24d38
- Size of remote file:
- 17.1 MB
- SHA256:
- f2c509a525eb51aebb33fb59c24ee923c1d4c1db23c3ae81fe05ccf354084f7b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.