Text Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
emotion-classification
midwest-emo
math-rock
domain-adaptation
hybrid-corpus
Eval Results (legacy)
text-embeddings-inference
Instructions to use anggars/xlm-emotion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anggars/xlm-emotion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="anggars/xlm-emotion")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("anggars/xlm-emotion") model = AutoModelForSequenceClassification.from_pretrained("anggars/xlm-emotion", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 30d4d8e239dc122dc10fe0c296f250ba20881f621a97fbd62c0a5eec3ea7469e
- Size of remote file:
- 17.1 MB
- SHA256:
- 2687cc191964de4bb7f4a43b29e7398decc0661f77e860d9c69d77a1bf6c5fdf
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.