Text Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
mbti
midwest-emo
math-rock
personality-detection
domain-adaptation
hybrid-corpus
Eval Results (legacy)
text-embeddings-inference
Instructions to use anggars/xlm-mbti with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anggars/xlm-mbti with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="anggars/xlm-mbti")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("anggars/xlm-mbti") model = AutoModelForSequenceClassification.from_pretrained("anggars/xlm-mbti", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f45629e214f359c5167695ebcf5177389da8972176b7cd60247a9c3084384d9c
- Size of remote file:
- 5.2 kB
- SHA256:
- e1137fa3cbeaef856588561a93c66824b5047b47b471872356561df70f06e7da
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