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")# pip install -U transformers accelerate # 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
Download model.safetensors from anggars/xlm-mbti: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/anggars/xlm-mbti/resolve/main/model.safetensors
- Command line
-
hf download hf://anggars/xlm-mbti/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/anggars/xlm-mbti/resolve/main/model.safetensors
1.11 GB
- Xet hash:
- 441f8277ee40cb57d2ee58af8032586af2212c98694d6dd27604a257a40ca11d
- Size of remote file:
- 1.11 GB
- SHA256:
- 9c2b587104cbda824a765bafd36899f2ba13a145aad1e3a3e46a72b3c3413f82
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