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")# pip install -U transformers accelerate # 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
Download tokenizer.json from anggars/xlm-emotion: direct link, hf CLI and curl.
- Browser
- Download file 16.8 MB
-
https://huggingface.co/anggars/xlm-emotion/resolve/46ec142be8075eabd33b30bf6e2ee84816c71ec2/tokenizer.json
- Command line
-
hf download hf://anggars/xlm-emotion@46ec142be8075eabd33b30bf6e2ee84816c71ec2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/anggars/xlm-emotion/resolve/46ec142be8075eabd33b30bf6e2ee84816c71ec2/tokenizer.json
16.8 MB
- Xet hash:
- fa2c7481a642580a0a0df2b70899a179c680df971c0dc5a49854ade114ee7f62
- Size of remote file:
- 16.8 MB
- SHA256:
- e5b633524ba90477daaba16ec27580a08a2856ae0ee8c33d9f5f9358378d3b35
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