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
Download training_args.bin from anggars/xlm-emotion: direct link, hf CLI and curl.
- Browser
- Download file 5.27 kB
-
https://huggingface.co/anggars/xlm-emotion/resolve/46ec142be8075eabd33b30bf6e2ee84816c71ec2/training_args.bin
- Command line
-
hf download hf://anggars/xlm-emotion@46ec142be8075eabd33b30bf6e2ee84816c71ec2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/anggars/xlm-emotion/resolve/46ec142be8075eabd33b30bf6e2ee84816c71ec2/training_args.bin
5.27 kB
- Xet hash:
- 0789fe96117f0896f3d77ebcd69b479e9261c29d9c77616c50af7da1a4f515c8
- Size of remote file:
- 5.27 kB
- SHA256:
- c946fc5563540ae1094f7942a6a6c81bf8b06a1f3c83f80f79497701713a881c
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