Instructions to use MilaNLProc/xlm-emo-t with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MilaNLProc/xlm-emo-t with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MilaNLProc/xlm-emo-t")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MilaNLProc/xlm-emo-t") model = AutoModelForSequenceClassification.from_pretrained("MilaNLProc/xlm-emo-t", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from MilaNLProc/xlm-emo-t: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/MilaNLProc/xlm-emo-t/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://MilaNLProc/xlm-emo-t/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/MilaNLProc/xlm-emo-t/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- 3569ed1eb4b0f6568d57ee1083e1575e2ef728bb2eeb7a30cad45e64a4ddc701
- Size of remote file:
- 1.11 GB
- SHA256:
- bbf1c252b9abcf7582ab462501337b812bf35d58054a66b68d1bd8df6b23b7ec
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