Instructions to use lordtt13/emo-mobilebert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lordtt13/emo-mobilebert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lordtt13/emo-mobilebert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lordtt13/emo-mobilebert") model = AutoModelForSequenceClassification.from_pretrained("lordtt13/emo-mobilebert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from lordtt13/emo-mobilebert: direct link, hf CLI and curl.
- Browser
- Download file 86.3 MB
-
https://huggingface.co/lordtt13/emo-mobilebert/resolve/main/tf_model.h5
- Command line
-
hf download hf://lordtt13/emo-mobilebert/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/lordtt13/emo-mobilebert/resolve/main/tf_model.h5
86.3 MB
- Xet hash:
- 17775c643e901593a48ee3b4c97c9aaa7d251cf842181fa1d1d68b6ad6dcf75e
- Size of remote file:
- 86.3 MB
- SHA256:
- fcc6c4734f86e4cc0483839853d4fdd0bb6db83da01dc547c413fddfdff4fbcb
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