Instructions to use KeeeeepGoing/esm2_t6_8M_UR50D-finetuned-localization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KeeeeepGoing/esm2_t6_8M_UR50D-finetuned-localization with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KeeeeepGoing/esm2_t6_8M_UR50D-finetuned-localization")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KeeeeepGoing/esm2_t6_8M_UR50D-finetuned-localization") model = AutoModelForSequenceClassification.from_pretrained("KeeeeepGoing/esm2_t6_8M_UR50D-finetuned-localization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e69e95144d8f01ea691927d8e9faf5807a57d05a9ba6db3b59dd0cebf6bc3c0b
- Size of remote file:
- 4.92 kB
- SHA256:
- b33a2d2898cea03c12118fb165db8583d1309e92f37131b08960bfd776702ddc
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