Instructions to use ToBeWithYou/ABSA-Multi_Label_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ToBeWithYou/ABSA-Multi_Label_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ToBeWithYou/ABSA-Multi_Label_Classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ToBeWithYou/ABSA-Multi_Label_Classification") model = AutoModelForSequenceClassification.from_pretrained("ToBeWithYou/ABSA-Multi_Label_Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from ToBeWithYou/ABSA-Multi_Label_Classification: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/ToBeWithYou/ABSA-Multi_Label_Classification/resolve/main/optimizer.pt
- Command line
-
hf download hf://ToBeWithYou/ABSA-Multi_Label_Classification/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/ToBeWithYou/ABSA-Multi_Label_Classification/resolve/main/optimizer.pt
1.42 GB
- Xet hash:
- 90f8d4fa10177cda3aee3be8981c0922be1817029ef8c4ed842c4a40eb2d17d4
- Size of remote file:
- 1.42 GB
- SHA256:
- bfd8aedb2bc4842e6b829552bb6e963ffb7a34519425081e5098b2fa679f6c19
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