Instructions to use HasinMDG/Labse_Sentiment_Toward_Topics_Baseline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use HasinMDG/Labse_Sentiment_Toward_Topics_Baseline with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("HasinMDG/Labse_Sentiment_Toward_Topics_Baseline") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - setfit
How to use HasinMDG/Labse_Sentiment_Toward_Topics_Baseline with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("HasinMDG/Labse_Sentiment_Toward_Topics_Baseline") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from HasinMDG/Labse_Sentiment_Toward_Topics_Baseline: direct link, hf CLI and curl.
- Browser
- Download file 13.6 MB
-
https://huggingface.co/HasinMDG/Labse_Sentiment_Toward_Topics_Baseline/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://HasinMDG/Labse_Sentiment_Toward_Topics_Baseline@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/HasinMDG/Labse_Sentiment_Toward_Topics_Baseline/resolve/refs%2Fpr%2F1/tokenizer.json
13.6 MB
- Xet hash:
- 9061164c657de59e1ffb961b1afb95a5e0afab558522095c235bb448a22b7c74
- Size of remote file:
- 13.6 MB
- SHA256:
- 92262b29204f8fdc169a63f9005a0e311a16262cef4d96ecfe2a7ed638662ed3
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