Instructions to use kimnt93/mt-seed-task-cls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use kimnt93/mt-seed-task-cls with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kimnt93/mt-seed-task-cls") 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 kimnt93/mt-seed-task-cls with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("kimnt93/mt-seed-task-cls") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from kimnt93/mt-seed-task-cls: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/kimnt93/mt-seed-task-cls/resolve/main/tokenizer.json
- Command line
-
hf download hf://kimnt93/mt-seed-task-cls/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/kimnt93/mt-seed-task-cls/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 480a9c5b408f3a5f51e21ca8381c8237cf55b59bb6db02be4f59e33eceee7cf0
- Size of remote file:
- 17.1 MB
- SHA256:
- 40e4a12784b3e7ee5c5c373c28a22f02497b30a4ea39d9548ee0f3c6a31e9ca9
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