Instructions to use HasinMDG/XLM_Roberta_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/XLM_Roberta_Sentiment_Toward_Topics_Baseline with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("HasinMDG/XLM_Roberta_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/XLM_Roberta_Sentiment_Toward_Topics_Baseline with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("HasinMDG/XLM_Roberta_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/XLM_Roberta_Sentiment_Toward_Topics_Baseline: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/HasinMDG/XLM_Roberta_Sentiment_Toward_Topics_Baseline/resolve/main/tokenizer.json
- Command line
-
hf download hf://HasinMDG/XLM_Roberta_Sentiment_Toward_Topics_Baseline/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/HasinMDG/XLM_Roberta_Sentiment_Toward_Topics_Baseline/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 5e583faa2b222c1a358683456d9f4d8e9643a6dc1acfb85f19926957d9f0911d
- Size of remote file:
- 17.1 MB
- SHA256:
- fa3d7a958dcbbca3d8d99146efe1a3ae117a8407e68313266a0892e559fe661d
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