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 modules.json from HasinMDG/Labse_Sentiment_Toward_Topics_Baseline: direct link, hf CLI and curl.
- Browser
- Download file 461 Bytes
-
https://huggingface.co/HasinMDG/Labse_Sentiment_Toward_Topics_Baseline/resolve/refs%2Fpr%2F1/modules.json
- Command line
-
hf download hf://HasinMDG/Labse_Sentiment_Toward_Topics_Baseline@refs/pr/1/modules.json
-
curl -L -o modules.json https://huggingface.co/HasinMDG/Labse_Sentiment_Toward_Topics_Baseline/resolve/refs%2Fpr%2F1/modules.json
461 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Dense", | |
| "type": "sentence_transformers.models.Dense" | |
| }, | |
| { | |
| "idx": 3, | |
| "name": "3", | |
| "path": "3_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
| } | |
| ] |