Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use nikcheerla/amd-partial-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use nikcheerla/amd-partial-v1 with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("nikcheerla/amd-partial-v1") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - sentence-transformers
How to use nikcheerla/amd-partial-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nikcheerla/amd-partial-v1") 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] - Notebooks
- Google Colab
- Kaggle
File size: 190 Bytes
4b604c3 | 1 2 3 4 5 6 7 | {
"word_embedding_dimension": 768,
"pooling_mode_cls_token": false,
"pooling_mode_mean_tokens": true,
"pooling_mode_max_tokens": false,
"pooling_mode_mean_sqrt_len_tokens": false
} |