Text Classification
setfit
Safetensors
sentence-transformers
multilingual
xlm-roberta
text-embeddings-inference
Instructions to use automatedMotiveCoder/setfit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use automatedMotiveCoder/setfit with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("automatedMotiveCoder/setfit") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - sentence-transformers
How to use automatedMotiveCoder/setfit with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("automatedMotiveCoder/setfit") 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
Update README.md
Browse files
README.md
CHANGED
|
@@ -1,5 +1,6 @@
|
|
| 1 |
---
|
| 2 |
-
base_model:
|
|
|
|
| 3 |
library_name: setfit
|
| 4 |
metrics:
|
| 5 |
- f1
|
|
@@ -11,7 +12,8 @@ tags:
|
|
| 11 |
widget: []
|
| 12 |
inference: true
|
| 13 |
license: mit
|
| 14 |
-
language:
|
|
|
|
| 15 |
---
|
| 16 |
|
| 17 |
# SetFit with mbrede/amc_setfit
|
|
|
|
| 1 |
---
|
| 2 |
+
base_model:
|
| 3 |
+
- intfloat/multilingual-e5-large
|
| 4 |
library_name: setfit
|
| 5 |
metrics:
|
| 6 |
- f1
|
|
|
|
| 12 |
widget: []
|
| 13 |
inference: true
|
| 14 |
license: mit
|
| 15 |
+
language:
|
| 16 |
+
- multilingual
|
| 17 |
---
|
| 18 |
|
| 19 |
# SetFit with mbrede/amc_setfit
|