Text Classification
setfit
Safetensors
sentence-transformers
English
bert
sentiment-analysis
few-shot-learning
Eval Results (legacy)
text-embeddings-inference
Instructions to use loganh274/nlp-testing-setfit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use loganh274/nlp-testing-setfit with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("loganh274/nlp-testing-setfit") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - sentence-transformers
How to use loganh274/nlp-testing-setfit with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("loganh274/nlp-testing-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
Upload classification_report.txt with huggingface_hub
Browse files- classification_report.txt +11 -0
classification_report.txt
ADDED
|
@@ -0,0 +1,11 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
precision recall f1-score support
|
| 2 |
+
|
| 3 |
+
0 0.82 0.70 0.76 20
|
| 4 |
+
1 0.60 0.75 0.67 20
|
| 5 |
+
2 0.74 0.85 0.79 20
|
| 6 |
+
3 1.00 0.75 0.86 20
|
| 7 |
+
4 1.00 1.00 1.00 20
|
| 8 |
+
|
| 9 |
+
accuracy 0.81 100
|
| 10 |
+
macro avg 0.83 0.81 0.81 100
|
| 11 |
+
weighted avg 0.83 0.81 0.81 100
|