Zero-Shot Classification
sentence-transformers
PyTorch
JAX
ONNX
Safetensors
OpenVINO
Transformers
English
roberta
text-classification
Instructions to use cross-encoder/nli-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cross-encoder/nli-roberta-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cross-encoder/nli-roberta-base") 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] - Transformers
How to use cross-encoder/nli-roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="cross-encoder/nli-roberta-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cross-encoder/nli-roberta-base") model = AutoModelForSequenceClassification.from_pretrained("cross-encoder/nli-roberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
nreimers commited on
Commit ·
041642e
1
Parent(s): 1a7d51c
up
Browse files
README.md
CHANGED
|
@@ -1,6 +1,6 @@
|
|
| 1 |
---
|
| 2 |
language: en
|
| 3 |
-
pipeline_tag:
|
| 4 |
tags:
|
| 5 |
- roberta-base
|
| 6 |
datasets:
|
|
@@ -8,6 +8,8 @@ datasets:
|
|
| 8 |
- snli
|
| 9 |
metrics:
|
| 10 |
- accuracy
|
|
|
|
|
|
|
| 11 |
---
|
| 12 |
|
| 13 |
# Cross-Encoder for Natural Language Inference
|
|
|
|
| 1 |
---
|
| 2 |
language: en
|
| 3 |
+
pipeline_tag: classification
|
| 4 |
tags:
|
| 5 |
- roberta-base
|
| 6 |
datasets:
|
|
|
|
| 8 |
- snli
|
| 9 |
metrics:
|
| 10 |
- accuracy
|
| 11 |
+
widget:
|
| 12 |
+
- text: "I like you. </s></s> I love you.
|
| 13 |
---
|
| 14 |
|
| 15 |
# Cross-Encoder for Natural Language Inference
|