nli-roberta-base /
nreimers's picture
language: en
pipeline_tag: zero-shot-classification
- roberta-base
- multi_nli
- snli
- accuracy
license: apache-2.0
# Cross-Encoder for Natural Language Inference
This model was trained using [SentenceTransformers]( [Cross-Encoder]( class.
## Training Data
The model was trained on the [SNLI]( and [MultiNLI]( datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.
## Performance
For evaluation results, see [ - Pretrained Cross-Encoder](
## Usage
Pre-trained models can be used like this:
from sentence_transformers import CrossEncoder
model = CrossEncoder('cross-encoder/nli-roberta-base')
scores = model.predict([('A man is eating pizza', 'A man eats something'), ('A black race car starts up in front of a crowd of people.', 'A man is driving down a lonely road.')])
#Convert scores to labels
label_mapping = ['contradiction', 'entailment', 'neutral']
labels = [label_mapping[score_max] for score_max in scores.argmax(axis=1)]
## Usage with Transformers AutoModel
You can use the model also directly with Transformers library (without SentenceTransformers library):
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/nli-roberta-base')
tokenizer = AutoTokenizer.from_pretrained('cross-encoder/nli-roberta-base')
features = tokenizer(['A man is eating pizza', 'A black race car starts up in front of a crowd of people.'], ['A man eats something', 'A man is driving down a lonely road.'], padding=True, truncation=True, return_tensors="pt")
with torch.no_grad():
scores = model(**features).logits
label_mapping = ['contradiction', 'entailment', 'neutral']
labels = [label_mapping[score_max] for score_max in scores.argmax(dim=1)]
## Zero-Shot Classification
This model can also be used for zero-shot-classification:
from transformers import pipeline
classifier = pipeline("zero-shot-classification", model='cross-encoder/nli-roberta-base')
sent = "Apple just announced the newest iPhone X"
candidate_labels = ["technology", "sports", "politics"]
res = classifier(sent, candidate_labels)