
# Cross-Encoder for Natural Language Inference

This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. This model is based on [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large)

## Training Data
The model was trained on the [SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.

## Performance
- Accuracy on SNLI-test dataset: 92.20
- Accuracy on  MNLI mismatched set: 90.49

For futher evaluation results, see [SBERT.net - Pretrained Cross-Encoder](https://www.sbert.net/docs/pretrained_cross-encoders.html#nli).

## Usage
Pre-trained models can be used like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('cross-encoder/nli-deberta-v3-large')
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):
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/nli-deberta-v3-large')
tokenizer = AutoTokenizer.from_pretrained('cross-encoder/nli-deberta-v3-large')

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")

model.eval()
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)]
    print(labels)
```

## Zero-Shot Classification
This model can also be used for zero-shot-classification:
```python
from transformers import pipeline

classifier = pipeline("zero-shot-classification", model='cross-encoder/nli-deberta-v3-large')

sent = "Apple just announced the newest iPhone X"
candidate_labels = ["technology", "sports", "politics"]
res = classifier(sent, candidate_labels)
print(res)
``` 
{"language": "en", "license": "apache-2.0", "tags": ["microsoft/deberta-v3-large"], "datasets": ["multi_nli", "snli"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification"}
transformers pytorch deberta-v2 text-classification microsoft/deberta-v3-large zero-shot-classification en dataset:multi_nli dataset:snli license:apache-2.0 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #deberta-v2 #text-classification #microsoft/deberta-v3-large #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 


# Cross-Encoder for Natural Language Inference

This model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-large

## 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
- Accuracy on SNLI-test dataset: 92.20
- Accuracy on  MNLI mismatched set: 90.49

For futher evaluation results, see URL - Pretrained Cross-Encoder.

## Usage
Pre-trained models can be used like this:


## Usage with Transformers AutoModel
You can use the model also directly with Transformers library (without SentenceTransformers library):


## Zero-Shot Classification
This model can also be used for zero-shot-classification:
 
# Cross-Encoder for Natural Language Inference

This model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-large ## 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
- Accuracy on SNLI-test dataset: 92.20
- Accuracy on  MNLI mismatched set: 90.49

For futher evaluation results, see URL - Pretrained Cross-Encoder. ## Usage
Pre-trained models can be used like this: ## Usage with Transformers AutoModel
You can use the model also directly with Transformers library (without SentenceTransformers library): ## Zero-Shot Classification
This model can also be used for zero-shot-classification:
TAGS
#transformers #pytorch #deberta-v2 #text-classification #microsoft/deberta-v3-large #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Cross-Encoder for Natural Language Inference

This model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-large ## 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
- Accuracy on SNLI-test dataset: 92.20
- Accuracy on  MNLI mismatched set: 90.49

For futher evaluation results, see URL - Pretrained Cross-Encoder. ## Usage
Pre-trained models can be used like this: ## Usage with Transformers AutoModel
You can use the model also directly with Transformers library (without SentenceTransformers library): ## Zero-Shot Classification
This model can also be used for zero-shot-classification:
TAGS
#transformers #pytorch #deberta-v2 #text-classification #microsoft/deberta-v3-large #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
# Cross-Encoder for Natural Language Inference

This model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-large## 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
- Accuracy on SNLI-test dataset: 92.20
- Accuracy on  MNLI mismatched set: 90.49

For futher evaluation results, see URL - Pretrained Cross-Encoder.## Usage
Pre-trained models can be used like this:## Usage with Transformers AutoModel
You can use the model also directly with Transformers library (without SentenceTransformers library):## Zero-Shot Classification
This model can also be used for zero-shot-classification: