README.md
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1 ---
2 language: en
3 pipeline_tag: zero-shot-classification
4 tags:
5 - transformers
6 datasets:
7 - nyu-mll/multi_nli
8 - stanfordnlp/snli
9 metrics:
10 - accuracy
11 license: apache-2.0
12 base_model:
13 - microsoft/deberta-v3-large
14 library_name: sentence-transformers
15 ---
16
17 # Cross-Encoder for Natural Language Inference
18 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)
19
20 ## Training Data
21 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.
22
23 ## Performance
24 - Accuracy on SNLI-test dataset: 92.20
25 - Accuracy on MNLI mismatched set: 90.49
26
27 For futher evaluation results, see [SBERT.net - Pretrained Cross-Encoder](https://www.sbert.net/docs/pretrained_cross-encoders.html#nli).
28
29 ## Usage
30
31 Pre-trained models can be used like this:
32 ```python
33 from sentence_transformers import CrossEncoder
34 model = CrossEncoder('cross-encoder/nli-deberta-v3-large')
35 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.')])
36
37 #Convert scores to labels
38 label_mapping = ['contradiction', 'entailment', 'neutral']
39 labels = [label_mapping[score_max] for score_max in scores.argmax(axis=1)]
40 ```
41
42 ## Usage with Transformers AutoModel
43 You can use the model also directly with Transformers library (without SentenceTransformers library):
44 ```python
45 from transformers import AutoTokenizer, AutoModelForSequenceClassification
46 import torch
47
48 model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/nli-deberta-v3-large')
49 tokenizer = AutoTokenizer.from_pretrained('cross-encoder/nli-deberta-v3-large')
50
51 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")
52
53 model.eval()
54 with torch.no_grad():
55 scores = model(**features).logits
56 label_mapping = ['contradiction', 'entailment', 'neutral']
57 labels = [label_mapping[score_max] for score_max in scores.argmax(dim=1)]
58 print(labels)
59 ```
60
61 ## Zero-Shot Classification
62 This model can also be used for zero-shot-classification:
63 ```python
64 from transformers import pipeline
65
66 classifier = pipeline("zero-shot-classification", model='cross-encoder/nli-deberta-v3-large')
67
68 sent = "Apple just announced the newest iPhone X"
69 candidate_labels = ["technology", "sports", "politics"]
70 res = classifier(sent, candidate_labels)
71 print(res)
72 ```