This is the DeepPavlov/rubert-base-cased fine-tuned to predict the logical relationship between two short texts: entailment, contradiction, or neutral.

Usage

How to run the model for NLI:

python

# !pip install transformers sentencepiece --quiet
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_checkpoint = 'cointegrated/rubert-base-cased-nli-threeway'
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)
if torch.cuda.is_available():
    model.cuda()

text1 = 'Сократ - человек, а все люди смертны.'
text2 = 'Сократ никогда не умрёт.'
with torch.inference_mode():
    out = model(**tokenizer(text1, text2, return_tensors='pt').to(model.device))
    proba = torch.softmax(out.logits, -1).cpu().numpy()[0]
print({v: proba[k] for k, v in model.config.id2label.items()})
# {'entailment': 0.009525929, 'contradiction': 0.9332064, 'neutral': 0.05726764} 

You can also use this model for zero-shot short text classification (by labels only), e.g. for sentiment analysis:

python

def predict_zero_shot(text, label_texts, model, tokenizer, label='entailment', normalize=True):
    label_texts
    tokens = tokenizer([text] * len(label_texts), label_texts, truncation=True, return_tensors='pt', padding=True)
    with torch.inference_mode():
        result = torch.softmax(model(**tokens.to(model.device)).logits, -1)
    proba = result[:, model.config.label2id[label]].cpu().numpy()
    if normalize:
        proba /= sum(proba)
    return proba

classes = ['Я доволен', 'Я недоволен']
predict_zero_shot('Какая гадость эта ваша заливная рыба!', classes, model, tokenizer)
# array([0.05609814, 0.9439019 ], dtype=float32)
predict_zero_shot('Какая вкусная эта ваша заливная рыба!', classes, model, tokenizer)
# array([0.9059292 , 0.09407079], dtype=float32)

Alternatively, you can use Huggingface pipelines for inference.

Sources

The model has been trained on a series of NLI datasets automatically translated to Russian from English.

Most datasets were taken from the repo of Felipe Salvatore: JOCI, MNLI, MPE, SICK, SNLI.

Some datasets obtained from the original sources: ANLI, NLI-style FEVER, IMPPRES.

Performance

The table below shows ROC AUC (one class vs rest) for five models on the corresponding dev sets:

tiny: a small BERT predicting entailment vs not_entailment
twoway: a base-sized BERT predicting entailment vs not_entailment
threeway (this model): a base-sized BERT predicting entailment vs contradiction vs neutral
vicgalle-xlm: a large multilingual NLI model
facebook-bart: a large multilingual NLI model

model	add_one_rte	anli_r1	anli_r2	anli_r3	copa	fever	help	iie	imppres	joci	mnli	monli	mpe	scitail	sick	snli	terra	total
n_observations	387	1000	1000	1200	200	20474	3355	31232	7661	939	19647	269	1000	2126	500	9831	307	101128
tiny/entailment	0.77	0.59	0.52	0.53	0.53	0.90	0.81	0.78	0.93	0.81	0.82	0.91	0.81	0.78	0.93	0.95	0.67	0.77
twoway/entailment	0.89	0.73	0.61	0.62	0.58	0.96	0.92	0.87	0.99	0.90	0.90	0.99	0.91	0.96	0.97	0.97	0.87	0.86
threeway/entailment	0.91	0.75	0.61	0.61	0.57	0.96	0.56	0.61	0.99	0.90	0.91	0.67	0.92	0.84	0.98	0.98	0.90	0.80
vicgalle-xlm/entailment	0.88	0.79	0.63	0.66	0.57	0.93	0.56	0.62	0.77	0.80	0.90	0.70	0.83	0.84	0.91	0.93	0.93	0.78
facebook-bart/entailment	0.51	0.41	0.43	0.47	0.50	0.74	0.55	0.57	0.60	0.63	0.70	0.52	0.56	0.68	0.67	0.72	0.64	0.58
threeway/contradiction		0.71	0.64	0.61		0.97			1.00	0.77	0.92		0.89		0.99	0.98		0.85
threeway/neutral		0.79	0.70	0.62		0.91			0.99	0.68	0.86		0.79		0.96	0.96		0.83

For evaluation (and for training of the tiny and twoway models), some extra datasets were used: Add-one RTE, CoPA, IIE, and SCITAIL taken from the repo of Felipe Salvatore and translatted, HELP and MoNLI taken from the original sources and translated, and Russian TERRa.