ragarwal/deberta-v3-base-nli-mixer-binary

NLI-Mixer is an attempt to tackle the Natural Language Inference (NLI) task by mixing multiple datasets together.

The approach is simple:

1. Combine all available NLI data without any domain-dependent re-balancing or re-weighting.
2. Finetune several SOTA transformers of different sizes (20m parameters to 300m parameters) on the combined data.
3. Evaluate on challenging NLI datasets.

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

Data

20+ NLI datasets were combined to train a binary classification model. The contradiction and neutral labels were combined to form a non-entailment class.

Usage

In Transformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
from torch.nn.functional import softmax, sigmoid

device = "cuda" if torch.cuda.is_available() else "cpu"

model_name="ragarwal/deberta-v3-base-nli-mixer-binary"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

sentence = "During its monthly call, the National Oceanic and Atmospheric Administration warned of \
increased temperatures and low precipitation" 
labels = ["Computer", "Climate Change", "Tablet", "Football", "Artificial Intelligence", "Global Warming"] 

features = tokenizer([[sentence, l] for l in labels], padding=True, truncation=True, return_tensors="pt")

model.eval()
with torch.no_grad():
    scores = model(**features).logits
    print("Multi-Label:", sigmoid(scores)) #Multi-Label Classification
    print("Single-Label:", softmax(scores, dim=0)) #Single-Label Classification

#Multi-Label: tensor([[0.0412],[0.2436],[0.0394],[0.0020],[0.0050],[0.1424]])
#Single-Label: tensor([[0.0742],[0.5561],[0.0709],[0.0035],[0.0087],[0.2867]])

In Sentence-Transformers

from sentence_transformers import CrossEncoder

model_name="ragarwal/deberta-v3-base-nli-mixer-binary"
model = CrossEncoder(model_name, max_length=256)

sentence = "During its monthly call, the National Oceanic and Atmospheric Administration warned of \
increased temperatures and low precipitation" 
labels = ["Computer", "Climate Change", "Tablet", "Football", "Artificial Intelligence", "Global Warming"] 

scores = model.predict([[sentence, l] for l in labels])
print(scores) 
#array([0.04118565, 0.2435827 , 0.03941465, 0.00203637, 0.00501176, 0.1423797], dtype=float32)