
**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](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. It is based on [microsoft/deberta-v3-base](https://huggingface.co/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

```python
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

```python
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)

```
{"license": "mit"}
transformers pytorch deberta-v2 text-classification license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #deberta-v2 #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us 


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




In Sentence-Transformers


### 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




In Sentence-Transformers
TAGS
#transformers #pytorch #deberta-v2 #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### 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




In Sentence-Transformers
passage: TAGS
#transformers #pytorch #deberta-v2 #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### 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




In Sentence-Transformers