This model was fine-tuned on the MultiNLI, Fever-NLI, Adversarial-NLI (ANLI), LingNLI and WANLI datasets, which comprise 885 242 NLI hypothesis-premise pairs. This model is the best performing NLI model on the Hugging Face Hub as of 06.06.22 and can be used for zero-shot classification. It significantly outperforms all other large models on the ANLI benchmark.
The foundation model is DeBERTa-v3-large from Microsoft. DeBERTa-v3 combines several recent innovations compared to classical Masked Language Models like BERT, RoBERTa etc., see the paper
How to use the model
Simple zero-shot classification pipeline
python
1from transformers import pipeline
2classifier = pipeline("zero-shot-classification", model="MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli")3sequence_to_classify ="Angela Merkel is a politician in Germany and leader of the CDU"4candidate_labels =["politics","economy","entertainment","environment"]5output = classifier(sequence_to_classify, candidate_labels, multi_label=False)6print(output)
NLI use-case
python
1from transformers import AutoTokenizer, AutoModelForSequenceClassification
2import torch
3device = torch.device("cuda")if torch.cuda.is_available()else torch.device("cpu")45model_name ="MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli"6tokenizer = AutoTokenizer.from_pretrained(model_name)7model = AutoModelForSequenceClassification.from_pretrained(model_name)89premise ="I first thought that I liked the movie, but upon second thought it was actually disappointing."10hypothesis ="The movie was not good."1112input= tokenizer(premise, hypothesis, truncation=True, return_tensors="pt")13output = model(input["input_ids"].to(device))# device = "cuda:0" or "cpu"14prediction = torch.softmax(output["logits"][0],-1).tolist()15label_names =["entailment","neutral","contradiction"]16prediction ={name:round(float(pred)*100,1)for pred, name inzip(prediction, label_names)}17print(prediction)
Training data
DeBERTa-v3-large-mnli-fever-anli-ling-wanli was trained on the MultiNLI, Fever-NLI, Adversarial-NLI (ANLI), LingNLI and WANLI datasets, which comprise 885 242 NLI hypothesis-premise pairs. Note that SNLI was explicitly excluded due to quality issues with the dataset. More data does not necessarily make for better NLI models.
Training procedure
DeBERTa-v3-large-mnli-fever-anli-ling-wanli was trained using the Hugging Face trainer with the following hyperparameters. Note that longer training with more epochs hurt performance in my tests (overfitting).
training_args = TrainingArguments(
num_train_epochs=4, # total number of training epochs
learning_rate=5e-06,
per_device_train_batch_size=16, # batch size per device during training
gradient_accumulation_steps=2, # doubles the effective batch_size to 32, while decreasing memory requirements
per_device_eval_batch_size=64, # batch size for evaluation
warmup_ratio=0.06, # number of warmup steps for learning rate scheduler
weight_decay=0.01, # strength of weight decay
fp16=True # mixed precision training
)
Eval results
The model was evaluated using the test sets for MultiNLI, ANLI, LingNLI, WANLI and the dev set for Fever-NLI. The metric used is accuracy.
The model achieves state-of-the-art performance on each dataset. Surprisingly, it outperforms the previous state-of-the-art on ANLI (ALBERT-XXL) by 8,3%. I assume that this is because ANLI was created to fool masked language models like RoBERTa (or ALBERT), while DeBERTa-v3 uses a better pre-training objective (RTD), disentangled attention and I fine-tuned it on higher quality NLI data.
Datasets
mnli_test_m
mnli_test_mm
anli_test
anli_test_r3
ling_test
wanli_test
Accuracy
0.912
0.908
0.702
0.64
0.87
0.77
Speed (text/sec, A100 GPU)
696.0
697.0
488.0
425.0
828.0
980.0
Limitations and bias
Please consult the original DeBERTa-v3 paper and literature on different NLI datasets for more information on the training data and potential biases. The model will reproduce statistical patterns in the training data.
Citation
If you use this model, please cite: Laurer, Moritz, Wouter van Atteveldt, Andreu Salleras Casas, and Kasper Welbers. 2022. ‘Less Annotating, More Classifying – Addressing the Data Scarcity Issue of Supervised Machine Learning with Deep Transfer Learning and BERT - NLI’. Preprint, June. Open Science Framework. https://osf.io/74b8k.
Ideas for cooperation or questions?
If you have questions or ideas for cooperation, contact me at m{dot}laurer{at}vu{dot}nl or LinkedIn
Debugging and issues
Note that DeBERTa-v3 was released on 06.12.21 and older versions of HF Transformers seem to have issues running the model (e.g. resulting in an issue with the tokenizer). Using Transformers>=4.13 might solve some issues.
