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	---
	language:
	- en
	tags:
	- text-classification
	- zero-shot-classification
	license: mit
	metrics:
	- accuracy
	datasets:
	- multi_nli
	- anli
	- fever
	- lingnli
	- alisawuffles/WANLI
	pipeline_tag: zero-shot-classification
	#- text-classification
	#widget:
	#- text: "I first thought that I really liked the movie, but upon second thought it was actually disappointing. [SEP] The movie was not good."

	model-index: # info: https://github.com/huggingface/hub-docs/blame/main/modelcard.md
	- name: DeBERTa-v3-large-mnli-fever-anli-ling-wanli
	results:
	- task:
	type: text-classification # Required. Example: automatic-speech-recognition
	name: Natural Language Inference # Optional. Example: Speech Recognition
	dataset:
	type: multi_nli # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
	name: MultiNLI-matched # Required. A pretty name for the dataset. Example: Common Voice (French)
	split: validation_matched # Optional. Example: test
	metrics:
	- type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics
	value: 0,912 # Required. Example: 20.90
	#name: # Optional. Example: Test WER
	verified: false # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
	- task:
	type: text-classification # Required. Example: automatic-speech-recognition
	name: Natural Language Inference # Optional. Example: Speech Recognition
	dataset:
	type: multi_nli # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
	name: MultiNLI-mismatched # Required. A pretty name for the dataset. Example: Common Voice (French)
	split: validation_mismatched # Optional. Example: test
	metrics:
	- type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics
	value: 0,908 # Required. Example: 20.90
	#name: # Optional. Example: Test WER
	verified: false # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
	- task:
	type: text-classification # Required. Example: automatic-speech-recognition
	name: Natural Language Inference # Optional. Example: Speech Recognition
	dataset:
	type: anli # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
	name: ANLI-all # Required. A pretty name for the dataset. Example: Common Voice (French)
	split: test_r1+test_r2+test_r3 # Optional. Example: test
	metrics:
	- type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics
	value: 0,702 # Required. Example: 20.90
	#name: # Optional. Example: Test WER
	verified: false # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
	- task:
	type: text-classification # Required. Example: automatic-speech-recognition
	name: Natural Language Inference # Optional. Example: Speech Recognition
	dataset:
	type: anli # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
	name: ANLI-r3 # Required. A pretty name for the dataset. Example: Common Voice (French)
	split: test_r3 # Optional. Example: test
	metrics:
	- type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics
	value: 0,64 # Required. Example: 20.90
	#name: # Optional. Example: Test WER
	verified: false # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
	- task:
	type: text-classification # Required. Example: automatic-speech-recognition
	name: Natural Language Inference # Optional. Example: Speech Recognition
	dataset:
	type: alisawuffles/WANLI # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
	name: WANLI # Required. A pretty name for the dataset. Example: Common Voice (French)
	split: test # Optional. Example: test
	metrics:
	- type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics
	value: 0,77 # Required. Example: 20.90
	#name: # Optional. Example: Test WER
	verified: false # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
	- task:
	type: text-classification # Required. Example: automatic-speech-recognition
	name: Natural Language Inference # Optional. Example: Speech Recognition
	dataset:
	type: lingnli # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
	name: LingNLI # Required. A pretty name for the dataset. Example: Common Voice (French)
	split: test # Optional. Example: test
	metrics:
	- type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics
	value: 0,87 # Required. Example: 20.90
	#name: # Optional. Example: Test WER
	verified: false # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).



	---

	# DeBERTa-v3-large-mnli-fever-anli-ling-wanli
	## Model description
	This model was fine-tuned on the [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), Adversarial-NLI ([ANLI](https://huggingface.co/datasets/anli)), [LingNLI](https://arxiv.org/pdf/2104.07179.pdf) and [WANLI](https://huggingface.co/datasets/alisawuffles/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](https://github.com/facebookresearch/anli).

	The foundation model is [DeBERTa-v3-large from Microsoft](https://huggingface.co/microsoft/deberta-v3-large). DeBERTa-v3 combines several recent innovations compared to classical Masked Language Models like BERT, RoBERTa etc., see the [paper](https://arxiv.org/abs/2111.09543)


	### How to use the model
	#### Simple zero-shot classification pipeline
	```python
	from transformers import pipeline
	classifier = pipeline("zero-shot-classification", model="MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli")
	sequence_to_classify = "Angela Merkel is a politician in Germany and leader of the CDU"
	candidate_labels = ["politics", "economy", "entertainment", "environment"]
	output = classifier(sequence_to_classify, candidate_labels, multi_label=False)
	print(output)
	```
	#### NLI use-case
	```python
	from transformers import AutoTokenizer, AutoModelForSequenceClassification
	import torch
	device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

	model_name = "MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli"
	tokenizer = AutoTokenizer.from_pretrained(model_name)
	model = AutoModelForSequenceClassification.from_pretrained(model_name)

	premise = "I first thought that I liked the movie, but upon second thought it was actually disappointing."
	hypothesis = "The movie was not good."

	input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt")
	output = model(input["input_ids"].to(device)) # device = "cuda:0" or "cpu"
	prediction = torch.softmax(output["logits"][0], -1).tolist()
	label_names = ["entailment", "neutral", "contradiction"]
	prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)}
	print(prediction)
	```

	### Training data
	DeBERTa-v3-large-mnli-fever-anli-ling-wanli was trained on the [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), Adversarial-NLI ([ANLI](https://huggingface.co/datasets/anli)), [LingNLI](https://arxiv.org/pdf/2104.07179.pdf) and [WANLI](https://huggingface.co/datasets/alisawuffles/WANLI) datasets, which comprise 885 242 NLI hypothesis-premise pairs. Note that [SNLI](https://huggingface.co/datasets/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](https://github.com/facebookresearch/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](https://www.linkedin.com/in/moritz-laurer/)

	### 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.