readme: ---
  language: 
  - en
  license: mit
  tags:
  - text-classification
  - zero-shot-classification
  metrics:
  - accuracy
  widget:
  - text: "I first thought that I liked the movie, but upon second thought it was actually disappointing. [SEP] The movie was good."
  
  ---
  # DeBERTa-v3-base-mnli-fever-docnli-ling-2c
  ## Model description
  This model was trained on 1.279.665 hypothesis-premise pairs from 8 NLI datasets: [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), [LingNLI](https://arxiv.org/abs/2104.07179) and [DocNLI](https://arxiv.org/pdf/2106.09449.pdf) (which includes [ANLI](https://github.com/facebookresearch/anli), QNLI, DUC, CNN/DailyMail, Curation). 
  
  It is the only model in the model hub trained on 8 NLI datasets, including DocNLI with very long texts to learn long range reasoning. Note that the model was trained on binary NLI to predict either "entailment" or "not-entailment". The DocNLI merges the classes "neural" and "contradiction" into "not-entailment" to enable the inclusion of the DocNLI dataset. 
  
  The base model is [DeBERTa-v3-base from Microsoft](https://huggingface.co/microsoft/deberta-v3-base). The v3 variant of DeBERTa substantially outperforms previous versions of the model by including a different pre-training objective, see annex 11 of the original [DeBERTa paper](https://arxiv.org/pdf/2006.03654.pdf) as well as the [DeBERTa-V3 paper](https://arxiv.org/abs/2111.09543). 
  
  For highest performance (but less speed), I recommend using https://huggingface.co/MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli.
  
  
  ### How to use the model
  #### Simple zero-shot classification pipeline
  ```python
  from transformers import pipeline
  classifier = pipeline("zero-shot-classification", model="MoritzLaurer/DeBERTa-v3-base-mnli-fever-docnli-ling-2c")
  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-base-mnli-fever-docnli-ling-2c"
  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 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", "not_entailment"]
  prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)}
  print(prediction)
  ```
  ### Training data
  This model was trained on 1.279.665 hypothesis-premise pairs from 8 NLI datasets: [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), [LingNLI](https://arxiv.org/abs/2104.07179) and [DocNLI](https://arxiv.org/pdf/2106.09449.pdf) (which includes [ANLI](https://github.com/facebookresearch/anli), QNLI, DUC, CNN/DailyMail, Curation).
  
  ### Training procedure
  DeBERTa-v3-small-mnli-fever-docnli-ling-2c was trained using the Hugging Face trainer with the following hyperparameters.
  ```
  training_args = TrainingArguments(
      num_train_epochs=3,              # total number of training epochs
      learning_rate=2e-05,
      per_device_train_batch_size=32,   # batch size per device during training
      per_device_eval_batch_size=32,    # batch size for evaluation
      warmup_ratio=0.1,                # number of warmup steps for learning rate scheduler
      weight_decay=0.06,               # strength of weight decay
      fp16=True                        # mixed precision training
  )
  ```
  ### Eval results
  The model was evaluated using the binary test sets for MultiNLI and ANLI and the binary dev set for Fever-NLI (two classes instead of three). The metric used is accuracy.
  
  mnli-m-2c | mnli-mm-2c | fever-nli-2c | anli-all-2c | anli-r3-2c | lingnli-2c
  ---------|----------|---------|----------|----------|------
  0.935 | 0.933 | 0.897 | 0.710 | 0.678 |  0.895
  
  
  ## Limitations and bias
  Please consult the original DeBERTa paper and literature on different NLI datasets for potential biases. 
  
  ## 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://o