readme: ---
  language: 
  - en
  license: mit
  tags:
  - text-classification
  - zero-shot-classification
  metrics:
  - accuracy
  datasets:
  - multi_nli
  - anli
  - fever
  - lingnli
  pipeline_tag: zero-shot-classification
  
  ---
  # DeBERTa-v3-xsmall-mnli-fever-anli-ling-binary
  ## Model description
  This model was trained on 782 357 hypothesis-premise pairs from 4 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 [ANLI](https://github.com/facebookresearch/anli). 
  
  Note that the model was trained on binary NLI to predict either "entailment" or "not-entailment". This is specifically designed for zero-shot classification, where the difference between "neutral" and "contradiction" is irrelevant. 
  
  The base model is [DeBERTa-v3-xsmall from Microsoft](https://huggingface.co/microsoft/deberta-v3-xsmall). The v3 variant of DeBERTa substantially outperforms previous versions of the model by including a different pre-training objective, see 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.
  
  ## Intended uses & limitations
  #### How to use the model
  ```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-xsmall-mnli-fever-anli-ling-binary"
  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 782 357 hypothesis-premise pairs from 4 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 [ANLI](https://github.com/facebookresearch/anli). 
  
  ### Training procedure
  DeBERTa-v3-xsmall-mnli-fever-anli-ling-binary was trained using the Hugging Face trainer with the following hyperparameters.
  ```
  training_args = TrainingArguments(
      num_train_epochs=5,              # 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, ANLI, LingNLI and the binary dev set for Fever-NLI (two classes instead of three). The metric used is accuracy.
  
  dataset | mnli-m-2c | mnli-mm-2c | fever-nli-2c | anli-all-2c | anli-r3-2c | lingnli-2c
  --------|---------|----------|---------|----------|----------|------
  accuracy | 0.925 | 0.922 | 0.892 | 0.676 | 0.665 | 0.888
  speed (text/sec, CPU, 128 batch) | 6.0 | 6.3 | 3.0 | 5.8 | 5.0 | 7.6
  speed (text/sec, GPU Tesla P100, 128 batch) | 473 | 487 | 230 | 390 | 340 | 586
  
  
  ## 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://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. 