readme: ---
  pipeline_tag: zero-shot-classification
  datasets:
  - multi_nli
  widget:
  - text: "natural language processing"
    candidate_labels: "Location & Address, Employment, Organizational, Name, Service, Studies, Science"
    hypothesis_template: "This is {}." 
  ---
  
  # Fb_improved_zeroshot
  
  Zero-Shot Model designed to classify academic search logs in German and English. Developed by students at ETH Zürich.
  
  This model was trained using the [bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli/) checkpoint provided by Meta on Huggingface. It was then fine-tuned to suit the needs of this project.
  
  ## NLI-based Zero-Shot Text Classification
  
  This method is based on Natural Language Inference (NLI), see [Yin et al.](https://arxiv.org/abs/1909.00161).
  The following tutorials are taken from the model card of [bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli/).
  
  #### With the zero-shot classification pipeline
  The model can be loaded with the `zero-shot-classification` pipeline like so:
  ```python
  from transformers import pipeline
  classifier = pipeline("zero-shot-classification",
                        model="oigele/Fb_improved_zeroshot")
  ```
  You can then use this pipeline to classify sequences into any of the class names you specify.
  ```python
  sequence_to_classify = "natural language processing"
  candidate_labels = ['Location & Address', 'Employment', 'Organizational', 'Name', 'Service', 'Studies', 'Science']
  classifier(sequence_to_classify, candidate_labels)
  ```
  If more than one candidate label can be correct, pass `multi_class=True` to calculate each class independently:
  ```python
  candidate_labels = ['Location & Address', 'Employment', 'Organizational', 'Name', 'Service', 'Studies', 'Science']
  classifier(sequence_to_classify, candidate_labels, multi_class=True)
  ```
  #### With manual PyTorch
  ```python
  # pose sequence as a NLI premise and label as a hypothesis
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  nli_model = AutoModelForSequenceClassification.from_pretrained('oigele/Fb_improved_zeroshot/')
  tokenizer = AutoTokenizer.from_pretrained('facebook/bart-large-mnli')
  premise = sequence
  hypothesis = f'This is {label}.'
  # run through model pre-trained on MNLI
  x = tokenizer.encode(premise, hypothesis, return_tensors='pt',
                       truncation_strategy='only_first')
  logits = nli_model(x.to(device))[0]
  # we throw away "neutral" (dim 1) and take the probability of
  # "entailment" (2) as the probability of the label being true 
  entail_contradiction_logits = logits[:,[0,2]]
  probs = entail_contradiction_logits.softmax(dim=1)
  prob_label_is_true = probs[:,1]
  