Update README.md

README.md

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   hypothesis_template: "This is {}." 
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-ETH Zeroshot
+# 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]