readme: ---
  language: 
  - el
  - en
  
  tags:
  - xlm-roberta-base
  datasets:
  - multi_nli
  - snli
  - allnli_greek
  
  metrics:
  - accuracy
  pipeline_tag: zero-shot-classification
  widget:
  - text: "Η Facebook κυκλοφόρησε τα πρώτα «έξυπνα» γυαλιά επαυξημένης πραγματικότητας."
    candidate_labels: "τεχνολογία, πολιτική, αθλητισμός"
    multi_class: false
  license: apache-2.0
  ---
  
  # Cross-Encoder for Greek Natural Language Inference (Textual Entailment) & Zero-Shot Classification 
  ## By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC)
  
  This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
  
  ## Training Data
  The model was trained on the the combined Greek+English version of the AllNLI dataset(sum of [SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/)). The Greek part was created using the EN2EL NMT model available [here](https://huggingface.co/lighteternal/SSE-TUC-mt-en-el-cased).
  
  The model can be used in two ways:
  * NLI/Textual Entailment: For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.
  * Zero-shot classification through the Huggingface pipeline: Given a sentence and a set of labels/topics, it will output the likelihood of the sentence belonging to each of the topic. Under the hood, the logit for entailment between the sentence and each label is taken as the logit for the candidate label being valid.
  
  ## Performance
  
  Evaluation on classification accuracy (entailment, contradiction, neutral) on mixed (Greek+English) AllNLI-dev set:
  
  | Metric | Value |
  | --- | --- |
  | Accuracy | 0.8409 |
  
  
  
  ## To use the model for NLI/Textual Entailment
  
  #### Usage with sentence_transformers
  
  Pre-trained models can be used like this:
  ```python
  from sentence_transformers import CrossEncoder
  model = CrossEncoder('lighteternal/nli-xlm-r-greek')
  scores = model.predict([('Δύο άνθρωποι συναντιούνται στο δρόμο', 'Ο δρόμος έχει κόσμο'),
                          ('Ένα μαύρο αυτοκίνητο ξεκινάει στη μέση του πλήθους.', 'Ένας άντρας οδηγάει σε ένα μοναχικό δρόμο'), 
                          ('Δυο γυναίκες μιλάνε στο κινητό', 'Το τραπέζι ήταν πράσινο')])
  
  
  #Convert scores to labels
  label_mapping = ['contradiction', 'entailment', 'neutral']
  labels = [label_mapping[score_max] for score_max in scores.argmax(axis=1)]
  print(scores, labels)
  
  # Οutputs
  #[[-3.1526504  2.9981945 -0.3108107]
  # [ 5.0549307 -2.757949  -1.6220676]
  # [-0.5124733 -2.2671669  3.1630592]] ['entailment', 'contradiction', 'neutral']
   ```
   
  #### Usage with Transformers AutoModel
  You can use the model also directly with Transformers library (without SentenceTransformers library):
  ```python
  from transformers import AutoTokenizer, AutoModelForSequenceClassification
  import torch
  
  model = AutoModelForSequenceClassification.from_pretrained('lighteternal/nli-xlm-r-greek')
  tokenizer = AutoTokenizer.from_pretrained('lighteternal/nli-xlm-r-greek')
  
  features = tokenizer(['Δύο άνθρωποι συναντιούνται στο δρόμο', 'Ο δρόμος έχει κόσμο'], 
                      ['Ένα μαύρο αυτοκίνητο ξεκινάει στη μέση του πλήθους.', 'Ένας άντρας οδηγάει σε ένα μοναχικό δρόμο.'],  
                      padding=True, truncation=True, return_tensors="pt")
  
  model.eval()
  with torch.no_grad():
      scores = model(**features).logits
      label_mapping = ['contradiction', 'entailment', 'neutral']
      labels = [label_mapping[score_max] for score_max in scores.argmax(dim=1)]
      print(labels)
  ```
  
  ## To use the model for Zero-Shot Classification
  This model can also be used for zero-shot-classification:
  ```python
  from transformers import pipeline
  
  classifier = pipeline("zero-shot-classification", model='lighteternal/nli-xlm-r-greek')
  
  sent = "Το Facebook κυκλοφόρησε τα πρώτα «έξυπνα» γυαλιά επαυξημένης πραγματικότητας"
  candidate_labels = ["πολιτική", "τεχνολογία", "αθλητισμός"]
  res = classifier(sent, candidate_labels)
  print(res)
  
  #outputs:
  #{'sequence': 'Το Facebook κυκλοφόρησε τα πρώτα «έξυπνα» γυαλιά επαυξημένης πραγματικότητας', 'labels': ['τεχνολογία', 'αθλητισμός', 'πολιτική'], 'scores': [0.8380699157714844, 0.09086982160806656, 0.07106029987335205]}
  ``` 
  ### Acknowledgement
  The research work was supported by the Hellenic Foundation for Research and Innovation (HFRI) under the HFRI PhD Fellowship grant (Fellowship Number:50, 2nd call)
  
  ### Citation info
  Citation for the Greek model TBA.
  Based on the work [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084)
  Kudos to @nreimers (Nils Reimers) for his support on Github . 
  