readme: ---
  tags:
  - autotrain
  - token-classification
  language:
  - en
  widget:
  - text: >-
      He at the last appointed him on one, And let all others from his hearte gon,
      And chose her of his own authority; For love is blind all day, and may not
      see.
  - text: >-
      I’m sorry but I just can’t seem to wrap my head around it. - I’m sorry but I
      just can’t seem to understand.
  - text: Why are you so bent out of shape? - Why are you so upset?
  - text: Listen, it is easier said than done, many people lack commitment.
  co2_eq_emissions:
    emissions: 0.04215761331893144
  license: mit
  library_name: transformers
  pipeline_tag: token-classification
  ---
  
  # Fine-tune datasets
   - MAGPIE corpus: https://aclanthology.org/2020.lrec-1.35/
   - EPIE corpus: https://link.springer.com/content/pdf/10.1007/978-3-030-58323-1.pdf
  
  
  # Model Trained Using AutoTrain
  
  - Problem type: Entity Extraction
  - Model ID: 1595156286
  - CO2 Emissions (in grams): 0.0422
  
  ## Validation Metrics
  
  - Loss: 0.012
  - Accuracy: 0.996
  - Precision: 0.000
  - Recall: 0.000
  - F1: 0.000
  
  ## Usage
  
  ### You can use cURL to access this model:
  
  ```
  $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/imranraad/autotrain-magpie-epie-combine-xlmr-metaphor-1595156286
  ```
  
  ### Or Python API:
  
  ```
  from transformers import AutoModelForTokenClassification, AutoTokenizer
  
  model = AutoModelForTokenClassification.from_pretrained("imranraad/autotrain-magpie-epie-combine-xlmr-metaphor-1595156286", use_auth_token=True)
  
  tokenizer = AutoTokenizer.from_pretrained("imranraad/autotrain-magpie-epie-combine-xlmr-metaphor-1595156286", use_auth_token=True)
  
  inputs = tokenizer("I love AutoTrain", return_tensors="pt")
  
  outputs = model(**inputs)
  ```
  
  ### How to get the idioms:
  
  ```
  from transformers import AutoTokenizer, AutoModelForTokenClassification
  from transformers import pipeline
  
  model = AutoModelForTokenClassification.from_pretrained("imranraad/idiom-xlm-roberta")
  
  tokenizer = AutoTokenizer.from_pretrained("imranraad/idiom-xlm-roberta")
  
  pipeline_idioms = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
  
  text = "Why are you so bent out of shape? - Why are you so upset?"
  
  idioms = pipeline_idioms(text)
  for idiom in idioms:
      if idiom['entity_group'] == '1':
          print(idiom['word'])
  ```