readme: ---
  language:
  - it
  - en
  - de
  - fr
  - es
  - multilingual
  license:
  - mit
  datasets:
  - xtreme
  metrics:
  - precision: 0.874
  - recall: 0.88
  - f1: 0.877
  - accuracy: 0.943
  inference:
    parameters:
      aggregation_strategy: first
  ---
  
  # gunghio/xlm-roberta-base-finetuned-panx-ner
  
  This model was trained starting from xlm-roberta-base on a subset of xtreme dataset.
  
  `xtreme` datasets subsets used are: PAN-X.{lang}. Language used for training/validation are: italian, english, german, french and spanish.
  
  Only 75% of the whole dataset was used.
  
  ## Intended uses & limitations
  
  Fine-tuned model can be used for Named Entity Recognition in it, en, de, fr, and es.
  
  ## Training and evaluation data
  
  Training dataset: [xtreme](https://huggingface.co/datasets/xtreme)
  
  ### Training results
  
  It achieves the following results on the evaluation set:
  
  - Precision: 0.8744154472771157
  - Recall: 0.8791424269015351
  - F1: 0.8767725659462058
  - Accuracy: 0.9432040948504613
  
  Details:
  
  | Label   | Precision | Recall | F1-Score | Support |
  |---------|-----------|--------|----------|---------|
  | PER     | 0.922     | 0.908  | 0.915    | 26639   |
  | LOC     | 0.880     | 0.906  | 0.892    | 37623   |
  | ORG     | 0.821     | 0.816  | 0.818    | 28045   |
  | Overall | 0.874     | 0.879  | 0.877    | 92307   |
  
  ## Usage
  
  Set aggregation stragey according to [documentation](https://huggingface.co/docs/transformers/v4.18.0/en/main_classes/pipelines#transformers.TokenClassificationPipeline).
  
  ```python
  from transformers import AutoTokenizer, AutoModelForTokenClassification
  from transformers import pipeline
  
  tokenizer = AutoTokenizer.from_pretrained("gunghio/xlm-roberta-base-finetuned-panx-ner")
  model = AutoModelForTokenClassification.from_pretrained("gunghio/xlm-roberta-base-finetuned-panx-ner")
  
  nlp = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="first")
  example = "My name is Wolfgang and I live in Berlin"
  
  ner_results = nlp(example)
  print(ner_results)
  ```
  