readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: xlm-roberta-large-NER-AstrID
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # xlm-roberta-large-NER-AstrID
  
  This model is a fine-tuned version of [xlm-roberta-large-finetuned-conll03-english](https://huggingface.co/xlm-roberta-large-finetuned-conll03-english) on the [WIESP2022-NER dataset](https://huggingface.co/datasets/fgrezes/WIESP2022-NER/blob/main/ner_tags.json).
  It achieves the following results on a sample evaluation set (from the dev set):
  - Loss: 0.1950
  - Precision: 0.8124
  - Recall: 0.8140
  - F1: 0.8132
  - Accuracy: 0.9473
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 2e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 220  | 0.3204          | 0.6974    | 0.7064 | 0.7019 | 0.9229   |
  | No log        | 2.0   | 440  | 0.2166          | 0.8083    | 0.8072 | 0.8078 | 0.9462   |
  | 0.3277        | 3.0   | 660  | 0.1950          | 0.8124    | 0.8140 | 0.8132 | 0.9473   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.12.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  