readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - wikiann
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: albert-large-v2_ner_wikiann
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: wikiann
        type: wikiann
        args: en
      metrics:
      - name: Precision
        type: precision
        value: 0.8239671720684378
      - name: Recall
        type: recall
        value: 0.8374805598755832
      - name: F1
        type: f1
        value: 0.8306689103912495
      - name: Accuracy
        type: accuracy
        value: 0.926951922121784
  ---
  
  <!-- 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. -->
  
  # albert-large-v2_ner_wikiann
  
  This model is a fine-tuned version of [albert-large-v2](https://huggingface.co/albert-large-v2) on the wikiann dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3416
  - Precision: 0.8240
  - Recall: 0.8375
  - F1: 0.8307
  - Accuracy: 0.9270
  
  ## 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: 1e-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: cosine
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.3451        | 1.0   | 2500  | 0.3555          | 0.7745    | 0.7850 | 0.7797 | 0.9067   |
  | 0.2995        | 2.0   | 5000  | 0.2927          | 0.7932    | 0.8240 | 0.8083 | 0.9205   |
  | 0.252         | 3.0   | 7500  | 0.2936          | 0.8094    | 0.8236 | 0.8164 | 0.9239   |
  | 0.1676        | 4.0   | 10000 | 0.3302          | 0.8256    | 0.8359 | 0.8307 | 0.9268   |
  | 0.1489        | 5.0   | 12500 | 0.3416          | 0.8240    | 0.8375 | 0.8307 | 0.9270   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.11.0
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  