readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - surrey-nlp/PLOD-unfiltered
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  language:
  - en
  widget:
  - text: "Light dissolved inorganic carbon (DIC) resulting from the oxidation of hydrocarbons."
  - text: "RAFs are plotted for a selection of neurons in the dorsal zone (DZ) of auditory cortex in Figure 1."
  - text: "Images were acquired using a GE 3.0T MRI scanner with an upgrade for echo-planar imaging (EPI)."
  model-index:
  - name: albert-large-v2-finetuned-ner_with_callbacks
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: surrey-nlp/PLOD-unfiltered
        type: token-classification
        args: PLODunfiltered
      metrics:
      - name: Precision
        type: precision
        value: 0.9655166719570215
      - name: Recall
        type: recall
        value: 0.9608483288141474
      - name: F1
        type: f1
        value: 0.9631768437660728
      - name: Accuracy
        type: accuracy
        value: 0.9589410429715819
  ---
  
  <!-- 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-finetuned-ner_with_callbacks
  
  This model is a fine-tuned version of [albert-large-v2](https://huggingface.co/albert-large-v2) on the [PLOD-unfiltered](https://huggingface.co/datasets/surrey-nlp/PLOD-unfiltered) dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1235
  - Precision: 0.9655
  - Recall: 0.9608
  - F1: 0.9632
  - Accuracy: 0.9589
  
  ## 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: 4
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 6
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.1377        | 0.49  | 7000  | 0.1294          | 0.9563    | 0.9422 | 0.9492 | 0.9436   |
  | 0.1244        | 0.98  | 14000 | 0.1165          | 0.9589    | 0.9504 | 0.9546 | 0.9499   |
  | 0.107         | 1.48  | 21000 | 0.1140          | 0.9603    | 0.9509 | 0.9556 | 0.9511   |
  | 0.1088        | 1.97  | 28000 | 0.1086          | 0.9613    | 0.9551 | 0.9582 | 0.9536   |
  | 0.0918        | 2.46  | 35000 | 0.1059          | 0.9617    | 0.9582 | 0.9600 | 0.9556   |
  | 0.0847        | 2.95  | 42000 | 0.1067          | 0.9620    | 0.9586 | 0.9603 | 0.9559   |
  | 0.0734        | 3.44  | 49000 | 0.1188          | 0.9646    | 0.9588 | 0.9617 | 0.9574   |
  | 0.0725        | 3.93  | 56000 | 0.1065          | 0.9660    | 0.9599 | 0.9630 | 0.9588   |
  | 0.0547        | 4.43  | 63000 | 0.1273          | 0.9662    | 0.9602 | 0.9632 | 0.9590   |
  | 0.0542        | 4.92  | 70000 | 0.1235          | 0.9655    | 0.9608 | 0.9632 | 0.9589   |
  | 0.0374        | 5.41  | 77000 | 0.1401          | 0.9647    | 0.9613 | 0.9630 | 0.9586   |
  | 0.0417        | 5.9   | 84000 | 0.1380          | 0.9641    | 0.9622 | 0.9632 | 0.9588   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.10.1+cu111
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  