readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - favsbot
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: roberta-base-NER-favsbot
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: favsbot
        type: favsbot
        config: default
        split: train
        args: default
      metrics:
      - name: Precision
        type: precision
        value: 0.6939890710382514
      - name: Recall
        type: recall
        value: 0.7055555555555556
      - name: F1
        type: f1
        value: 0.6997245179063362
      - name: Accuracy
        type: accuracy
        value: 0.8454332552693209
  ---
  
  <!-- 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. -->
  
  # roberta-base-NER-favsbot
  
  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the favsbot dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4812
  - Precision: 0.6940
  - Recall: 0.7056
  - F1: 0.6997
  - Accuracy: 0.8454
  
  ## 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: 1.5e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 20
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 4    | 2.1885          | 0.1787    | 0.3722 | 0.2414 | 0.2998   |
  | No log        | 2.0   | 8    | 1.9109          | 0.3768    | 0.1444 | 0.2088 | 0.4707   |
  | No log        | 3.0   | 12   | 1.6425          | 0.0       | 0.0    | 0.0    | 0.4075   |
  | No log        | 4.0   | 16   | 1.5402          | 0.75      | 0.0167 | 0.0326 | 0.4145   |
  | No log        | 5.0   | 20   | 1.3569          | 0.5263    | 0.3333 | 0.4082 | 0.5785   |
  | No log        | 6.0   | 24   | 1.2250          | 0.4691    | 0.5056 | 0.4866 | 0.6721   |
  | No log        | 7.0   | 28   | 1.0957          | 0.4973    | 0.5167 | 0.5068 | 0.6885   |
  | No log        | 8.0   | 32   | 0.9751          | 0.5581    | 0.5333 | 0.5455 | 0.7190   |
  | No log        | 9.0   | 36   | 0.8824          | 0.6456    | 0.5667 | 0.6036 | 0.7518   |
  | No log        | 10.0  | 40   | 0.8035          | 0.6442    | 0.5833 | 0.6122 | 0.7658   |
  | No log        | 11.0  | 44   | 0.7336          | 0.6412    | 0.6056 | 0.6229 | 0.7822   |
  | No log        | 12.0  | 48   | 0.6722          | 0.6384    | 0.6278 | 0.6331 | 0.7963   |
  | No log        | 13.0  | 52   | 0.6205          | 0.6610    | 0.65   | 0.6555 | 0.8080   |
  | No log        | 14.0  | 56   | 0.5796          | 0.6740    | 0.6778 | 0.6759 | 0.8244   |
  | No log        | 15.0  | 60   | 0.5503          | 0.6796    | 0.6833 | 0.6814 | 0.8244   |
  | No log        | 16.0  | 64   | 0.5256          | 0.6851    | 0.6889 | 0.6870 | 0.8337   |
  | No log        | 17.0  | 68   | 0.5066          | 0.6868    | 0.6944 | 0.6906 | 0.8407   |
  | No log        | 18.0  | 72   | 0.4929          | 0.6868    | 0.6944 | 0.6906 | 0.8431   |
  | No log        | 19.0  | 76   | 0.4845          | 0.6868    | 0.6944 | 0.6906 | 0.8431   |
  | No log        | 20.0  | 80   | 0.4812          | 0.6940    | 0.7056 | 0.6997 | 0.8454   |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.1
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  