readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - favsbot
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: xlm-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: 0.5555555555555556
      - name: Recall
        type: recall
        value: 0.4722222222222222
      - name: F1
        type: f1
        value: 0.5105105105105106
      - name: Accuracy
        type: accuracy
        value: 0.6900452488687783
  ---
  
  <!-- 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-base-NER-favsbot
  
  This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the favsbot dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.0572
  - Precision: 0.5556
  - Recall: 0.4722
  - F1: 0.5105
  - Accuracy: 0.6900
  
  ## 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    | 2.4303          | 0.1448    | 0.3556 | 0.2058 | 0.1855   |
  | No log        | 2.0   | 8    | 2.3220          | 0.1465    | 0.3556 | 0.2075 | 0.1991   |
  | No log        | 3.0   | 12   | 2.1842          | 0.2486    | 0.2389 | 0.2436 | 0.4593   |
  | No log        | 4.0   | 16   | 1.9552          | 0.4       | 0.0111 | 0.0216 | 0.4367   |
  | No log        | 5.0   | 20   | 1.6989          | 0.0       | 0.0    | 0.0    | 0.4321   |
  | No log        | 6.0   | 24   | 1.6532          | 0.5       | 0.0056 | 0.0110 | 0.4344   |
  | No log        | 7.0   | 28   | 1.5724          | 0.3649    | 0.15   | 0.2126 | 0.5045   |
  | No log        | 8.0   | 32   | 1.5164          | 0.3654    | 0.2111 | 0.2676 | 0.5271   |
  | No log        | 9.0   | 36   | 1.4448          | 0.4203    | 0.1611 | 0.2329 | 0.5090   |
  | No log        | 10.0  | 40   | 1.3922          | 0.4833    | 0.1611 | 0.2417 | 0.5158   |
  | No log        | 11.0  | 44   | 1.3409          | 0.5395    | 0.2278 | 0.3203 | 0.5498   |
  | No log        | 12.0  | 48   | 1.2831          | 0.5824    | 0.2944 | 0.3911 | 0.5950   |
  | No log        | 13.0  | 52   | 1.2269          | 0.5714    | 0.3556 | 0.4384 | 0.6335   |
  | No log        | 14.0  | 56   | 1.1766          | 0.5625    | 0.4    | 0.4675 | 0.6606   |
  | No log        | 15.0  | 60   | 1.1408          | 0.5540    | 0.4278 | 0.4828 | 0.6674   |
  | No log        | 16.0  | 64   | 1.1159          | 0.56      | 0.4667 | 0.5091 | 0.6810   |
  | No log        | 17.0  | 68   | 1.0908          | 0.5658    | 0.4778 | 0.5181 | 0.6855   |
  | No log        | 18.0  | 72   | 1.0722          | 0.5658    | 0.4778 | 0.5181 | 0.6923   |
  | No log        | 19.0  | 76   | 1.0615          | 0.5592    | 0.4722 | 0.5120 | 0.6900   |
  | No log        | 20.0  | 80   | 1.0572          | 0.5556    | 0.4722 | 0.5105 | 0.6900   |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.1
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  