readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - generator
  model-index:
  - name: scideberta-cs-tdm-pretrained-finetuned-ner
    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. -->
  
  # scideberta-cs-tdm-pretrained-finetuned-ner
  
  This model is a fine-tuned version of [sohamtiwari3120/scideberta-cs-tdm-pretrained](https://huggingface.co/sohamtiwari3120/scideberta-cs-tdm-pretrained) on the generator dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6836
  - Overall Precision: 0.5912
  - Overall Recall: 0.6850
  - Overall F1: 0.6347
  - Overall Accuracy: 0.9609
  - Datasetname F1: 0.5882
  - Hyperparametername F1: 0.6897
  - Hyperparametervalue F1: 0.7619
  - Methodname F1: 0.6525
  - Metricname F1: 0.7500
  - Metricvalue F1: 0.6452
  - Taskname F1: 0.5370
  
  ## 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: 100
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Datasetname F1 | Hyperparametername F1 | Hyperparametervalue F1 | Methodname F1 | Metricname F1 | Metricvalue F1 | Taskname F1 |
  |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:---------------------:|:----------------------:|:-------------:|:-------------:|:--------------:|:-----------:|
  | No log        | 1.0   | 132  | 0.3507          | 0.3972            | 0.6870         | 0.5034     | 0.9410           | 0.4370         | 0.5441                | 0.5814                 | 0.6124        | 0.5604        | 0.6207         | 0.3724      |
  | No log        | 2.0   | 264  | 0.3079          | 0.4066            | 0.7520         | 0.5278     | 0.9430           | 0.4138         | 0.5380                | 0.6222                 | 0.5895        | 0.625         | 0.7273         | 0.4340      |
  | No log        | 3.0   | 396  | 0.3740          | 0.5007            | 0.7195         | 0.5905     | 0.9535           | 0.4882         | 0.6777                | 0.7500                 | 0.6254        | 0.6747        | 0.7097         | 0.4962      |
  | 0.4014        | 4.0   | 528  | 0.4072          | 0.5161            | 0.7154         | 0.5997     | 0.9540           | 0.5167         | 0.6612                | 0.6374                 | 0.6337        | 0.6753        | 0.6061         | 0.5341      |
  | 0.4014        | 5.0   | 660  | 0.4088          | 0.5590            | 0.7317         | 0.6338     | 0.9582           | 0.5660         | 0.6667                | 0.7397                 | 0.6250        | 0.7226        | 0.75           | 0.5794      |
  | 0.4014        | 6.0   | 792  | 0.4810          | 0.5201            | 0.7093         | 0.6002     | 0.9550           | 0.4874         | 0.5970                | 0.6506                 | 0.6207        | 0.6708        | 0.6250         | 0.5756      |
  | 0.4014        | 7.0   | 924  | 0.5288          | 0.5403            | 0.6809         | 0.6025     | 0.9576           | 0.4915         | 0.6500                | 0.6133                 | 0.6255        | 0.7006        | 0.7879         | 0.5389      |
  | 0.0912        | 8.0   | 1056 | 0.5281          | 0.5468            | 0.6890         | 0.6097     | 0.9574           | 0.5370         | 0.7143                | 0.6866                 | 0.5854        | 0.6939        | 0.7742         | 0.5491      |
  | 0.0912        | 9.0   | 1188 | 0.4744          | 0.5371            | 0.7358         | 0.6209     | 0.9560           | 0.5370         | 0.6341                | 0.6753                 | 0.6554        | 0.6795        | 0.7059         | 0.5699      |
  | 0.0912        | 10.0  | 1320 | 0.5498          | 0.5686            | 0.7073         | 0.6304     | 0.9586           | 0.5370         | 0.6349                | 0.7500                 | 0.6553        | 0.7152        | 0.7742         | 0.5573      |
  | 0.0912        | 11.0  | 1452 | 0.6424          | 0.5857            | 0.7012         | 0.6383     | 0.9597           | 0.56           | 0.6789                | 0.7246                 | 0.6667        | 0.6974        | 0.6875         | 0.5757      |
  | 0.0354        | 12.0  | 1584 | 0.5867          | 0.5641            | 0.6890         | 0.6203     | 0.9585           | 0.5185         | 0.6496                | 0.7213                 | 0.6619        | 0.7152        | 0.7333         | 0.5402      |
  | 0.0354        | 13.0  | 1716 | 0.5500          | 0.5667            | 0.6992         | 0.6260     | 0.9592           | 0.55