metadata

language:
  - en
license: cc-by-4.0
tags:
  - generated_from_trainer
datasets:
  - squad_v2
  - conll2003
model_index:
  - name: >-
      bert-large-uncased-whole-word-masking-squad2-with-ner-conll2003-with-neg-with-repeat
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: squad_v2
          type: squad_v2
          args: conll2003
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: conll2003
          type: conll2003
model-index:
  - name: >-
      andi611/bert-large-uncased-whole-word-masking-squad2-with-ner-conll2003-with-neg-with-repeat
    results:
      - task:
          type: question-answering
          name: Question Answering
        dataset:
          name: adversarial_qa
          type: adversarial_qa
          config: adversarialQA
          split: validation
        metrics:
          - type: f1
            value: 18.5493
            name: F1
            verified: true
            verifyToken: >-
              eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDY3NjQ2ZDViNGU5OTA4YWRmNTA0NTNhOWJmNDQ3NzgwY2ZiYmYyYzAxYTE3YTgxNGZhMjBjY2YwODMwZGVhOSIsInZlcnNpb24iOjF9.mmxIAZ6p-fxKtLI49f9CvjB_LjNrgDJ-TU3esLiV5dv9y6HkRgZ5PAsF0VO6gbdfEJWxKWZFX0aBlNo9Vz_tBg
          - type: exact_match
            value: 13.3333
            name: Exact Match
            verified: true
            verifyToken: >-
              eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOWZlZWU3MDMzY2NkYTdkNTY2NTg5OTZiMzczNDVjYThjZmQ4NTlmZjQwM2VhMjVhOTZjZjliMTI4ZWFkMjA3ZSIsInZlcnNpb24iOjF9.HN7DglPZtzdAJ_vwyGleQLKKJJautl8b3jroS3FqUmz3dLQWNS9omjAFuu5i1G7pxom2DhKTXwZKFhIxwzReDA
          - type: loss
            value: 7.114065647125244
            name: loss
            verified: true
            verifyToken: >-
              eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNWZhODY3NTdhNDFiNTYyZGJhZjc0OWJjZGNkNDlkNzc2MTAxMzI2Yzg0NjkxYTRmOTY5YWZhNTI0NzE4MzI2ZiIsInZlcnNpb24iOjF9.wzZOasFqp8mrzS26Ubz8CQq2fnpyXJF0V0c1I6gjaiWcMoMwePNSapXK41cD4tB5orRqKeQFodvp2hW7xjtSCQ

bert-large-uncased-whole-word-masking-squad2-with-ner-conll2003-with-neg-with-repeat

This model is a fine-tuned version of deepset/bert-large-uncased-whole-word-masking-squad2 on the squad_v2 and the conll2003 datasets.

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: 4
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Framework versions

Transformers 4.8.2
Pytorch 1.8.1+cu111
Datasets 1.8.0
Tokenizers 0.10.3