---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- boolq
metrics:
- accuracy
model_index:
- name: distilbert-base-uncased-boolq
  results:
  - task:
      name: Question Answering
      type: question-answering
    dataset:
      name: boolq
      type: boolq
      args: default
    metric:
      name: Accuracy
      type: accuracy
      value: 0.7314984709480122
model-index:
- name: andi611/distilbert-base-uncased-qa-boolq
  results:
  - task:
      type: natural-language-inference
      name: Natural Language Inference
    dataset:
      name: boolq
      type: boolq
      config: default
      split: train
    metrics:
    - type: accuracy
      value: 0.875676249071815
      name: Accuracy
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDdjNjIwZDRlZDkzZDZmM2JmYzA0ZjIwMjBlZTI3OWQ5ZWNiNWU0OWI2ZWZmMGI2OGZmMDVhYzhjOTE1M2UzNSIsInZlcnNpb24iOjF9.A4-llThkLZ5SdVf6KTc7kWnJlpPna5b7hhzR7DdbFozIvqlFSeXqUhYf9lxn2svdvfiCJSsP3kHzcn46lYybAg
    - type: precision
      value: 0.8591506263366941
      name: Precision
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZmRhNDNhYjE3YTY4Mjk2ZThlMzQ5MGZiNGIxNmM4NDBlNzdlODkxYjRmNWM4YzAwZTlkOTFhZmJkMTQzZTYyZiIsInZlcnNpb24iOjF9.wl_bDHN2z0BXD5_IlLY8eQHFeCRkUGSj3NMOchIcbphiqoVoC_eWZNQqpZhM0XgCdoQrRKw4MNjCiwDq3euYCQ
    - type: recall
      value: 0.9574395641811372
      name: Recall
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGM0ZWJhYjI4YWIwNGQxZGQ3MTA4M2JiOTE5ZDc1ZDk5YjI5N2VjYjQzMTM3ZjM4YjVlNjNhNmU0MTVjZGJkNCIsInZlcnNpb24iOjF9.oC3_3F4164-tAIb0huR5xdzzRLpbxyJ52waXaWjbES8h0YRCrIjzmzgbhx4PPulxm8J59X1RF1wFsVXFFco3Bg
    - type: auc
      value: 0.9423158636459945
      name: AUC
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYmE5YjAxYjVmMjQwMzM0ZjBmNmM2NjFjZTcxMzIzNTk3NTdlNzVlOTM3YTMxMTdlNWMzNmE3YTk5MDQ1Y2VhYSIsInZlcnNpb24iOjF9.96hf0lrJ59bzlDm8lX9fv4WqNTP0mFVtpILWz-L3yBZyb4TIIKUh-JgDRwlLPu-JZlZS-gJSeAxPobrhJY0iCg
    - type: f1
      value: 0.9056360708534621
      name: F1
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzk0N2EzMWUyZGI4NmE3NjlkZDI3ZjkwMDIwZTdhNzAwZjBjNmYxYjYzYjJkMjFlOWRiNWUxMTFiZmM5ZmJhNyIsInZlcnNpb24iOjF9.W5wBUPEtxI2Movs6_UKrxA5sNNgV7m619TLWfwG5uSA0bgcE9xmH9EnNljsbSnFn2ObxTmrUK-W0OZ3SzL9hCg
    - type: loss
      value: 0.45028823614120483
      name: loss
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDdkOGYyMTJlYmRlYTRmNGI3YzkyOTRhOGMwZGY2N2MzYjVhZjgwN2U2YjdjMGQzMmYyZjFkMTFlM2Y0NmQ0ZCIsInZlcnNpb24iOjF9.PJWhSy48ZNYnp76dTuvhuvj-EFFWd8hzN5He1nIlHOqiPHglCtnSon161R7Ar4ILWy4LyPM8ByRslhzJfj-WDw
---

<!-- 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. -->

# distilbert-base-uncased-boolq

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the boolq dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2071
- Accuracy: 0.7315

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6506        | 1.0   | 531  | 0.6075          | 0.6681   |
| 0.575         | 2.0   | 1062 | 0.5816          | 0.6978   |
| 0.4397        | 3.0   | 1593 | 0.6137          | 0.7253   |
| 0.2524        | 4.0   | 2124 | 0.8124          | 0.7466   |
| 0.126         | 5.0   | 2655 | 1.1437          | 0.7370   |


### Framework versions

- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3