metadata
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: validation
metrics:
- name: Accuracy
type: accuracy
value: 0.7314984709480122
verified: true
- name: Precision
type: precision
value: 0.766743648960739
verified: true
- name: Recall
type: recall
value: 0.8165272995573045
verified: true
- name: AUC
type: auc
value: 0.7719917242618858
verified: true
- name: F1
type: f1
value: 0.7908527870414483
verified: true
- name: loss
type: loss
value: 1.2070027589797974
verified: true
distilbert-base-uncased-boolq
This model is a fine-tuned version of 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