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---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: qqp
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QQP
type: glue
args: qqp
metrics:
- name: Accuracy
type: accuracy
value: 0.8988869651249073
- name: F1
type: f1
value: 0.8670050100852366
- task:
type: natural-language-inference
name: Natural Language Inference
dataset:
name: glue
type: glue
config: qqp
split: validation
metrics:
- name: Accuracy
type: accuracy
value: 0.8989859015582489
verified: true
- name: Precision
type: precision
value: 0.8407470502870844
verified: true
- name: Recall
type: recall
value: 0.8951965065502183
verified: true
- name: AUC
type: auc
value: 0.9590670523994457
verified: true
- name: F1
type: f1
value: 0.8671178499381792
verified: true
- name: loss
type: loss
value: 0.2457672506570816
verified: true
---
<!-- 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. -->
# qqp
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2458
- Accuracy: 0.8989
- F1: 0.8670
- Combined Score: 0.8829
## 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: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.5
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
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