
# Pretrained checkpoint: bert-base-uncased
# Traning hyperparameters:
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 24
- eval_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- prompt_format: sentence aspect - sentiment

# Training results

|Epoch | Train loss| Subtask 3 f1 | Subtask 3 precision | Subtask 3 recall | Subtask4 accuracy |
|:----:|:---------:|:------------:|:-------------------:|:----------------:|:-----------------:|
|1|305.5731324516237|0.8653648509763618|0.9142236699239956|0.8214634146341463|0.7921951219512195|
|2|160.19575848057866|0.8591029023746701|0.9356321839080459|0.7941463414634147|0.8009756097560976|
|3|101.52328581456095|0.8882175226586102|0.9177939646201873|0.8604878048780488|0.8321951219512195|
|4|63.44610589882359|0.8818737270875764|0.9222577209797657|0.8448780487804878|0.8282926829268292|
|5|43.48708916385658|0.8917835671342685|0.9165808444902163|0.8682926829268293|0.8214634146341463|



{}
transformers pytorch bert text-classification autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us 

Pretrained checkpoint: bert-base-uncased
========================================


Traning hyperparameters:
========================


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 24
* eval\_batch\_size: 24
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
* prompt\_format: sentence aspect - sentiment


Training results
================




TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us 
