
# Pretrained checkpoint: roberta-large-mnli
# 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: 4
- prompt_format: sentence aspect - sentiment

# Training results

|Epoch | Train loss| Subtask 3 f1 | Subtask 3 precision | Subtask 3 recall | Subtask4 accuracy |
|:----:|:---------:|:------------:|:-------------------:|:----------------:|:-----------------:|
|1|341.82094554277137|0.8827514330380406|0.9474272930648769|0.8263414634146341|0.8429268292682927|
|2|164.69039884814993|0.9055276381909548|0.933678756476684|0.8790243902439024|0.8839024390243903|
|3|79.89190268042148|0.9282533399307275|0.9417670682730924|0.9151219512195122|0.8692682926829268|
|4|34.275944823923055|0.9211045364891519|0.9312063808574277|0.911219512195122|0.8751219512195122|



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

Pretrained checkpoint: roberta-large-mnli
=========================================


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: 4
* prompt\_format: sentence aspect - sentiment


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




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