
# Pretrained checkpoint: roberta-large
# 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|302.38164756447077|0.8747412008281573|0.9316427783902976|0.824390243902439|0.5219512195121951|
|2|152.67940049804747|0.8930041152263374|0.9445048966267682|0.8468292682926829|0.8614634146341463|
|3|99.03914468642324|0.9071318624935865|0.9567099567099567|0.8624390243902439|0.8721951219512195|
|4|60.156904806615785|0.905241935483871|0.9363920750782064|0.8760975609756098|0.8790243902439024|
|5|36.06248981086537|0.9195855944745931|0.9301397205588823|0.9092682926829269|0.8926829268292683|



{}
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
====================================


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 #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us 
