
# 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: 3
- prompt_format: sentence aspect - sentiment

# Training results

|Epoch | Train loss| Subtask 3 f1 | Subtask 3 precision | Subtask 3 recall | Subtask4 accuracy |
|:----:|:---------:|:------------:|:-------------------:|:----------------:|:-----------------:|
|1|331.22805924050044|0.8987603305785123|0.9549945115257958|0.848780487804878|0.848780487804878|
|2|143.6195309770701|0.9197224975222993|0.9345417925478349|0.9053658536585366|0.8887804878048781|
|3|58.553219705150696|0.923837784371909|0.9368104312938816|0.911219512195122|0.8760975609756098|



{}
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: 3
* prompt\_format: sentence aspect - sentiment


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




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