roberta-base_stress_classification

This model is a fine-tuned version of roberta-base on the glassdoor dataset based on 100000 employees' reviews. It achieves the following results on the evaluation set:

• Loss: 0.1800
• Accuracy: 0.9647
• F1: 0.9647
• Precision: 0.9647
• Recall: 0.9647

Training data

Training data was classified as follow:

class	Description
0	Not Stressed
1	Stressed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.704	1.0	8000	0.6933	0.5	0.3333	0.25	0.5
0.6926	2.0	16000	0.6980	0.5	0.3333	0.25	0.5
0.0099	3.0	24000	0.1800	0.9647	0.9647	0.9647	0.9647
0.2727	4.0	32000	0.2243	0.9526	0.9526	0.9527	0.9526
0.0618	5.0	40000	0.2128	0.9536	0.9536	0.9546	0.9536

Model performance

-	precision	recall	f1	support
Not Stressed	0.96	0.97	0.97	10000
Stressed	0.97	0.96	0.97	10000
accuracy			0.97	20000
macro avg	0.97	0.97	0.97	20000
weighted avg	0.97	0.97	0.97	20000

How to use roberta-base_topic_classification_nyt_news with HuggingFace

from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("dstefa/roberta-base_topic_classification_nyt_news")
model = AutoModelForSequenceClassification.from_pretrained("dstefa/roberta-base_topic_classification_nyt_news")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer, device=0)

text = "They also caused so much stress because some leaders valued optics over output."
pipe(text)

[{'label': 'Stressed', 'score': 0.9959163069725037}]

### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2