metadata

license: mit
base_model: roberta-base
tags:
  - stress
  - classification
  - glassdoor
metrics:
  - accuracy
  - f1
  - precision
  - recall
widget:
  - text: >-
      They also caused so much stress because some leaders valued optics over
      output.
    example_title: Stressed 1 Example
  - text: Way too much work pressure.
    example_title: Stressed 2 Example
  - text: Understaffed, lots of deck revisions, unpredictable, terrible technology.
    example_title: Stressed 3 Example
  - text: Nice environment good work life balance.
    example_title: Not Stressed 1 Example
model-index:
  - name: roberta-base_topic_classification_nyt_news
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: New_York_Times_Topics
          type: News
        metrics:
          - type: F1
            name: F1
            value: 0.97
          - type: accuracy
            name: accuracy
            value: 0.97
          - type: precision
            name: precision
            value: 0.97
          - type: recall
            name: recall
            value: 0.97
pipeline_tag: text-classification

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