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--- |
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license: mit |
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base_model: roberta-base |
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tags: |
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- stress |
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- classification |
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- glassdoor |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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widget: |
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- text: >- |
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They also caused so much stress because some leaders valued optics over output. |
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example_title: Stressed 1 Example |
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- text: >- |
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Way too much work pressure. |
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example_title: Stressed 2 Example |
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- text: >- |
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Understaffed, lots of deck revisions, unpredictable, terrible technology. |
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example_title: Stressed 3 Example |
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- text: >- |
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Nice environment good work life balance. |
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example_title: Not Stressed 1 Example |
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model-index: |
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- name: roberta-base_topic_classification_nyt_news |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: New_York_Times_Topics |
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type: News |
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metrics: |
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- type: F1 |
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name: F1 |
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value: 0.97 |
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- type: accuracy |
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name: accuracy |
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value: 0.97 |
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- type: precision |
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name: precision |
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value: 0.97 |
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- type: recall |
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name: recall |
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value: 0.97 |
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pipeline_tag: text-classification |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# roberta-base_stress_classification |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the glassdoor dataset based on 100000 employees' reviews. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1800 |
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- Accuracy: 0.9647 |
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- F1: 0.9647 |
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- Precision: 0.9647 |
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- Recall: 0.9647 |
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## Training data |
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Training data was classified as follow: |
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class |Description |
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-|- |
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0 |Not Stressed |
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1 |Stressed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.704 | 1.0 | 8000 | 0.6933 | 0.5 | 0.3333 | 0.25 | 0.5 | |
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| 0.6926 | 2.0 | 16000 | 0.6980 | 0.5 | 0.3333 | 0.25 | 0.5 | |
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| 0.0099 | 3.0 | 24000 | 0.1800 | 0.9647 | 0.9647 | 0.9647 | 0.9647 | |
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| 0.2727 | 4.0 | 32000 | 0.2243 | 0.9526 | 0.9526 | 0.9527 | 0.9526 | |
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| 0.0618 | 5.0 | 40000 | 0.2128 | 0.9536 | 0.9536 | 0.9546 | 0.9536 | |
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### Model performance |
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-|precision|recall|f1|support |
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-|-|-|-|- |
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Not Stressed|0.96|0.97|0.97|10000 |
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Stressed|0.97|0.96|0.97|10000 |
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accuracy|||0.97|20000 |
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macro avg|0.97|0.97|0.97|20000 |
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weighted avg|0.97|0.97|0.97|20000 |
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### How to use roberta-base_topic_classification_nyt_news with HuggingFace |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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from transformers import pipeline |
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tokenizer = AutoTokenizer.from_pretrained("dstefa/roberta-base_topic_classification_nyt_news") |
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model = AutoModelForSequenceClassification.from_pretrained("dstefa/roberta-base_topic_classification_nyt_news") |
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pipe = pipeline("text-classification", model=model, tokenizer=tokenizer, device=0) |
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text = "They also caused so much stress because some leaders valued optics over output." |
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pipe(text) |
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[{'label': 'Stressed', 'score': 0.9959163069725037}] |
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### Framework versions |
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- Transformers 4.32.1 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.2 |
