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EVALutionRelationTrain-5
RKessler/EVALutionRelationTrain-5
EVALutionRelationTrain-5 is a text classification model from RKessler. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- roberta
- Access
- Public
- Created
- Jan 10, 2024
- Updated
- Jan 10, 2024
- SHA
- 189df223
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Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
EVALutionRelationTrain-5
This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6933
- Accuracy: 0.5
- Precision: 0.5
- Recall: 1.0
- F1: 0.6667
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
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
- num_epochs: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| No log | 0.35 | 100 | 0.7119 | 0.5 | 0.0 | 0.0 | 0.0 |
| No log | 0.71 | 200 | 0.7123 | 0.5 | 0.5 | 1.0 | 0.6667 |
| No log | 1.06 | 300 | 0.6936 | 0.5 | 0.5 | 1.0 | 0.6667 |
| No log | 1.42 | 400 | 0.6933 | 0.5 | 0.5 | 1.0 | 0.6667 |
| 0.6993 | 1.77 | 500 | 0.6945 | 0.5 | 0.0 | 0.0 | 0.0 |
| 0.6993 | 2.13 | 600 | 0.6948 | 0.5 | 0.0 | 0.0 | 0.0 |
| 0.6993 | 2.48 | 700 | 0.6999 | 0.5 | 0.5 | 1.0 | 0.6667 |
| 0.6993 | 2.84 | 800 | 0.6943 | 0.5 | 0.0 | 0.0 | 0.0 |
| 0.6993 | 3.19 | 900 | 0.6951 | 0.5 | 0.5 | 1.0 | 0.6667 |
| 0.698 | 3.55 | 1000 | 0.6945 | 0.5 | 0.0 | 0.0 | 0.0 |
| 0.698 | 3.9 | 1100 | 0.6956 | 0.5 | 0.0 | 0.0 | 0.0 |
| 0.698 | 4.26 | 1200 | 0.6933 | 0.5 | 0.5 | 1.0 | 0.6667 |
| 0.698 | 4.61 | 1300 | 0.6941 | 0.5 | 0.5 | 1.0 | 0.6667 |
| 0.698 | 4.96 | 1400 | 0.6934 | 0.5 | 0.0 | 0.0 | 0.0 |
| 0.6964 | 5.32 | 1500 | 0.6933 | 0.5 | 0.0 | 0.0 | 0.0 |
| 0.6964 | 5.67 | 1600 | 0.6943 | 0.5 | 0.0 | 0.0 | 0.0 |
| 0.6964 | 6.03 | 1700 | 0.6946 | 0.5 | 0.0 | 0.0 | 0.0 |
| 0.6964 | 6.38 | 1800 | 0.6932 | 0.5 | 0.0 | 0.0 | 0.0 |
| 0.6964 | 6.74 | 1900 | 0.6952 | 0.5 | 0.0 | 0.0 | 0.0 |
| 0.6952 | 7.09 | 2000 | 0.6934 | 0.5 | 0.5 | 1.0 | 0.6667 |
| 0.6952 | 7.45 | 2100 | 0.6935 | 0.5 | 0.5 | 1.0 | 0.6667 |
| 0.6952 | 7.8 | 2200 | 0.6933 | 0.5 | 0.5 | 1.0 | 0.6667 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Created
- Jan 10, 2024
- Updated
- Jan 10, 2024