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ind_roberta
harshavsk/ind_roberta
ind_roberta is a text classification model from harshavsk. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- roberta
- Access
- Public
- Created
- Mar 30, 2024
- Updated
- Mar 31, 2024
- SHA
- cf2b613c
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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. -->
ind_roberta
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3951
- Accuracy@en: 0.9367
- F1@en: 0.9341
- Precision@en: 0.9360
- Recall@en: 0.9324
- Loss@en: 0.3951
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy@en | F1@en | Precision@en | Recall@en | Loss@en |
|---|---|---|---|---|---|---|---|---|
| 0.1854 | 1.0 | 375 | 0.4027 | 0.9033 | 0.8994 | 0.9012 | 0.8979 | 0.4027 |
| 0.203 | 2.0 | 750 | 0.4013 | 0.89 | 0.8877 | 0.8845 | 0.8944 | 0.4013 |
| 0.1282 | 3.0 | 1125 | 0.6106 | 0.89 | 0.8883 | 0.8858 | 0.8983 | 0.6106 |
| 0.0811 | 4.0 | 1500 | 0.3951 | 0.9367 | 0.9341 | 0.9360 | 0.9324 | 0.3951 |
| 0.0425 | 5.0 | 1875 | 0.4764 | 0.93 | 0.9282 | 0.9250 | 0.9333 | 0.4764 |
| 0.005 | 6.0 | 2250 | 0.5299 | 0.9367 | 0.9343 | 0.9349 | 0.9337 | 0.5299 |
| 0.0147 | 7.0 | 2625 | 0.5200 | 0.93 | 0.9285 | 0.9249 | 0.9359 | 0.5200 |
| 0.0182 | 8.0 | 3000 | 0.5532 | 0.9267 | 0.9242 | 0.9232 | 0.9253 | 0.5532 |
| 0.0125 | 9.0 | 3375 | 0.5398 | 0.9367 | 0.9346 | 0.9331 | 0.9363 | 0.5398 |
| 0.0171 | 10.0 | 3750 | 0.5157 | 0.9367 | 0.9349 | 0.9321 | 0.9389 | 0.5157 |
| 0.0109 | 11.0 | 4125 | 0.6538 | 0.92 | 0.9184 | 0.9149 | 0.9261 | 0.6538 |
| 0.0054 | 12.0 | 4500 | 0.5676 | 0.93 | 0.9281 | 0.9253 | 0.9320 | 0.5676 |
| 0.0047 | 13.0 | 4875 | 0.6763 | 0.9167 | 0.9146 | 0.9114 | 0.9195 | 0.6763 |
| 0.0076 | 14.0 | 5250 | 0.6970 | 0.9133 | 0.9109 | 0.9084 | 0.9141 | 0.6970 |
| 0.0066 | 15.0 | 5625 | 0.6947 | 0.9167 | 0.9146 | 0.9114 | 0.9195 | 0.6947 |
Framework versions
- Transformers 4.17.0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- Created
- Mar 30, 2024
- Updated
- Mar 31, 2024