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RoBERTa_FPB_finetuned_v2
Elanthamiljeeva/RoBERTa_FPB_finetuned_v2
RoBERTa_FPB_finetuned_v2 is a machine learning model from Elanthamiljeeva. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- License
- apache-2.0
- Model type
- roberta
- Access
- Public
- Created
- Aug 5, 2024
- Updated
- Aug 5, 2024
- SHA
- 4e0e6f20
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. -->
RoBERTa_FPB_finetuned_v2
This model is a fine-tuned version of soleimanian/financial-roberta-large-sentiment on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5080
- Accuracy: 0.8660
- F1: 0.8658
- Precision: 0.8663
- Recall: 0.8660
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: 16
- eval_batch_size: 64
- 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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.3263 | 1.0 | 218 | 0.3427 | 0.8711 | 0.8725 | 0.8797 | 0.8711 |
| 0.377 | 2.0 | 436 | 0.4305 | 0.8454 | 0.8458 | 0.8470 | 0.8454 |
| 0.2243 | 3.0 | 654 | 0.5149 | 0.8763 | 0.8756 | 0.8769 | 0.8763 |
| 0.1564 | 4.0 | 872 | 0.5080 | 0.8660 | 0.8658 | 0.8663 | 0.8660 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Tokenizers 0.19.1
- Type
- roberta
- License
- apache-2.0
- Created
- Aug 5, 2024
- Updated
- Aug 5, 2024