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---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finbert-finetuned-FG-SINGLE_SENTENCE-NEWS-WEIGHTED
results: []
---
<!-- 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. -->
# finbert-finetuned-FG-SINGLE_SENTENCE-NEWS-WEIGHTED
This model is a fine-tuned version of [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2741
- Accuracy: 0.7475
- F1: 0.7253
## 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: 6e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 249 | 0.9150 | 0.7346 | 0.6484 |
| No log | 2.0 | 498 | 0.8837 | 0.6210 | 0.6317 |
| 1.033 | 3.0 | 747 | 0.8460 | 0.6485 | 0.6666 |
| 1.033 | 4.0 | 996 | 1.0089 | 0.6831 | 0.6909 |
| 0.5642 | 5.0 | 1245 | 1.2507 | 0.7352 | 0.7152 |
| 0.5642 | 6.0 | 1494 | 1.3241 | 0.7129 | 0.7042 |
| 0.2078 | 7.0 | 1743 | 1.5163 | 0.7528 | 0.7230 |
| 0.2078 | 8.0 | 1992 | 1.5818 | 0.7352 | 0.7236 |
| 0.1108 | 9.0 | 2241 | 1.7930 | 0.7012 | 0.7046 |
| 0.1108 | 10.0 | 2490 | 1.8262 | 0.7305 | 0.7211 |
| 0.07 | 11.0 | 2739 | 2.0415 | 0.7440 | 0.7192 |
| 0.07 | 12.0 | 2988 | 2.1260 | 0.7563 | 0.7230 |
| 0.0392 | 13.0 | 3237 | 2.1502 | 0.7528 | 0.7323 |
| 0.0392 | 14.0 | 3486 | 2.2117 | 0.7516 | 0.7270 |
| 0.0174 | 15.0 | 3735 | 2.2657 | 0.7405 | 0.7236 |
| 0.0174 | 16.0 | 3984 | 2.2741 | 0.7475 | 0.7253 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.9.1
- Datasets 1.18.4
- Tokenizers 0.11.6
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