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---
license: apache-2.0
tags:
- Finance-sentiment-analysis
- generated_from_trainer
metrics:
- f1
- accuracy
- precision
- recall
model-index:
- name: bert-base-finance-sentiment-noisy-search
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. -->
# bert-base-finance-sentiment-noisy-search
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7819
- F1: 0.8708
- Accuracy: 0.8707
- Precision: 0.8713
- Recall: 0.8707
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:---------:|:------:|
| 0.476 | 1.0 | 1022 | 0.4611 | 0.8268 | 0.8283 | 0.8289 | 0.8283 |
| 0.3193 | 2.0 | 2044 | 0.4427 | 0.8565 | 0.8565 | 0.8565 | 0.8565 |
| 0.1926 | 3.0 | 3066 | 0.5921 | 0.8583 | 0.8587 | 0.8585 | 0.8587 |
| 0.1104 | 4.0 | 4088 | 0.6870 | 0.8677 | 0.8674 | 0.8695 | 0.8674 |
| 0.0544 | 5.0 | 5110 | 0.7819 | 0.8708 | 0.8707 | 0.8713 | 0.8707 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
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