README.md

---
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
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# 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