File size: 1,648 Bytes
4848198 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 |
---
license: apache-2.0
tags:
- Finance-sentiment-analysis
- generated_from_trainer
metrics:
- accuracy
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: 0.8400
- Accuracy: 0.8674
## 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4852 | 1.0 | 1022 | 0.4865 | 0.8326 |
| 0.3445 | 2.0 | 2044 | 0.5594 | 0.8478 |
| 0.2096 | 3.0 | 3066 | 0.6503 | 0.8571 |
| 0.097 | 4.0 | 4088 | 0.7111 | 0.8652 |
| 0.0539 | 5.0 | 5110 | 0.8400 | 0.8674 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|