File size: 2,066 Bytes
1ecf998 eb88fb0 1ecf998 eb88fb0 1ecf998 eb88fb0 1ecf998 eb88fb0 1ecf998 eb88fb0 1ecf998 eb88fb0 |
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 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- financial_phrasebank
metrics:
- f1
- accuracy
model-index:
- name: phrasebank-sentiment-analysis
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: financial_phrasebank
type: financial_phrasebank
config: sentences_50agree
split: train
args: sentences_50agree
metrics:
- name: F1
type: f1
value: 0.8523902641427674
- name: Accuracy
type: accuracy
value: 0.8658872077028886
---
<!-- 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. -->
# phrasebank-sentiment-analysis
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on the financial_phrasebank dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7138
- F1: 0.8524
- Accuracy: 0.8659
## 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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| 0.1413 | 0.94 | 100 | 0.5298 | 0.8460 | 0.8652 |
| 0.057 | 1.89 | 200 | 0.7137 | 0.8354 | 0.8556 |
| 0.0399 | 2.83 | 300 | 0.7157 | 0.8375 | 0.8473 |
| 0.0279 | 3.77 | 400 | 0.7138 | 0.8524 | 0.8659 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|