File size: 2,511 Bytes
7be5890 |
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 85 86 87 88 89 90 91 92 93 94 |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- consumer-finance-complaints
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: distilbert-complaints-wandb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: consumer-finance-complaints
type: consumer-finance-complaints
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.868877906608376
- name: F1
type: f1
value: 0.8630522401242867
- name: Recall
type: recall
value: 0.868877906608376
- name: Precision
type: precision
value: 0.8616053523512515
---
<!-- 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. -->
# distilbert-complaints-wandb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the consumer-finance-complaints dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4448
- Accuracy: 0.8689
- F1: 0.8631
- Recall: 0.8689
- Precision: 0.8616
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.571 | 0.51 | 2000 | 0.5150 | 0.8469 | 0.8349 | 0.8469 | 0.8249 |
| 0.4765 | 1.01 | 4000 | 0.4676 | 0.8561 | 0.8451 | 0.8561 | 0.8376 |
| 0.3376 | 1.52 | 6000 | 0.4560 | 0.8609 | 0.8546 | 0.8609 | 0.8547 |
| 0.268 | 2.03 | 8000 | 0.4399 | 0.8684 | 0.8611 | 0.8684 | 0.8607 |
| 0.2654 | 2.53 | 10000 | 0.4448 | 0.8689 | 0.8631 | 0.8689 | 0.8616 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|