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End of training
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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- banking77
metrics:
- f1
model-index:
- name: bert-base-banking77-pt2_model_BertForSequenceClassification
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: banking77
type: banking77
config: default
split: test
args: default
metrics:
- name: F1
type: f1
value: 0.9327100079940828
---
<!-- 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-banking77-pt2_model_BertForSequenceClassification
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the banking77 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2937
- F1: 0.9327
## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1254 | 1.0 | 626 | 0.8005 | 0.8480 |
| 0.3798 | 2.0 | 1252 | 0.3632 | 0.9218 |
| 0.1754 | 3.0 | 1878 | 0.2937 | 0.9327 |
### Framework versions
- Transformers 4.27.1
- Pytorch 2.2.0.dev20231010+cu121
- Datasets 2.9.0
- Tokenizers 0.13.3