---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bert-base-banking77-pt2
  results: []
datasets:
- PolyAI/banking77
language:
- en
base_model:
- google-bert/bert-base-uncased
---

<!-- 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

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: 0.3111
- F1: 0.9275

## 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.0958        | 1.0   | 626  | 0.7854          | 0.8363 |
| 0.3958        | 2.0   | 1252 | 0.3744          | 0.9168 |
| 0.1894        | 3.0   | 1878 | 0.3111          | 0.9275 |


### Framework versions

- Transformers 4.27.1
- Pytorch 2.4.1+cu121
- Datasets 2.9.0
- Tokenizers 0.13.3

## How to use

```py
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

ckpt = 'pistachio7/bert-base-banking77-pt2'
tokenizer = AutoTokenizer.from_pretrained(ckpt)
model = AutoModelForSequenceClassification.from_pretrained(ckpt)

classifier = pipeline('text-classification', tokenizer=tokenizer, model=model)
classifier('What is the base of the exchange rates?')
# Output: [{'label': 'exchange_rate', 'score': 0.9961327314376831}]
```