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

<!-- 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 an PolyAI/banking77 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3089
- F1: 0.9362

## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.261         | 1.0   | 313  | 1.0894          | 0.7969 |
| 0.5499        | 2.0   | 626  | 0.4196          | 0.9103 |
| 0.305         | 3.0   | 939  | 0.3403          | 0.9157 |
| 0.1277        | 4.0   | 1252 | 0.3020          | 0.9251 |
| 0.0857        | 5.0   | 1565 | 0.2911          | 0.9306 |
| 0.0347        | 6.0   | 1878 | 0.2865          | 0.9333 |
| 0.0251        | 7.0   | 2191 | 0.2994          | 0.9362 |
| 0.0111        | 8.0   | 2504 | 0.2970          | 0.9365 |
| 0.0075        | 9.0   | 2817 | 0.3102          | 0.9364 |
| 0.0058        | 10.0  | 3130 | 0.3089          | 0.9362 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1

## How to use

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

ckpt = 'sharmax-vikas/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}]
```