bert-base-banking77-pt2

This model is a fine-tuned version of 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

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}]