metadata
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
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}]