Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("MarcorpAI/bert-base-uncased-banking77")
model = AutoModelForSequenceClassification.from_pretrained("MarcorpAI/bert-base-uncased-banking77")Quick Links
bert-base-uncased-banking77
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
Model description
crafted a specialized model by fine-tuning BERT base uncased with the Banking77 dataset, enhancing its ability to understand and process banking-related information. This fine-tuned model is optimized for tasks within the financial domain, showcasing improved performance in tasks like sentiment analysis, intent detection, or document classification related to banking data.
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
Framework versions
- Transformers 4.35.2
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
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Model tree for MarcorpAI/bert-base-uncased-banking77
Base model
google-bert/bert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MarcorpAI/bert-base-uncased-banking77")