Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use Extrabass/bert-base-banking77-pt2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Extrabass/bert-base-banking77-pt2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Extrabass/bert-base-banking77-pt2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Extrabass/bert-base-banking77-pt2") model = AutoModelForSequenceClassification.from_pretrained("Extrabass/bert-base-banking77-pt2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Extrabass/bert-base-banking77-pt2: direct link, hf CLI and curl.
- Browser
- Download file 1.47 kB
-
https://huggingface.co/Extrabass/bert-base-banking77-pt2/resolve/main/README.md
- Command line
-
hf download hf://Extrabass/bert-base-banking77-pt2/README.md
-
curl -L -o README.md https://huggingface.co/Extrabass/bert-base-banking77-pt2/resolve/main/README.md
1.47 kB
metadata
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bert-base-banking77-pt2
results: []
bert-base-banking77-pt2
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3129
- F1: 0.9265
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 |
|---|---|---|---|---|
| 1.1599 | 1.0 | 626 | 0.8200 | 0.8339 |
| 0.3802 | 2.0 | 1252 | 0.3896 | 0.9136 |
| 0.1893 | 3.0 | 1878 | 0.3129 | 0.9265 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.1