Instructions to use Geo/bert-base-multilingual-cased-fine-tuned-intent-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Geo/bert-base-multilingual-cased-fine-tuned-intent-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Geo/bert-base-multilingual-cased-fine-tuned-intent-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Geo/bert-base-multilingual-cased-fine-tuned-intent-classification") model = AutoModelForSequenceClassification.from_pretrained("Geo/bert-base-multilingual-cased-fine-tuned-intent-classification") - Notebooks
- Google Colab
- Kaggle
bert-base-multilingual-cased-fine-tuned-intent-classification
This model is a fine-tuned version of bert-base-multilingual-cased on an unknown dataset.
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
Training results
Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
- Downloads last month
- 6
Model tree for Geo/bert-base-multilingual-cased-fine-tuned-intent-classification
Base model
google-bert/bert-base-multilingual-cased