Text Classification
Transformers
PyTorch
TensorBoard
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use azamat/geocoder_coordinates_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use azamat/geocoder_coordinates_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="azamat/geocoder_coordinates_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("azamat/geocoder_coordinates_model") model = AutoModelForSequenceClassification.from_pretrained("azamat/geocoder_coordinates_model") - Notebooks
- Google Colab
- Kaggle
geocoder_model_xlm_roberta_50
This model is a fine-tuned version of azamat/geocoder_model_xlm_roberta_50 on the None dataset. It achieves the following results on the evaluation set:
- eval_loss: 29.6316
- eval_mse: 29.6316
- eval_mae: 2.0573
- eval_r2: 0.9858
- eval_runtime: 6.145
- eval_samples_per_second: 257.935
- eval_steps_per_second: 4.068
- epoch: 29.15
- step: 6500
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
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