Text Classification
Transformers
TensorFlow
roberta
generated_from_keras_callback
text-embeddings-inference
Instructions to use vladjr/roberta-teste with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vladjr/roberta-teste with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vladjr/roberta-teste")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vladjr/roberta-teste") model = AutoModelForSequenceClassification.from_pretrained("vladjr/roberta-teste", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from vladjr/roberta-teste: direct link, hf CLI and curl.
- Browser
- Download file 1.88 kB
-
https://huggingface.co/vladjr/roberta-teste/resolve/main/README.md
- Command line
-
hf download hf://vladjr/roberta-teste/README.md
-
curl -L -o README.md https://huggingface.co/vladjr/roberta-teste/resolve/main/README.md
1.88 kB
metadata
license: mit
base_model: roberta-base
tags:
- generated_from_keras_callback
model-index:
- name: vladjr/roberta-teste
results: []
vladjr/roberta-teste
This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.1447
- Validation Loss: 0.1107
- Train Accuracy: 0.9690
- Epoch: 2
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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2100, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|---|---|---|---|
| 1.7291 | 0.3609 | 0.9482 | 0 |
| 0.3063 | 0.1604 | 0.9577 | 1 |
| 0.1447 | 0.1107 | 0.9690 | 2 |
Framework versions
- Transformers 4.34.0
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1